101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
import cv2
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import dlib
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import time # To measure time for FPS calculation
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# GStreamer pipeline to acquire video from the CSI camera (Jetson Nano)
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def gstreamer_pipeline(
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sensor_id=0,
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capture_width=1280,
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capture_height=720,
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display_width=640,
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display_height=360,
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framerate=15,
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flip_method=0,
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):
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return (
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f"nvarguscamerasrc sensor-id={sensor_id} ! "
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f"video/x-raw(memory:NVMM), width=(int){capture_width}, height=(int){capture_height}, "
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f"format=(string)NV12, framerate=(fraction){framerate}/1 ! "
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f"nvvidconv flip-method={flip_method} ! "
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f"video/x-raw, width=(int){display_width}, height=(int){display_height}, format=(string)BGRx ! "
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f"videoconvert ! video/x-raw, format=(string)BGR ! appsink"
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)
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# Capture from the camera using OpenCV and start tracking
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cap = cv2.VideoCapture(gstreamer_pipeline(), cv2.CAP_GSTREAMER)
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if not cap.isOpened():
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print("Unable to open camera")
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else:
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# Read the first frame to allow user to draw a bounding box
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ret, frame = cap.read()
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if not ret:
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print("Failed to grab the initial frame")
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cap.release()
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# Let the user draw the initial bounding box
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bbox = cv2.selectROI("Select ROI", frame, fromCenter=False, showCrosshair=True)
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# Initialize Dlib correlation tracker
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tracker = dlib.correlation_tracker()
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tracker.start_track(frame, dlib.rectangle(bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]))
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img_counter = 0 # To count frames and name images
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frame_counter = 0 # To track the number of frames
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start_time = time.time() # Start time for FPS calculation
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try:
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while True:
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ret, frame = cap.read()
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if not ret:
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print("Failed to grab frame")
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break
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# Start measuring time per frame
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frame_start_time = time.time()
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# Update the tracker with the current frame
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tracker.update(frame)
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pos = tracker.get_position()
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# Extract the bounding box from the tracker
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x1, y1, x2, y2 = int(pos.left()), int(pos.top()), int(pos.right()), int(pos.bottom())
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# Draw the updated tracking bounding box on the frame
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
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# Show the frame with the tracking rectangle
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cv2.imshow("Tracking", frame)
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# Save every 10th frame
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if frame_counter % 10 == 0:
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img_name = f"tracked_frame_{img_counter}.jpg"
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cv2.imwrite(img_name, frame)
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print(f"Saved {img_name}")
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img_counter += 1
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# Calculate and print FPS
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frame_time = time.time() - frame_start_time
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fps = 1.0 / frame_time
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print(f"FPS: {fps:.2f}")
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frame_counter += 1
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# Exit if 'q' is pressed
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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except KeyboardInterrupt:
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print("Tracking interrupted by user")
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cap.release()
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cv2.destroyAllWindows()
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# Calculate and print average FPS over all frames
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end_time = time.time()
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total_time = end_time - start_time
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average_fps = frame_counter / total_time
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print(f"Average FPS: {average_fps:.2f}")
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print("Finished tracking")
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