Skip to content

S
SpeakEzy_Backend
Commit bb4b8ed2 authored by Shehan Liyanage's avatar Shehan Liyanage
Browse files
Options

Merge branch 'AddTrainingModels' into 'master' 

add jupyter notebooks

See merge request !2
parents e82fe00e e386e7c8
Hide whitespace changes
Inline Side-by-side
Showing with 2445 additions and 0 deletions
sign_language_backend/SignLanguageDetection.ipynb 0 → 100644
View file @ bb4b8ed2
{
"cells": [
{
"cell_type": "markdown",
"id": "174b5cb4",
"metadata": {},
"source": [
"# Installing and importing dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d35d0afb",
"metadata": {},
"outputs": [],
"source": [
"# install these\n",
"!pip install tensorflow==2.13.0 \n",
"!pip install mediapipe==0.10.3\n",
"!pip install scikit-learn==1.3.1\n",
"!pip install opencv-python==4.9.0.80\n",
"!pip install matplotlib==3.8.3"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0cdb80ee",
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"import os\n",
"from matplotlib import pyplot as plt\n",
"import time\n",
"import mediapipe as mp"
]
},
{
"cell_type": "markdown",
"id": "c823bc27",
"metadata": {},
"source": [
"# Collecting keypoints from mediapipe holistics"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81a10b4a",
"metadata": {},
"outputs": [],
"source": [
"mp_holistic = mp.solutions.holistic #we are bringing the holistic model of mideapipe\n",
"mp_drawing = mp.solutions.drawing_utils #we are also bringing the drawing utilities of mideapipe."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ccb80832",
"metadata": {},
"outputs": [],
"source": [
"def mediapipe_detection(image, model):\n",
" image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB\n",
" image.flags.writeable = False # Image is no longer writeable\n",
" results = model.process(image) # Make prediction\n",
" image.flags.writeable = True # Image is now writeable \n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR\n",
" return image, results"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe20a0f8",
"metadata": {},
"outputs": [],
"source": [
"def draw_landmarks(image, results):\n",
" # Draw face connections\n",
" mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, \n",
" mp_drawing.DrawingSpec(color=(70,110,10), thickness=1, circle_radius=1), \n",
" mp_drawing.DrawingSpec(color=(70,256,121), thickness=1, circle_radius=1)\n",
" ) \n",
" # Draw pose connections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(70,22,10), thickness=2, circle_radius=4), \n",
" mp_drawing.DrawingSpec(color=(70,44,121), thickness=2, circle_radius=2)\n",
" ) \n",
" # Draw left hand connections\n",
" mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
" mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4), \n",
" mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
" ) \n",
" # Draw right hand connections \n",
" mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n",
" ) "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a075f076",
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"# Set mediapipe model \n",
"with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
" while cap.isOpened():\n",
" #here we are reading the feed\n",
" ret,frame = cap.read()\n",
" \n",
" #make detections\n",
" image,results = mediapipe_detection(frame,holistic)\n",
" print(results)\n",
" \n",
" #drawing landmarks on real time\n",
" draw_landmarks(image,results)\n",
" \n",
" #now we are showing the feed to the screen\n",
" cv2.imshow(\"OpenCV Feed\",image)\n",
"\n",
" #breaking the loop if we press q.\n",
" if cv2.waitKey(10) & 0xFF==ord('q'):\n",
" break\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f81ba75",
"metadata": {},
"outputs": [],
"source": [
"results.left_hand_landmarks"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4bf5df6",
"metadata": {},
"outputs": [],
"source": [
"draw_landmarks(frame,results)\n",
"plt.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))"
]
},
{
"cell_type": "markdown",
"id": "256834f5",
"metadata": {},
"source": [
"# Extracting keypoints value"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1b8c1ce",
"metadata": {},
"outputs": [],
"source": [
"# results.pose_landmarks\n",
"# len(results.face_landmarks.landmark)\n",
"len(results.right_hand_landmarks.landmark)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18576048",
"metadata": {},
"outputs": [],
"source": [
"pose = np.array([[res.x,res.y,res.z,res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(132)\n",
"face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(1404)\n",
"lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
"rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cdba5b51",
"metadata": {},
"outputs": [],
"source": [
"def extract_keypoints(results):\n",
" pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)\n",
" face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)\n",
" lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
" rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)\n",
" return np.concatenate([pose, face, lh, rh])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d54f49eb",
"metadata": {},
"outputs": [],
"source": [
"extract_keypoints(results).shape\n",
"# 468*3+33*4+21*3+21*3"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "065aef3f",
"metadata": {},
"outputs": [],
"source": [
"result_test = extract_keypoints(results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af6df385",
"metadata": {},
"outputs": [],
"source": [
"result_test"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "848358f7",
"metadata": {},
"outputs": [],
"source": [
"np.save('0',result_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8742aff",
"metadata": {},
"outputs": [],
"source": [
"np.load('0.npy')#load backup our array"
]
},
{
"cell_type": "markdown",
"id": "28582d44",
"metadata": {},
"source": [
"# Folder setup for data Collection"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "099ad60b",
"metadata": {},
"outputs": [],
"source": [
"# path for exported data,numpy arrays\n",
"PATH = os.path.join(\"MP_Data\")\n",
"\n",
"# Actions that we are trying to detect\n",
"#\n",
"actions = [\"haloo\", \"dannwa\",\"mama\", \"obata\" ,\"puluwan\", \"suba\" ,\"udaasanak\"]\n",
"# ,,, --\n",
"\n",
"# total number of sequences that we are trying to collect which is 30 videos worth of data\n",
"no_sequences = 50\n",
"\n",
"# 30 frames length of video\n",
"sequence_length = 50\n",
"\n",
"# folder start\n",
"start_folder = 50"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bba40fb1",
"metadata": {},
"outputs": [],
"source": [
"for action in actions:\n",
" for sequence in range(no_sequences):\n",
" try:\n",
" os.makedirs(os.path.join(PATH,action,str(sequence)))\n",
" except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "31f37a2b",
"metadata": {},
"outputs": [],
"source": [
"os.listdir()"
]
},
{
"cell_type": "markdown",
"id": "5a396320",
"metadata": {},
"source": [
"# COLLECTING KEYPOINTS FOR TRAINING AND TESTING"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1714ef8b",
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"# Set mediapipe model \n",
"with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
" \n",
" # NEW LOOP\n",
" # Loop through actions\n",
" for action in actions:\n",
" # Loop through sequences aka videos\n",
" for sequence in range(no_sequences):\n",
" # Loop through video length aka sequence length\n",
" for frame_num in range(sequence_length):\n",
"\n",
" #here we are reading the feed\n",
" ret,frame = cap.read()\n",
"\n",
" #make detections\n",
" image,results = mediapipe_detection(frame,holistic)\n",
"\n",
" #drawing landmarks on real time\n",
" draw_landmarks(image,results)\n",
" \n",
" # NEW Apply wait logic\n",
" if frame_num == 0: \n",
" cv2.putText(image, 'STARTING COLLECTION', (120,200), \n",
" cv2.FONT_HERSHEY_SIMPLEX , 1, (255, 0,0), 4, cv2.LINE_AA)\n",
" cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), \n",
" cv2.FONT_HERSHEY_SIMPLEX , 0.5, (0, 255,0), 1, cv2.LINE_AA)\n",
" # Show to screen\n",
" cv2.imshow('OpenCV Feed', image)\n",
" cv2.waitKey(3000)\n",
" else: \n",
" cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15,12), \n",
" cv2.FONT_HERSHEY_SIMPLEX , 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n",
" # Show to screen\n",
" cv2.imshow('OpenCV Feed', image)\n",
" \n",
" # NEW Export keypoints\n",
" keypoints = extract_keypoints(results)\n",
" npy_path = os.path.join(PATH, action, str(sequence), str(frame_num))\n",
" np.save(npy_path, keypoints)\n",
"\n",
" # Break gracefully\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
" \n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fcfa9787",
"metadata": {},
"outputs": [],
"source": [
"cap.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "markdown",
"id": "77eeca8c",
"metadata": {},
"source": [
"# Preprocessing our data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff20ae81",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from tensorflow.keras.utils import to_categorical"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6608f51e",
"metadata": {},
"outputs": [],
"source": [
"label_map = {label:num for num, label in enumerate(actions)}\n",
"label_map"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "db923ffe",
"metadata": {},
"outputs": [],
"source": [
"sequences, labels = [], []\n",
"for action in actions:\n",
" for sequence in range(no_sequences):\n",
" video = []\n",
" for frame_num in range(sequence_length):\n",
" res = np.load(os.path.join(PATH, action, str(sequence), \"{}.npy\".format(frame_num)))\n",
" video.append(res)\n",
" sequences.append(video)\n",
" labels.append(label_map[action])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "323b08a9",
"metadata": {},
"outputs": [],
"source": [
"print(np.array(sequences).shape)\n",
"print(np.array(labels).shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb74ec71",
"metadata": {},
"outputs": [],
"source": [
"X = np.array(sequences)\n",
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b74e8540",
"metadata": {},
"outputs": [],
"source": [
"y = to_categorical(labels).astype(int)\n",
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1395ccc1",
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)\n",
"X_train.shape"
]
},
{
"cell_type": "markdown",
"id": "ff1588e9",
"metadata": {},
"source": [
"# Building and training LSTM DL Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "949ec61a",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import LSTM, Dense\n",
"from tensorflow.keras.layers import Dropout\n",
"# from tensorflow.keras.callbacks import TensorBoard\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f77f681",
"metadata": {},
"outputs": [],
"source": [
"# !pip install tensorboard==2.13.0\n",
"# load_ext tensorboard\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42241ea3",
"metadata": {},
"outputs": [],
"source": [
"log_dir = os.path.join('Logs')\n",
"# tb_callback = TensorBoard(log_dir=log_dir)\n",
"tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir, histogram_freq=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f38c04f",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#instantiating our model\n",
"#here we are using media-pipe holistic and lstm model .\n",
"#less data is required,it is faster to train with 1.53 million parameters\n",
"model = Sequential()\n",
"model.add(LSTM(128, return_sequences=True, activation='tanh', input_shape=(50, 1662)))\n",
"model.add(Dropout(0.2))\n",
"model.add(LSTM(256, return_sequences=True, activation='tanh'))\n",
"model.add(Dropout(0.2))\n",
"model.add(LSTM(128, return_sequences=False, activation='tanh'))\n",
"model.add(Dropout(0.2))\n",
"model.add(Dense(128, activation='relu'))\n",
"model.add(Dropout(0.2))\n",
"model.add(Dense(64, activation='relu'))\n",
"model.add(Dense(np.array(actions).shape[0], activation='softmax'))\n",
"optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1adc370e",
"metadata": {},
"outputs": [],
"source": [
"np.array(actions).shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2655277e",
"metadata": {},
"outputs": [],
"source": [
"model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4f80cbe",
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train, y_train, epochs=2000, callbacks=[tensorboard_callback])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2faa67ea",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# tensorboard --logdir logs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59d11d60",
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "markdown",
"id": "622c25a2",
"metadata": {},
"source": [
"# Predicting result from test data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ae31efd",
"metadata": {},
"outputs": [],
"source": [
"result = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e4c3ad0",
"metadata": {},
"outputs": [],
"source": [
"actions[np.argmax(result[0])]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c127bae9",
"metadata": {},
"outputs": [],
"source": [
"actions[np.argmax(y_test[0])]"
]
},
{
"cell_type": "markdown",
"id": "b1f90159",
"metadata": {},
"source": [
"# Saving weights for future"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "301ec59b",
"metadata": {},
"outputs": [],
"source": [
"tf.keras.models.save_model(\n",
" model, \"/Users/shehanliyanage/Research/MLAndFlask/finalModel.h5\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1319a76e",
"metadata": {},
"outputs": [],
"source": [
"# del model\n",
"from tensorflow.keras.models import load_model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c74de9b",
"metadata": {},
"outputs": [],
"source": [
"model.load_weights(\"finalModel.h5\")"
]
},
{
"cell_type": "markdown",
"id": "7c5a8092",
"metadata": {},
"source": [
"# Accuracy Calculation and Evaluation using Confusion matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42ef880d",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "298e79bb",
"metadata": {},
"outputs": [],
"source": [
"yhat = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f2df9c7",
"metadata": {},
"outputs": [],
"source": [
"ytrue = np.argmax(y_test, axis=1).tolist()\n",
"yhat = np.argmax(yhat, axis=1).tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1d0c708",
"metadata": {},
"outputs": [],
"source": [
"multilabel_confusion_matrix(ytrue, yhat)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0037941",
"metadata": {},
"outputs": [],
"source": [
"accuracy_score(ytrue, yhat)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de7080df",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e7e64d99",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
sign_language_backend/SignLanguageDetectionRealTimePrediction.ipynb 0 → 100644
View file @ bb4b8ed2
{
"cells": [
{
"cell_type": "markdown",
"id": "174b5cb4",
"metadata": {},
"source": [
"# Installing and importing dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d35d0afb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting tensorflow==2.13.0\n",
" Using cached tensorflow-2.13.0-cp310-cp310-macosx_12_0_arm64.whl (1.9 kB)\n",
"Collecting tensorflow-macos==2.13.0\n",
" Using cached tensorflow_macos-2.13.0-cp310-cp310-macosx_12_0_arm64.whl (189.3 MB)\n",
"Collecting numpy<=1.24.3,>=1.22\n",
" Using cached numpy-1.24.3-cp310-cp310-macosx_11_0_arm64.whl (13.9 MB)\n",
"Collecting absl-py>=1.0.0\n",
" Using cached absl_py-2.1.0-py3-none-any.whl (133 kB)\n",
"Collecting grpcio<2.0,>=1.24.3\n",
" Using cached grpcio-1.63.0-cp310-cp310-macosx_12_0_universal2.whl (10.1 MB)\n",
"Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3\n",
" Using cached protobuf-4.25.3-cp37-abi3-macosx_10_9_universal2.whl (394 kB)\n",
"Requirement already satisfied: six>=1.12.0 in ./venv/lib/python3.10/site-packages (from tensorflow-macos==2.13.0->tensorflow==2.13.0) (1.16.0)\n",
"Collecting h5py>=2.9.0\n",
" Using cached h5py-3.11.0-cp310-cp310-macosx_11_0_arm64.whl (2.9 MB)\n",
"Collecting tensorboard<2.14,>=2.13\n",
" Using cached tensorboard-2.13.0-py3-none-any.whl (5.6 MB)\n",
"Collecting flatbuffers>=23.1.21\n",
" Using cached flatbuffers-24.3.25-py2.py3-none-any.whl (26 kB)\n",
"Collecting wrapt>=1.11.0\n",
" Using cached wrapt-1.16.0-cp310-cp310-macosx_11_0_arm64.whl (38 kB)\n",
"Collecting tensorflow-estimator<2.14,>=2.13.0\n",
" Using cached tensorflow_estimator-2.13.0-py2.py3-none-any.whl (440 kB)\n",
"Requirement already satisfied: setuptools in ./venv/lib/python3.10/site-packages (from tensorflow-macos==2.13.0->tensorflow==2.13.0) (65.5.0)\n",
"Collecting opt-einsum>=2.3.2\n",
" Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)\n",
"Collecting gast<=0.4.0,>=0.2.1\n",
" Using cached gast-0.4.0-py3-none-any.whl (9.8 kB)\n",
"Requirement already satisfied: packaging in ./venv/lib/python3.10/site-packages (from tensorflow-macos==2.13.0->tensorflow==2.13.0) (24.0)\n",
"Collecting keras<2.14,>=2.13.1\n",
" Using cached keras-2.13.1-py3-none-any.whl (1.7 MB)\n",
"Collecting google-pasta>=0.1.1\n",
" Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)\n",
"Collecting typing-extensions<4.6.0,>=3.6.6\n",
" Using cached typing_extensions-4.5.0-py3-none-any.whl (27 kB)\n",
"Collecting astunparse>=1.6.0\n",
" Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
"Collecting termcolor>=1.1.0\n",
" Using cached termcolor-2.4.0-py3-none-any.whl (7.7 kB)\n",
"Collecting libclang>=13.0.0\n",
" Using cached libclang-18.1.1-py2.py3-none-macosx_11_0_arm64.whl (26.4 MB)\n",
"Collecting wheel<1.0,>=0.23.0\n",
" Using cached wheel-0.43.0-py3-none-any.whl (65 kB)\n",
"Collecting markdown>=2.6.8\n",
" Using cached Markdown-3.6-py3-none-any.whl (105 kB)\n",
"Collecting google-auth-oauthlib<1.1,>=0.5\n",
" Using cached google_auth_oauthlib-1.0.0-py2.py3-none-any.whl (18 kB)\n",
"Collecting werkzeug>=1.0.1\n",
" Using cached werkzeug-3.0.3-py3-none-any.whl (227 kB)\n",
"Collecting requests<3,>=2.21.0\n",
" Using cached requests-2.31.0-py3-none-any.whl (62 kB)\n",
"Collecting tensorboard-data-server<0.8.0,>=0.7.0\n",
" Using cached tensorboard_data_server-0.7.2-py3-none-any.whl (2.4 kB)\n",
"Collecting google-auth<3,>=1.6.3\n",
" Using cached google_auth-2.29.0-py2.py3-none-any.whl (189 kB)\n",
"Collecting pyasn1-modules>=0.2.1\n",
" Using cached pyasn1_modules-0.4.0-py3-none-any.whl (181 kB)\n",
"Collecting rsa<5,>=3.1.4\n",
" Using cached rsa-4.9-py3-none-any.whl (34 kB)\n",
"Collecting cachetools<6.0,>=2.0.0\n",
" Using cached cachetools-5.3.3-py3-none-any.whl (9.3 kB)\n",
"Collecting requests-oauthlib>=0.7.0\n",
" Using cached requests_oauthlib-2.0.0-py2.py3-none-any.whl (24 kB)\n",
"Collecting urllib3<3,>=1.21.1\n",
" Using cached urllib3-2.2.1-py3-none-any.whl (121 kB)\n",
"Collecting idna<4,>=2.5\n",
" Using cached idna-3.7-py3-none-any.whl (66 kB)\n",
"Collecting certifi>=2017.4.17\n",
" Using cached certifi-2024.2.2-py3-none-any.whl (163 kB)\n",
"Collecting charset-normalizer<4,>=2\n",
" Using cached charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl (120 kB)\n",
"Collecting MarkupSafe>=2.1.1\n",
" Using cached MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl (18 kB)\n",
"Collecting pyasn1<0.7.0,>=0.4.6\n",
" Using cached pyasn1-0.6.0-py2.py3-none-any.whl (85 kB)\n",
"Collecting oauthlib>=3.0.0\n",
" Using cached oauthlib-3.2.2-py3-none-any.whl (151 kB)\n",
"Installing collected packages: libclang, flatbuffers, wrapt, wheel, urllib3, typing-extensions, termcolor, tensorflow-estimator, tensorboard-data-server, pyasn1, protobuf, oauthlib, numpy, MarkupSafe, markdown, keras, idna, grpcio, google-pasta, gast, charset-normalizer, certifi, cachetools, absl-py, werkzeug, rsa, requests, pyasn1-modules, opt-einsum, h5py, astunparse, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow-macos, tensorflow\n",
" Attempting uninstall: typing-extensions\n",
" Found existing installation: typing_extensions 4.11.0\n",
" Uninstalling typing_extensions-4.11.0:\n",
" Successfully uninstalled typing_extensions-4.11.0\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"ipython 8.24.0 requires typing-extensions>=4.6; python_version < \"3.12\", but you have typing-extensions 4.5.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed MarkupSafe-2.1.5 absl-py-2.1.0 astunparse-1.6.3 cachetools-5.3.3 certifi-2024.2.2 charset-normalizer-3.3.2 flatbuffers-24.3.25 gast-0.4.0 google-auth-2.29.0 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.63.0 h5py-3.11.0 idna-3.7 keras-2.13.1 libclang-18.1.1 markdown-3.6 numpy-1.24.3 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-4.25.3 pyasn1-0.6.0 pyasn1-modules-0.4.0 requests-2.31.0 requests-oauthlib-2.0.0 rsa-4.9 tensorboard-2.13.0 tensorboard-data-server-0.7.2 tensorflow-2.13.0 tensorflow-estimator-2.13.0 tensorflow-macos-2.13.0 termcolor-2.4.0 typing-extensions-4.5.0 urllib3-2.2.1 werkzeug-3.0.3 wheel-0.43.0 wrapt-1.16.0\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Collecting mediapipe==0.10.3\n",
" Using cached mediapipe-0.10.3-cp310-cp310-macosx_11_0_universal2.whl (53.5 MB)\n",
"Collecting opencv-contrib-python\n",
" Using cached opencv_contrib_python-4.9.0.80-cp37-abi3-macosx_11_0_arm64.whl (44.2 MB)\n",
"Requirement already satisfied: numpy in ./venv/lib/python3.10/site-packages (from mediapipe==0.10.3) (1.24.3)\n",
"Collecting sounddevice>=0.4.4\n",
" Using cached sounddevice-0.4.6-py3-none-macosx_10_6_x86_64.macosx_10_6_universal2.whl (107 kB)\n",
"Collecting matplotlib\n",
" Using cached matplotlib-3.9.0-cp310-cp310-macosx_11_0_arm64.whl (7.8 MB)\n",
"Requirement already satisfied: flatbuffers>=2.0 in ./venv/lib/python3.10/site-packages (from mediapipe==0.10.3) (24.3.25)\n",
"Requirement already satisfied: absl-py in ./venv/lib/python3.10/site-packages (from mediapipe==0.10.3) (2.1.0)\n",
"Collecting attrs>=19.1.0\n",
" Using cached attrs-23.2.0-py3-none-any.whl (60 kB)\n",
"Collecting protobuf<4,>=3.11\n",
" Using cached protobuf-3.20.3-py2.py3-none-any.whl (162 kB)\n",
"Collecting CFFI>=1.0\n",
" Using cached cffi-1.16.0-cp310-cp310-macosx_11_0_arm64.whl (176 kB)\n",
"Collecting contourpy>=1.0.1\n",
" Using cached contourpy-1.2.1-cp310-cp310-macosx_11_0_arm64.whl (244 kB)\n",
"Requirement already satisfied: packaging>=20.0 in ./venv/lib/python3.10/site-packages (from matplotlib->mediapipe==0.10.3) (24.0)\n",
"Requirement already satisfied: python-dateutil>=2.7 in ./venv/lib/python3.10/site-packages (from matplotlib->mediapipe==0.10.3) (2.9.0.post0)\n",
"Collecting pillow>=8\n",
" Using cached pillow-10.3.0-cp310-cp310-macosx_11_0_arm64.whl (3.4 MB)\n",
"Collecting pyparsing>=2.3.1\n",
" Using cached pyparsing-3.1.2-py3-none-any.whl (103 kB)\n",
"Collecting kiwisolver>=1.3.1\n",
" Using cached kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl (66 kB)\n",
"Collecting fonttools>=4.22.0\n",
" Using cached fonttools-4.51.0-cp310-cp310-macosx_10_9_universal2.whl (2.8 MB)\n",
"Collecting cycler>=0.10\n",
" Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
"Collecting pycparser\n",
" Using cached pycparser-2.22-py3-none-any.whl (117 kB)\n",
"Requirement already satisfied: six>=1.5 in ./venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->mediapipe==0.10.3) (1.16.0)\n",
"Installing collected packages: pyparsing, pycparser, protobuf, pillow, opencv-contrib-python, kiwisolver, fonttools, cycler, contourpy, attrs, matplotlib, CFFI, sounddevice, mediapipe\n",
" Attempting uninstall: protobuf\n",
" Found existing installation: protobuf 4.25.3\n",
" Uninstalling protobuf-4.25.3:\n",
" Successfully uninstalled protobuf-4.25.3\n",
"Successfully installed CFFI-1.16.0 attrs-23.2.0 contourpy-1.2.1 cycler-0.12.1 fonttools-4.51.0 kiwisolver-1.4.5 matplotlib-3.9.0 mediapipe-0.10.3 opencv-contrib-python-4.9.0.80 pillow-10.3.0 protobuf-3.20.3 pycparser-2.22 pyparsing-3.1.2 sounddevice-0.4.6\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Collecting scikit-learn==1.3.1\n",
" Using cached scikit_learn-1.3.1-cp310-cp310-macosx_12_0_arm64.whl (9.5 MB)\n",
"Collecting scipy>=1.5.0\n",
" Using cached scipy-1.13.0-cp310-cp310-macosx_12_0_arm64.whl (30.3 MB)\n",
"Collecting threadpoolctl>=2.0.0\n",
" Using cached threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n",
"Requirement already satisfied: numpy<2.0,>=1.17.3 in ./venv/lib/python3.10/site-packages (from scikit-learn==1.3.1) (1.24.3)\n",
"Collecting joblib>=1.1.1\n",
" Using cached joblib-1.4.2-py3-none-any.whl (301 kB)\n",
"Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n",
"Successfully installed joblib-1.4.2 scikit-learn-1.3.1 scipy-1.13.0 threadpoolctl-3.5.0\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Collecting opencv-python==4.9.0.80\n",
" Using cached opencv_python-4.9.0.80-cp37-abi3-macosx_11_0_arm64.whl (35.4 MB)\n",
"Requirement already satisfied: numpy>=1.17.0 in ./venv/lib/python3.10/site-packages (from opencv-python==4.9.0.80) (1.24.3)\n",
"Installing collected packages: opencv-python\n",
"Successfully installed opencv-python-4.9.0.80\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Collecting matplotlib==3.8.3\n",
" Using cached matplotlib-3.8.3-cp310-cp310-macosx_11_0_arm64.whl (7.5 MB)\n",
"Requirement already satisfied: numpy<2,>=1.21 in ./venv/lib/python3.10/site-packages (from matplotlib==3.8.3) (1.24.3)\n",
"Requirement already satisfied: python-dateutil>=2.7 in ./venv/lib/python3.10/site-packages (from matplotlib==3.8.3) (2.9.0.post0)\n",
"Requirement already satisfied: fonttools>=4.22.0 in ./venv/lib/python3.10/site-packages (from matplotlib==3.8.3) (4.51.0)\n",
"Requirement already satisfied: pillow>=8 in ./venv/lib/python3.10/site-packages (from matplotlib==3.8.3) (10.3.0)\n",
"Requirement already satisfied: contourpy>=1.0.1 in ./venv/lib/python3.10/site-packages (from matplotlib==3.8.3) (1.2.1)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in ./venv/lib/python3.10/site-packages (from matplotlib==3.8.3) (3.1.2)\n",
"Requirement already satisfied: cycler>=0.10 in ./venv/lib/python3.10/site-packages (from matplotlib==3.8.3) (0.12.1)\n",
"Requirement already satisfied: packaging>=20.0 in ./venv/lib/python3.10/site-packages (from matplotlib==3.8.3) (24.0)\n",
"Requirement already satisfied: kiwisolver>=1.3.1 in ./venv/lib/python3.10/site-packages (from matplotlib==3.8.3) (1.4.5)\n",
"Requirement already satisfied: six>=1.5 in ./venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.8.3) (1.16.0)\n",
"Installing collected packages: matplotlib\n",
" Attempting uninstall: matplotlib\n",
" Found existing installation: matplotlib 3.9.0\n",
" Uninstalling matplotlib-3.9.0:\n",
" Successfully uninstalled matplotlib-3.9.0\n",
"Successfully installed matplotlib-3.8.3\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"!pip install tensorflow==2.13.0\n",
"!pip install mediapipe==0.10.3\n",
"!pip install scikit-learn==1.3.1\n",
"!pip install opencv-python==4.9.0.80\n",
"!pip install matplotlib==3.8.3"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0cdb80ee",
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"import os\n",
"from matplotlib import pyplot as plt\n",
"import time\n",
"import mediapipe as mp"
]
},
{
"cell_type": "markdown",
"id": "c823bc27",
"metadata": {},
"source": [
"# Collecting keypoints from mediapipe holistics"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "81a10b4a",
"metadata": {},
"outputs": [],
"source": [
"mp_holistic = mp.solutions.holistic #we are bringing the holistic model of mideapipe\n",
"mp_drawing = mp.solutions.drawing_utils #we are also bringing the drawing utilities of mideapipe."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ccb80832",
"metadata": {},
"outputs": [],
"source": [
"def mediapipe_detection(image, model):\n",
" image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB\n",
" image.flags.writeable = False # Image is no longer writeable\n",
" results = model.process(image) # Make prediction\n",
" image.flags.writeable = True # Image is now writeable \n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR\n",
" return image, results"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fe20a0f8",
"metadata": {},
"outputs": [],
"source": [
"def draw_landmarks(image, results):\n",
" # Draw face connections\n",
" mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, \n",
" mp_drawing.DrawingSpec(color=(70,110,10), thickness=1, circle_radius=1), \n",
" mp_drawing.DrawingSpec(color=(70,256,121), thickness=1, circle_radius=1)\n",
" ) \n",
" # Draw pose connections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(70,22,10), thickness=2, circle_radius=4), \n",
" mp_drawing.DrawingSpec(color=(70,44,121), thickness=2, circle_radius=2)\n",
" ) \n",
" # Draw left hand connections\n",
" mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
" mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4), \n",
" mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
" ) \n",
" # Draw right hand connections \n",
" mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n",
" ) "
]
},
{
"cell_type": "markdown",
"id": "256834f5",
"metadata": {},
"source": [
"# Extracting keypoints value"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "cdba5b51",
"metadata": {},
"outputs": [],
"source": [
"def extract_keypoints(results):\n",
" pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)\n",
" face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)\n",
" lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
" rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)\n",
" return np.concatenate([pose, face, lh, rh])"
]
},
{
"cell_type": "markdown",
"id": "28582d44",
"metadata": {},
"source": [
"# Folder setup for data Collection"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "659c0f2a",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import load_model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c2bd8487",
"metadata": {},
"outputs": [],
"source": [
"# new\n",
"actions = (\"haloo\", \"dannwa\", \"mama\", \"obata\", \"puluwan\", \"suba\", \"udaasanak\")\n",
"\n",
"model = load_model(r\"/Users/shehanliyanage/Github/Sinhala-Sign-Language-Detection-using-ACTION-RECOGNITION/finalModel.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "64ef7452",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 [==============================] - 1s 630ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 15ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 15ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 15ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 15ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 15ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 15ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 15ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 15ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 18ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-05-19 09:25:34.535 Python[49219:945613] WARNING: Secure coding is not enabled for restorable state! Enable secure coding by implementing NSApplicationDelegate.applicationSupportsSecureRestorableState: and returning YES.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"dannwa\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"dannwa\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"dannwa\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"haloo\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"obata\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"mama\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"suba\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"puluwan\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 20ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 17ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n",
"1/1 [==============================] - 0s 16ms/step\n",
"udaasanak\n",
"-----------------------------------------------\n"
]
}
],
"source": [
"sequence = []\n",
"sentence = []\n",
"predictions = []\n",
"threshold = 0.7\n",
"\n",
"cap = cv2.VideoCapture(0)\n",
"# Set mediapipe model \n",
"with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
" while cap.isOpened():\n",
"\n",
" # Read feed\n",
" ret, frame = cap.read()\n",
"\n",
" # Make detections\n",
" image, results = mediapipe_detection(frame, holistic)\n",
"# print(results)\n",
" \n",
" # Draw landmarks\n",
" draw_landmarks(image, results)\n",
" \n",
" # 2. Prediction logic\n",
" keypoints = extract_keypoints(results)\n",
" sequence.append(keypoints)\n",
" sequence = sequence[-50:]\n",
" \n",
" if len(sequence) == 50:\n",
" res = model.predict(np.expand_dims(sequence, axis=0))[0]\n",
" print(actions[np.argmax(res)])\n",
" print('-----------------------------------------------')\n",
" predictions.append(np.argmax(res))\n",
" \n",
" \n",
" #3. Viz logic\n",
" if np.unique(predictions[-25:])[0]==np.argmax(res): \n",
" if res[np.argmax(res)] > threshold: \n",
" \n",
" if len(sentence) > 0: \n",
" if actions[np.argmax(res)] != sentence[-1]:\n",
" sentence.append(actions[np.argmax(res)])\n",
" else:\n",
" sentence.append(actions[np.argmax(res)])\n",
"\n",
" if len(sentence) > 5: \n",
" sentence = sentence[-5:]\n",
" \n",
" cv2.rectangle(image, (0,0), (1500, 60), (0, 102, 255), -1)\n",
" cv2.putText(image, ' '.join(sentence), (5,20), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 255, 255), 2, cv2.LINE_AA)\n",
"\n",
" # Show to screen\n",
" cv2.imshow('OpenCV Feed', image)\n",
"\n",
" # Break gracefully\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9730488b",
"metadata": {},
"outputs": [],
"source": [
"cap.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5efd9fb7",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment