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Open Source Computer Vision Library
https://opencv.org/
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59 lines
1.9 KiB
59 lines
1.9 KiB
import numpy as np |
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import cv2 |
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import cv2.cv as cv |
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from video import create_capture |
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from common import clock, draw_str |
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help_message = ''' |
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USAGE: facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<video_source>] |
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''' |
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def detect(img, cascade): |
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rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE) |
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if len(rects) == 0: |
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return [] |
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rects[:,2:] += rects[:,:2] |
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return rects |
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def draw_rects(img, rects, color): |
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for x1, y1, x2, y2 in rects: |
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cv2.rectangle(img, (x1, y1), (x2, y2), color, 2) |
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if __name__ == '__main__': |
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import sys, getopt |
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print help_message |
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args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade=']) |
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try: video_src = video_src[0] |
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except: video_src = 'synth:bg=../cpp/lena.jpg:noise=0.05' |
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args = dict(args) |
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cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml") |
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nested_fn = args.get('--nested-cascade', "../../data/haarcascades/haarcascade_eye.xml") |
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cascade = cv2.CascadeClassifier(cascade_fn) |
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nested = cv2.CascadeClassifier(nested_fn) |
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cam = create_capture(video_src) |
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while True: |
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ret, img = cam.read() |
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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gray = cv2.equalizeHist(gray) |
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t = clock() |
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rects = detect(gray, cascade) |
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vis = img.copy() |
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draw_rects(vis, rects, (0, 255, 0)) |
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for x1, y1, x2, y2 in rects: |
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roi = gray[y1:y2, x1:x2] |
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vis_roi = vis[y1:y2, x1:x2] |
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subrects = detect(roi.copy(), nested) |
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draw_rects(vis_roi, subrects, (255, 0, 0)) |
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dt = clock() - t |
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draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000)) |
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cv2.imshow('facedetect', vis) |
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if cv2.waitKey(5) == 27: |
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break |
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