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Open Source Computer Vision Library
https://opencv.org/
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95 lines
3.3 KiB
95 lines
3.3 KiB
import numpy as np |
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import cv2 |
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import cv2.cv as cv |
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def detect(img, cascade): |
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rects = cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) |
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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 detect_turned(img, cascade): |
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img = cv2.cvtColor(img, cv.CV_BGR2GRAY) |
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img = cv2.equalizeHist(img) |
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img_t = cv2.transpose(img) |
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img_cw = cv2.flip(img_t, 1) |
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img_ccw = cv2.flip(img_t, 0) |
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r = detect(img, cascade) |
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r_cw = detect(img_cw, cascade) |
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r_ccw = detect(img_ccw, cascade) |
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h, w = img.shape[:2] |
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rects = [] |
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rects += [(x1, y1, x2, y2, 1, 0) for x1, y1, x2, y2 in r] |
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rects += [(y1, h-x1-1, y2, h-x2-1, 0, -1) for x1, y1, x2, y2 in r_cw] |
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rects += [(w-y1-1, x1, w-y2-1, x2, 0, 1) for x1, y1, x2, y2 in r_ccw] |
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return rects |
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def process_image(fn, cascade, extract_faces=True): |
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img = cv2.imread(fn) |
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h, w = img.shape[:2] |
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scale = max(h, w) / 512.0 |
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small = cv2.resize(img, (int(w/scale), int(h/scale)), interpolation=cv2.INTER_AREA) |
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rects = detect_turned(small, cascade) |
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for i, (x1, y1, x2, y2, vx, vy) in enumerate(rects): |
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cv2.rectangle(small, (x1, y1), (x2, y2), (0, 255, 0)) |
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cv2.circle(small, (x1, y1), 2, (0, 0, 255), -1) |
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cv2.putText(small, str(i), ((x1+x2)/2, (y1+y2)/2), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 255, 0)) |
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rects = np.float32(rects).reshape(-1,6) |
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rects[:,:4] = np.around(rects[:,:4]*scale) |
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faces = [] |
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if extract_faces: |
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path, name, ext = splitfn(fn) |
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face_sz = 256 |
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for i, r in enumerate(rects): |
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p1, p2, u = r.reshape(3, 2) |
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v = np.float32( [-u[1], u[0]] ) |
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w = np.abs(p2-p1).max() |
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fscale = w / face_sz |
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p0 = 0.5*(p1+p2 - w*(u+v)) |
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M = np.float32([u*fscale, v*fscale, p0]).T |
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face = cv2.warpAffine(img, M, (face_sz, face_sz), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_AREA) |
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faces.append(face) |
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return small, rects, faces |
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if __name__ == '__main__': |
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import sys |
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import getopt |
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from glob import glob |
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from common import splitfn, image_extensions |
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args, img_args = getopt.getopt(sys.argv[1:], '', ['cascade=', 'outdir=']) |
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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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outdir = args.get('--outdir') |
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img_list = [] |
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if len(img_args) == 0: |
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img_list = ['../cpp/lena.jpg'] |
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else: |
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for mask in img_args: |
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img_list.extend(glob(mask)) |
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img_list = [fn for fn in img_list if splitfn(fn)[-1].lower() in image_extensions] |
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cascade = cv2.CascadeClassifier(cascade_fn) |
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for i, fn in enumerate(img_list): |
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print '%d / %d %s' % (i+1, len(img_list), fn), |
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vis, rects, faces = process_image(fn, cascade) |
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if len(faces) > 0 and outdir is not None: |
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path, name, ext = splitfn(fn) |
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cv2.imwrite('%s/%s_all.bmp' % (outdir, name), vis) |
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for face_i, face in enumerate(faces): |
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cv2.imwrite('%s/%s_obj%02d.bmp' % (outdir, name, face_i), face) |
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print ' - %d object(s) found' % len(faces) |
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cv2.imshow('img', vis) |
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cv2.waitKey(50) |
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cv2.waitKey()
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