Merge branch 'master' of http://gitlab.sliit.lk/23-064/23-064

Mistaken Identification System/.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml

Mistaken Identification System/.idea/Mistaken Identification System.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.10" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

Mistaken Identification System/.idea/inspectionProfiles/Project_Default.xml

+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyInterpreterInspection" enabled="true" level="INFORMATION" enabled_by_default="true" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="12">
+            <item index="0" class="java.lang.String" itemvalue="scikit-image" />
+            <item index="1" class="java.lang.String" itemvalue="scipy" />
+            <item index="2" class="java.lang.String" itemvalue="filterpy" />
+            <item index="3" class="java.lang.String" itemvalue="PyYAML" />
+            <item index="4" class="java.lang.String" itemvalue="torch" />
+            <item index="5" class="java.lang.String" itemvalue="requests" />
+            <item index="6" class="java.lang.String" itemvalue="torchvision" />
+            <item index="7" class="java.lang.String" itemvalue="ultralytics" />
+            <item index="8" class="java.lang.String" itemvalue="tqdm" />
+            <item index="9" class="java.lang.String" itemvalue="hydra-core" />
+            <item index="10" class="java.lang.String" itemvalue="matplotlib" />
+            <item index="11" class="java.lang.String" itemvalue="lap" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N806" />
+          <option value="N802" />
+          <option value="N803" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>
\ No newline at end of file

Mistaken Identification System/.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

Mistaken Identification System/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10" project-jdk-type="Python SDK" />
+  <component name="PyCharmProfessionalAdvertiser">
+    <option name="shown" value="true" />
+  </component>
+</project>
\ No newline at end of file

Mistaken Identification System/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Mistaken Identification System.iml" filepath="$PROJECT_DIR$/.idea/Mistaken Identification System.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

Mistaken Identification System/.idea/vcs.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+  </component>
+</project>
\ No newline at end of file

Mistaken Identification System/Mistakes_Identify.py

+from yolov7_package import Yolov7Detector
+import cv2 as cv
+from functions import reSizeFrame, croppedImage, enhance_image
+from model_identification import vehicleIdentify
+
+
+def identifyMistakes(file,fps, objAccuracy, dimension, imgAccuracy, representYear,movie_name):
+    # custom variables
+    fps_count = fps  # how many frames per second eg 1
+    object_accuracy = objAccuracy  # object accurate eg 0.5,0.6,0.8
+    image_accuracy = imgAccuracy  # object accurate eg 0.5,0.6,0.8
+    object_dimension = [dimension, dimension]  # object diamention [100, 100],[50, 50]
+    year = representYear
+
+    # static variables
+    det = Yolov7Detector(traced=False)
+    color = (0, 0, 255)
+    i = 0
+    c = 0
+
+    cap = cv.VideoCapture('./Uploads/{}'.format(file))
+    fps = cap.get(cv.CAP_PROP_FPS)
+    frameCount = int(fps / fps_count)
+
+    while cap.isOpened():
+        currentFrameNo = cap.get(1)
+        ret, img = cap.read()
+
+        if not ret:
+            break
+        frame_time_ms = cap.get(cv.CAP_PROP_POS_MSEC)
+        if int(currentFrameNo) == c:
+            resized_image = reSizeFrame(img, 640, 640)
+            classes, boxes, scores = det.detect(resized_image)
+            print(classes)
+            
+            # cv.imshow("image", resized_image)
+            if cv.waitKey(1) & 0xFF == ord('q'):
+                break
+
+            for i, a in enumerate(classes[0]):
+                print(a)
+                if a == 2:
+                    print("vehicle Detected")
+                    if scores[0][i] > object_accuracy:
+                        cropped_image = croppedImage(resized_image, boxes[0][i][1], boxes[0][i][3], boxes[0][i][0],
+                                                     boxes[0][i][2])
+                        if cropped_image.shape[1] > object_dimension[0] and cropped_image.shape[0] > object_dimension[
+                            1]:
+                            # enhanced_image = enhance_image(cropped_image)
+                            vehicleIdentify(cropped_image, year, image_accuracy, frame_time_ms, resized_image,
+                                            boxes[0][i][1], boxes[0][i][3], boxes[0][i][0], boxes[0][i][2],movie_name,i)
+
+            i += 1
+            c = c + frameCount

Mistaken Identification System/functions.py

+import cv2 as cv
+import os
+import zipfile
+import shutil
+
+
+ALLOWED_EXTENSIONS = {'mp4', 'avi', 'mkv'}
+
+def reSizeFrame(frame, width: int, height: int):
+    dimentions = (width, height)
+    return cv.resize(frame, dimentions, interpolation=cv.INTER_AREA)
+
+
+def croppedImage(frame, index1, index3, index0, index2):
+    return frame[int(index1):int(index3), int(index0):int(index2)]
+    # 1:3 ,0:2
+
+
+def allowed_file(filename):
+    return '.' in filename and \
+        filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+
+def enhance_image(image, h=10, hForColor=10, templateWindowSize=7, searchWindowSize=21):
+    # Apply Non-local Means Denoising
+    return cv.fastNlMeansDenoisingColored(image, None, h, hForColor, templateWindowSize, searchWindowSize)
+
+
+def draw_rectangle(image, index1, index3, index0, index2):
+    x1, y1 = int(index0), int(index1)  # Top-left corner
+    x2, y2 = int(index2), int(index3)  # Bottom-right corner
+    color = (0, 0, 255)  # red color
+    cv.rectangle(image, (x1, y1), (x2, y2), color, thickness=2)
+    return image
+
+
+def write_image(folder_name, image, time,image_no,obj_name,index1, index0,):
+    text = str(obj_name)
+    font = cv.FONT_HERSHEY_SIMPLEX
+    font_scale = 0.5
+    font_color = (0, 0, 255)  # White color in BGR
+    thickness = 2
+    position = (int(index1), int(index0))  # (x, y) coordinates of the top-left corner of the text
+    image_with_text = cv.putText(image, text, position, font, font_scale, font_color, thickness)
+
+    seconds, milliseconds = divmod(int(time), 1000)
+    minutes, seconds = divmod(seconds, 60)
+    hours, minutes = divmod(minutes, 60)
+    file_name = str(str(hours) + "-" + str(minutes) + "-" + str(seconds)+ "_" + str(image_no))
+    if not os.path.exists("./Output/" + folder_name):
+        os.makedirs("./Output/{}/".format(folder_name))
+        cv.imwrite('./Output/{}/{}.jpg'.format(folder_name, file_name), image_with_text)
+    else:
+        cv.imwrite('./Output/{}/{}.jpg'.format(folder_name, file_name), image_with_text)
+
+
+def zip_folder(folder_name):
+    if not os.path.exists("./Output/" + folder_name):
+        return
+
+    # Name of the output ZIP archive
+    output_zip_filename = str('./Output/{}.zip'.format(folder_name))
+    # Name of the file you want to include in the ZIP archive
+    folder_to_zip = './Output/{}'.format(folder_name)
+
+    # Create a new ZIP archive and add the file to it
+    with zipfile.ZipFile(output_zip_filename, 'w', zipfile.ZIP_DEFLATED) as zipf:
+        for root, dirs, files in os.walk(folder_to_zip):
+            for file in files:
+                file_path = os.path.join(root, file)
+                arcname = os.path.relpath(file_path, folder_to_zip)
+                zipf.write(file_path, arcname)
+
+    # Reomove folder
+    shutil.rmtree(folder_to_zip)
+
+

Mistaken Identification System/main.py

+from flask import Flask, request, jsonify, send_file
+from pymongo import MongoClient
+import json
+from Mistakes_Identify import identifyMistakes
+import os
+from werkzeug.utils import secure_filename
+from functions import allowed_file, enhance_image, zip_folder
+
+app = Flask(__name__)
+
+UPLOAD_FOLDER = 'Uploads'
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+app.config[
+    'MONGO_URI'] = 'mongodb+srv://thiva:KL6f2SewDJerXNPg@first.tqnzb.mongodb.net/Research?retryWrites=true&w=majority'
+mongo = MongoClient(app.config['MONGO_URI'])
+db = mongo.researchDB
+
+
+@app.route('/clear/video', methods=['GET'])
+def clear_image():
+    enhance_image("./Images/images.jpeg", "./Output")
+    return jsonify({"message": "Video cleared successfully."}), 201
+
+
+@app.route('/check/video', methods=['POST'])
+def analise_video():
+    data = request.json
+    required_keys = ['user_id', 'movie_name', 'file_name', 'fps_rate', 'obj_accuracy', 'obj_dimension', 'img_accuracy',
+                     'year']
+    missing_keys = [key for key in required_keys if key not in data]
+    if missing_keys:
+        missing_keys_str = ', '.join(missing_keys)
+        return jsonify({"message": f"Required keys {missing_keys_str} are missing in the data."}), 400
+
+    if data.get('user_id') is None:
+        return jsonify({"message": "Please provide a user ID."}), 400
+    if data.get('movie_name') is None:
+        return jsonify({"message": "Please provide a movie name."}), 400
+    if data.get('file_name') is None:
+        return jsonify({"message": "Please provide a file name."}), 400
+    if data.get('fps_rate') is None:
+        return jsonify({"message": "Please provide a fps rate."}), 400
+    if data.get('obj_accuracy') is None:
+        return jsonify({"message": "Please provide a object accuracy."}), 400
+    if data.get('obj_dimension') is None:
+        return jsonify({"message": "Please provide a object dimension."}), 400
+    if data.get('img_accuracy') is None:
+        return jsonify({"message": "Please provide a image accuracy."}), 400
+    if data.get('year') is None:
+        return jsonify({"message": "Please provide a movie represent year."}), 400
+
+    movie_name = str(data.get('movie_name'))
+    file_name = str(data.get('file_name'))
+    fps = int(data.get('fps_rate'))
+    objAccuracy = float(data.get('obj_accuracy'))
+    dimension = int(data.get('obj_dimension'))
+    imgAccuracy = float(data.get('img_accuracy'))
+    representYear = int(data.get('year'))
+    video_path = './Uploads/{}'.format(file_name)
+    if not os.path.exists(video_path):
+        return jsonify({"message": "Movie file could not found"}), 404
+
+    identifyMistakes(file_name, fps, objAccuracy, dimension, imgAccuracy, representYear, movie_name)
+    zip_folder(movie_name)
+
+    collection = db.researchCollection
+    inserted_data = collection.insert_one(data)
+    if inserted_data:
+        return jsonify({"message": "Video analyzed successfully."}), 200
+    else:
+        return jsonify({"message": "Failed to save data"}), 500
+
+
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'video' not in request.files:
+        return jsonify({"message": "Video could not found."}), 400
+    file = request.files['video']
+    if file.filename == '':
+        return jsonify({"message": "Please select a video."}), 400
+
+    if file and allowed_file(file.filename):
+        filename = secure_filename(file.filename)
+        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        file.save(filepath)
+        return jsonify({"message": "Video uploaded successfully. Please wait for the results."}), 201
+    else:
+        return jsonify({"message": "Please upload a valid file format.(mp4, avi, mkv)"}), 400
+
+
+@app.route('/download/<filename>', methods=['GET'])
+def download_video(filename):
+    folder_path = "./Output"
+    zip_filename = "{}.zip".format(filename)
+    zip_filepath = os.path.join(folder_path, zip_filename)
+    os.system(f"zip -r {zip_filepath} {folder_path}")
+    return send_file(zip_filepath, as_attachment=True, download_name='my_folder.zip')
+
+
+@app.route('/movie/details/<userID>', methods=['GET'])
+def get_data(userID):
+    collection = db.researchCollection
+    if userID:
+        data = list(collection.find({"user_id": userID}))
+        for item in data:
+            item['_id'] = str(item['_id'])
+        if data:
+            return json.dumps(data), 200
+        else:
+            return jsonify({"message": "No data found"}), 404
+    else:
+        return jsonify({"message": "Missing user_id parameter"}), 400
+
+
+if __name__ == '__main__':
+    app.run()

Mistaken Identification System/model/labels old.txt

+0 2010-Alto
+1 2015-Alto
+2 2020-Alto
+3 2020-Civic
+4 2020-Corolla
+5 2020-Hiace
+6 2020-Hilux
+7 2020-Prius
+8 2020-Swift
+9 2020-VagonR
+10 2020-Vezel
+11 2020-Vitz

Mistaken Identification System/model/labels.txt

+0 2020-Honda-Civic-Back
+1 2020-Honda-Civic-Front
+2 2020-Toyota-Aqua-Back
+3 2020-Toyota-Aqua-Front
+4 2020-Toyota-Prius-Back
+5 2020-Toyota-Prius-Front
+6 2020-Vitz-Back
+7 2020-Vitz-Front
+8 2007-Dimo-Batta
+9 2012-Toyota-Prius-Front
+10 2012-Toyota-Prius-Back 
+11 2009-Toyota-Prius-Back 
+12 2009-Toyota-Prius-Front
+13 2010-Tata-Nano-Front
+14 2010-Tata-Nano-Back

Mistaken Identification System/model_identification.py

+from keras.models import load_model
+import tensorflow as tf
+import numpy as np
+from functions import reSizeFrame, write_image, draw_rectangle
+import cv2 as cv
+
+
+def vehicleIdentify(object_image, year, confidence_score, time, image, index1, index3, index0, index2,movie_name,image_no):
+    np.set_printoptions(suppress=True)
+    model = load_model("./model/model.h5", compile=False)
+    class_names = open("./model/labels.txt", "r").readlines()
+    new_image = reSizeFrame(object_image, 224, 224)
+
+    # cv.imshow("image", object_image)
+    # cv.waitKey()
+
+    X = tf.keras.utils.img_to_array(new_image)
+    X = np.expand_dims(X, axis=0)
+    image_data = np.vstack([X])
+
+    prediction = model.predict(image_data)
+    index = np.argmax(prediction)
+    class_name = class_names[index]
+    score = prediction[0][index]
+
+    if score > confidence_score:
+        if year < 1920:
+            print("Unrelated Object Detected")
+            image_with_rectangle = draw_rectangle(image, index1, index3, index0, index2)
+            write_image(movie_name, image_with_rectangle, time,image_no,"Unrelated",index1, index0)
+            # cv.imshow("Found Object Image", image_with_rectangle)
+            # cv.waitKey()
+        elif year < int(class_name[2:].split("-")[0]):
+            print(class_name[2:])
+            print("Confidence Score:", score)
+            image_with_rectangle = draw_rectangle(image, index1, index3, index0, index2)
+            write_image(movie_name, image_with_rectangle, time,image_no,class_name[2:],index1, index0)
+            # cv.imshow("Found Object Image", image_with_rectangle)
+            # cv.waitKey()