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