Create gait_recognition.py

Author: 770navyasharma

OpenCV Projects/Gait Recognition/gait_recognition.py

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+import cv2
+import numpy as np
+from sklearn.neighbors import KNeighborsClassifier
+
+# Load the pre-trained gait recognition model (or train a new one)
+model = KNeighborsClassifier(n_neighbors=3)
+
+# Function to perform background subtraction and silhouette extraction
+def extract_silhouette(frame):
+    # Convert the frame to grayscale
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    
+    # Apply background subtraction
+    fgmask = cv2.createBackgroundSubtractorMOG2().apply(gray)
+    
+    # Threshold to binarize the silhouette
+    _, silhouette = cv2.threshold(fgmask, 250, 255, cv2.THRESH_BINARY)
+    
+    return silhouette
+
+# Function to extract gait features from the silhouette
+def extract_gait_features(silhouette):
+    # Example: Extract contour area as a feature
+    contours, _ = cv2.findContours(silhouette, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if contours:
+        largest_contour = max(contours, key=cv2.contourArea)
+        return [cv2.contourArea(largest_contour)]
+    return [0]  # Return zero if no valid silhouette is found
+
+# Start capturing video (from webcam or pre-recorded video)
+cap = cv2.VideoCapture('walking_video.mp4')
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # Extract silhouette from the current frame
+    silhouette = extract_silhouette(frame)
+
+    # Extract gait features
+    gait_features = extract_gait_features(silhouette)
+
+    # Display the silhouette
+    cv2.imshow("Silhouette", silhouette)
+
+    # Check for user input to exit
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()