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96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
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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