first commit

.gitignore

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+# ignore folder env
+env/

main.py

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+import cv2
+import mediapipe as mp
+import os
+import numpy as np
+
+# Create the assets directory if it doesn't exist
+if not os.path.exists('assets'):
+    os.makedirs('assets')
+
+# Load the pose detection model
+with mp.solutions.pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
+    # Get the index of the last saved pose dataset image
+    count = len([name for name in os.listdir('assets') if name.endswith('.jpg')])
+    # Capture frames from the webcam
+    cap = cv2.VideoCapture(0)
+    
+    # Load saved pose images and store their landmarks and filenames
+    saved_landmarks = []
+    saved_filenames = []
+    for i in range(count):
+        filename = f'assets/pose_{i}.jpg'
+        image = cv2.imread(filename)
+        if image is not None:
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            results = pose.process(image_rgb)
+            if results.pose_landmarks:
+                landmarks = np.array([[landmark.x, landmark.y, landmark.z] for landmark in results.pose_landmarks.landmark])
+                saved_landmarks.append(landmarks)
+                saved_filenames.append(filename)
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        # Convert the image to RGB
+        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # Process the image and find the landmarks
+        results = pose.process(image)
+        # Draw the landmarks on the image
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        mp.solutions.drawing_utils.draw_landmarks(image, results.pose_landmarks, mp.solutions.pose.POSE_CONNECTIONS)
+        
+        # Compare the pose with saved pose dataset images
+        highest_similarity = -1
+        most_similar_filename = ""
+        if results.pose_landmarks:
+            detected_landmarks = np.array([[landmark.x, landmark.y, landmark.z] for landmark in results.pose_landmarks.landmark])
+            
+            for i, saved_landmark in enumerate(saved_landmarks):
+                # Calculate cosine similarity between the landmarks
+                similarity = np.dot(detected_landmarks.flatten(), saved_landmark.flatten()) / (np.linalg.norm(detected_landmarks) * np.linalg.norm(saved_landmark))
+                
+                if similarity > highest_similarity:
+                    highest_similarity = similarity
+                    most_similar_filename = saved_filenames[i]
+        
+        # Calculate similarity percentage
+        similarity_percentage = round(highest_similarity * 100, 2)
+        
+        # Display the most similar filename and similarity percentage if similarity is above 96%
+        if similarity_percentage > 94.6:
+            text = f"Most Similar: {most_similar_filename} - Similarity: {similarity_percentage}%"
+            cv2.putText(image, text, (10, image.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+        
+        # Display the image
+        cv2.imshow('Pose Detection', image)
+
+        # Save the image if the 'q' key is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            filename = f'assets/pose_{count}.jpg'
+            # Check if the file already exists and increment the count if it does
+            while os.path.exists(filename):
+                count += 1
+                filename = f'assets/pose_{count}.jpg'
+            cv2.imwrite(filename, image)
+            print(f'Saved pose dataset image: {filename}')
+            count += 1
+        # Exit the loop if the 'ESC' key is pressed
+        if cv2.waitKey(1) == 27:
+            break
+
+    # Release the capture and destroy the window
+    cap.release()
+    cv2.destroyAllWindows()

main2.py

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+import cv2
+import mediapipe as mp
+import os
+import time
+
+# Create the assets directory if it doesn't exist
+if not os.path.exists('assets'):
+    os.makedirs('assets')
+
+# Get the index of the last saved pose dataset image
+count = len([name for name in os.listdir('assets') if name.endswith('.jpg')])
+
+with mp.solutions.pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
+    cap = cv2.VideoCapture(0)
+    start_time = time.time()
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        # Convert the image to RGB
+        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # To improve performance, optionally mark the image as not writeable to pass by reference.
+        image.flags.writeable = False
+        # Process the image and find the landmarks
+        results = pose.process(image)
+        # Draw the landmarks on the image
+        image.flags.writeable = True
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        mp.solutions.drawing_utils.draw_landmarks(image, results.pose_landmarks, mp.solutions.pose.POSE_CONNECTIONS)
+        # Display the image in a window
+        cv2.imshow('Pose Detection', image)
+        # Save the image and close the window if 5 seconds have passed
+        if time.time() - start_time > 5:
+            filename = f'assets/pose_{count}.jpg'
+            # Check if the file already exists and increment the count if it does
+            while os.path.exists(filename):
+                count += 1
+                filename = f'assets/pose_{count}.jpg'
+            cv2.imwrite(filename, image)
+            print(f'Saved pose dataset image: {filename}')
+            count += 1
+            # Close the window
+            cv2.destroyAllWindows()
+            break
+        # Exit the loop if the 'ESC' key is pressed
+        if cv2.waitKey(1) == 27:
+            break
+    # Release the capture and destroy the window
+    cap.release()
+    cv2.destroyAllWindows()

requirements.txt

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+absl-py==1.4.0
+attrs==23.1.0
+blinker==1.6.2
+cffi==1.15.1
+click==8.1.7
+contourpy==1.1.0
+cycler==0.11.0
+Flask==2.3.3
+flatbuffers==23.5.26
+fonttools==4.42.1
+importlib-metadata==6.8.0
+importlib-resources==6.0.1
+itsdangerous==2.1.2
+Jinja2==3.1.2
+kiwisolver==1.4.5
+MarkupSafe==2.1.3
+matplotlib==3.7.2
+mediapipe==0.10.3
+numpy==1.25.2
+opencv-contrib-python==4.8.0.76
+packaging==23.1
+Pillow==10.0.0
+protobuf==3.20.3
+pycparser==2.21
+pyparsing==3.0.9
+python-dateutil==2.8.2
+six==1.16.0
+sounddevice==0.4.6
+Werkzeug==2.3.7
+zipp==3.16.2

runtime.txt

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+python-3.9.10

templates/index.html

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+<!DOCTYPE html>
+<html>
+  <head>
+    <title>Pose Detection</title>
+  </head>
+  <body>
+    <h1>Pose Detection</h1>
+    <img src="{{ url_for('video_feed') }}" width="640" height="480">
+  </body>
+</html>