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hvg.py
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hvg.py
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import cv2
# Initialize the HOG descriptor
hog = cv2.HOGDescriptor()
# Initialize the SVM classifier
svm = cv2.ml.SVM_create()
# Set the SVM parameters
svm.setType(cv2.ml.SVM_C_SVC)
svm.setKernel(cv2.ml.SVM_LINEAR)
# Load the video file
cap = cv2.VideoCapture('video.mp4')
# Loop through each frame in the video
while cap.isOpened():
# Read the frame from the video
ret, frame = cap.read()
if ret:
# Convert the frame to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Compute the HOG descriptors for the frame
descriptors = hog.compute(gray)
# Use the SVM classifier to predict the presence of faces
prediction = svm.predict(descriptors)[1]
# Draw a rectangle around the detected faces
for i in range(prediction.shape[0]):
if prediction[i] == 1:
x, y, w, h = hog.detectMultiScale(gray[i:i+64, j:j+128])
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Display the frame with the detected faces
cv2.imshow('frame', frame)
# Wait for the 'q' key to be pressed to quit the program
if cv2.waitKey(25) & 0xFF == ord('q'):
break
else:
break
# Release the video capture object and close all windows
cap.release()
cv2.destroyAllWindows()