f:scene script

Scene_Classify/classify_model/label.txt

+kitchen
+livingroom
+stairs

Scene_Classify/detection_image3.py

+import os
+import argparse
+import cv2
+import numpy as np
+import sys
+import importlib.util
+
+# Define and parse input arguments
+parser = argparse.ArgumentParser()
+parser.add_argument('--modeldir', help='Folder the .tflite file is located in', required=True)
+parser.add_argument('--graph', help='Name of the .tflite file, if different than detect.tflite', default='detect.tflite')
+parser.add_argument('--labels', help='Name of the labelmap file, if different than labelmap.txt', default='label.txt')
+parser.add_argument('--threshold', help='Minimum confidence threshold for displaying detected objects', default=0.5)
+parser.add_argument('--edgetpu', help='Use Coral Edge TPU Accelerator to speed up detection', action='store_true')
+
+args = parser.parse_args()
+
+MODEL_NAME = args.modeldir
+GRAPH_NAME = args.graph
+LABELMAP_NAME = args.labels
+min_conf_threshold = float(args.threshold)
+use_TPU = args.edgetpu
+
+# Import TensorFlow libraries
+pkg = importlib.util.find_spec('tflite_runtime')
+if pkg:
+    from tflite_runtime.interpreter import Interpreter
+    if use_TPU:
+        from tflite_runtime.interpreter import load_delegate
+else:
+    from tensorflow.lite.python.interpreter import Interpreter
+    if use_TPU:
+        from tensorflow.lite.python.interpreter import load_delegate
+
+# If using Edge TPU, assign filename for Edge TPU model
+if use_TPU:
+    if GRAPH_NAME == 'detect.tflite':
+        GRAPH_NAME = 'edgetpu.tflite'
+
+# Get path to current working directory
+CWD_PATH = os.getcwd()
+
+# Path to .tflite file, which contains the model that is used for object detection
+PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, GRAPH_NAME)
+
+# Path to label map file
+PATH_TO_LABELS = os.path.join(CWD_PATH, MODEL_NAME, LABELMAP_NAME)
+
+# Load the label map
+with open(PATH_TO_LABELS, 'r') as f:
+    labels = [line.strip() for line in f.readlines()]
+
+# Initialize previous label to None
+prev_label = None
+
+# Load the TensorFlow Lite model
+if use_TPU:
+    interpreter = Interpreter(model_path=PATH_TO_CKPT, experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
+else:
+    interpreter = Interpreter(model_path=PATH_TO_CKPT)
+
+interpreter.allocate_tensors()
+
+# Get model details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+height = input_details[0]['shape'][1]
+width = input_details[0]['shape'][2]
+
+floating_model = (input_details[0]['dtype'] == np.float32)
+
+input_mean = 127.5
+input_std = 127.5
+
+# Create output folder to save labeled images
+output_folder = 'labeled_images'
+os.makedirs(output_folder, exist_ok=True)
+
+# Iterate over the images
+images_dir = 'D:\ObjectDetection\Try_17.0_classification_mobilenet_balanceddataset\images'  # Path to the directory containing the images
+image_files = os.listdir(images_dir)
+for image_file in image_files:
+    image_path = os.path.join(images_dir, image_file)
+    frame = cv2.imread(image_path)
+    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    frame_resized = cv2.resize(frame_rgb, (width, height))
+    input_data = np.expand_dims(frame_resized, axis=0)
+
+    # Normalize pixel values if using a floating model (i.e., if the model is non-quantized)
+    if floating_model:
+        input_data = (np.float32(input_data) - input_mean) / input_std
+
+    # Perform model inference
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+    interpreter.invoke()
+
+    # Get prediction from output tensor
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    prediction = np.argmax(output_data)
+
+    # Get label name
+    label_name = labels[prediction]
+
+    # Only print label name if it's different from the previous label
+    if label_name != prev_label:
+        print(label_name)
+        prev_label = label_name
+
+    # Draw label on the image
+    cv2.putText(frame, label_name, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
+
+    # Show the image in a window
+    cv2.imshow('Indoor Scenes', frame)
+    cv2.waitKey(0)  # Wait until a key is pressed to proceed to the next image
+
+    # Save the labeled image in the output folder
+    output_image_path = os.path.join(output_folder, image_file)
+    cv2.imwrite(output_image_path, frame)
+
+# Clean up
+cv2.destroyAllWindows()