57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
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import cv2
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import json
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dataset = "dataset.json"
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# open the dataset file
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with open(dataset, 'r') as f:
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data = json.load(f)
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# check the last data
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last_data = data[-1]
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# check the name
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name = last_data["name"]
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# Open a video capture from the default camera (0)
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video_capture = cv2.VideoCapture(0)
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# Set the window to normal so it can be resized
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cv2.namedWindow('Image Capture', cv2.WINDOW_NORMAL)
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# Set the window to full screen
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cv2.setWindowProperty('Image Capture', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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while True:
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# Capture a frame from the video stream
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ret, frame = video_capture.read()
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# Convert the frame to grayscale for face detection
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gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# Detect faces in the frame
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faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
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# Initialize the key variable to None
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key = cv2.waitKey(1)
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# Press 'q' to save the entire image, but only if faces were detected
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if len(faces) > 0 and key == ord('q'):
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# Save the frame before drawing the rectangle
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cv2.imwrite('faces/' + name + '.jpg', frame)
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break
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# Draw rectangles around the detected faces
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for (x, y, w, h) in faces:
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cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
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# Display the frame
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cv2.imshow('Image Capture', frame)
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# Release the video capture and close the OpenCV window
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video_capture.release()
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cv2.destroyAllWindows()
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