Cộng đồng Hỏi & Đáp của Inflearn

import time

file_name = 'ai_cv/video/face_01.mp4'

frame_count = 0

# csrt

# tracker = cv2.TrackerCSRT_create()

# kcf

tracker = cv2.TrackerKCF_create()

# boosting

# tracker = cv2.TrackerBoosting_create()

# mil

# tracker = cv2.TrackerMIL_create()

# tld

# tracker = cv2.TrackerTLD_create()

# medianflow

# tracker = cv2.TrackerMedianFlow_create()

# mosse

# tracker = cv2.TrackerMOSSE_create()

face_cascade_name = 'ai_cv/haarcascades/haarcascade_frontalface_alt.xml'

face_cascade = cv2.CascadeClassifier()

if not face_cascade.load(cv2.samples.findFile(face_cascade_name)):

    print('### Error loading face cascade ###')

    exit(0)

detected = False

frame_mode = 'Tracking'

elapsed_time = 0

trackers = cv2.MultiTracker_create()

vs = cv2.VideoCapture(file_name)

while True:

        ret, frame = vs.read()

        if frame is None:

            print('### No more frame ###')

            break

        start_time = time.time()

        frame_count += 1

        if detected:

            frame_mode = 'Tracking'

            (success, boxes) = trackers.update(frame)

            for box in boxes:

                (x, y, w, h) = [int(v) for v in box]

                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

        else:

            frame_mode = 'Detection'

            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

            frame_gray = cv2.equalizeHist(frame_gray)            

            faces = face_cascade.detectMultiScale(frame_gray)

            for (x,y,w,h) in faces:

                cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 255), 4)

            trackers.add(tracker, frame, tuple(faces[0]))

            detected = True

        cv2.imshow("Frame", frame)

        frame_time = time.time() - start_time

        elapsed_time += frame_time

        print("[{}] Frame {} time {}".format(frame_mode, frame_count, frame_time))

        key = cv2.waitKey(1) & 0xFF

        if key == ord("q"):

            break

print("Elapsed time {}".format(elapsed_time))

vs.release()

cv2.destroyAllWindows()

[Detection] Frame 1 time 0.049742698669433594
[Tracking] Frame 2 time 0.00684809684753418
[Tracking] Frame 3 time 0.010833024978637695
[Tracking] Frame 4 time 0.011183738708496094
[Tracking] Frame 5 time 0.009935855865478516
[Tracking] Frame 6 time 0.01006460189819336
[Tracking] Frame 7 time 0.011047124862670898
[Tracking] Frame 8 time 0.00996708869934082
[Tracking] Frame 9 time 0.009964227676391602
[Tracking] Frame 10 time 0.009967327117919922
[Tracking] Frame 11 time 0.008969306945800781
[Tracking] Frame 12 time 0.010144472122192383
[Tracking] Frame 13 time 0.010159730911254883
[Tracking] Frame 14 time 0.010029315948486328
[Tracking] Frame 15 time 0.009900569915771484
[Tracking] Frame 16 time 0.012940406799316406
[Tracking] Frame 17 time 0.01034688949584961
[Tracking] Frame 18 time 0.01052403450012207
[Tracking] Frame 19 time 0.008970022201538086
[Tracking] Frame 20 time 0.010088205337524414
[Tracking] Frame 21 time 0.010962724685668945
[Tracking] Frame 22 time 0.009985685348510742
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[Tracking] Frame 99 time 0.010538816452026367
[Tracking] Frame 100 time 0.010112285614013672
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[Tracking] Frame 103 time 0.009095907211303711
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[Tracking] Frame 105 time 0.010706901550292969
[Tracking] Frame 106 time 0.011118888854980469
[Tracking] Frame 107 time 0.009864330291748047
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[Tracking] Frame 115 time 0.010689735412597656
Elapsed time 1.2488503456115723


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