+++ /dev/null
-import numpy as np\r
-import cv2\r
-\r
-def detect(img, cascade):\r
- rects = cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))\r
- if len(rects) == 0:\r
- return []\r
- rects[:,2:] += rects[:,:2]\r
- return rects\r
-\r
-def detect_turned(img, cascade):\r
- img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r
- img = cv2.equalizeHist(img)\r
-\r
- img_t = cv2.transpose(img)\r
- img_cw = cv2.flip(img_t, 1)\r
- img_ccw = cv2.flip(img_t, 0)\r
- r = detect(img, cascade)\r
- r_cw = detect(img_cw, cascade)\r
- r_ccw = detect(img_ccw, cascade)\r
-\r
- h, w = img.shape[:2]\r
- rects = []\r
- rects += [(x1, y1, x2, y2, 1, 0) for x1, y1, x2, y2 in r]\r
- rects += [(y1, h-x1-1, y2, h-x2-1, 0, -1) for x1, y1, x2, y2 in r_cw]\r
- rects += [(w-y1-1, x1, w-y2-1, x2, 0, 1) for x1, y1, x2, y2 in r_ccw]\r
- return rects\r
-\r
-def process_image(fn, cascade, extract_faces=True):\r
- img = cv2.imread(fn)\r
- h, w = img.shape[:2]\r
- scale = max(h, w) / 512.0\r
- small = cv2.resize(img, (int(w/scale), int(h/scale)), interpolation=cv2.INTER_AREA)\r
- rects = detect_turned(small, cascade)\r
-\r
- for i, (x1, y1, x2, y2, vx, vy) in enumerate(rects):\r
- cv2.rectangle(small, (x1, y1), (x2, y2), (0, 255, 0))\r
- cv2.circle(small, (x1, y1), 2, (0, 0, 255), -1)\r
- cv2.putText(small, str(i), ((x1+x2)/2, (y1+y2)/2), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 255, 0))\r
-\r
- rects = np.float32(rects).reshape(-1,6)\r
- rects[:,:4] = np.around(rects[:,:4]*scale)\r
-\r
- faces = []\r
- if extract_faces:\r
- path, name, ext = splitfn(fn)\r
- face_sz = 256\r
- for i, r in enumerate(rects):\r
- p1, p2, u = r.reshape(3, 2)\r
- v = np.float32( [-u[1], u[0]] )\r
- w = np.abs(p2-p1).max()\r
- fscale = w / face_sz\r
- p0 = 0.5*(p1+p2 - w*(u+v))\r
- M = np.float32([u*fscale, v*fscale, p0]).T\r
- face = cv2.warpAffine(img, M, (face_sz, face_sz), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_AREA)\r
- faces.append(face)\r
-\r
- return small, rects, faces\r
- \r
- \r
-\r
-if __name__ == '__main__':\r
- import sys\r
- import getopt\r
- from glob import glob\r
- from common import splitfn, image_extensions\r
-\r
- args, img_args = getopt.getopt(sys.argv[1:], '', ['cascade=', 'outdir='])\r
- args = dict(args)\r
- cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")\r
- outdir = args.get('--outdir')\r
- \r
- img_list = []\r
- if len(img_args) == 0:\r
- img_list = ['../cpp/lena.jpg']\r
- else:\r
- for mask in img_args:\r
- img_list.extend(glob(mask))\r
- img_list = [fn for fn in img_list if splitfn(fn)[-1].lower() in image_extensions]\r
-\r
- cascade = cv2.CascadeClassifier(cascade_fn)\r
-\r
- for i, fn in enumerate(img_list):\r
- print '%d / %d %s' % (i+1, len(img_list), fn),\r
- vis, rects, faces = process_image(fn, cascade)\r
- if len(faces) > 0 and outdir is not None:\r
- path, name, ext = splitfn(fn)\r
- cv2.imwrite('%s/%s_all.bmp' % (outdir, name), vis)\r
- for face_i, face in enumerate(faces):\r
- cv2.imwrite('%s/%s_obj%02d.bmp' % (outdir, name, face_i), face)\r
- print ' - %d object(s) found' % len(faces)\r
- cv2.imshow('img', vis)\r
- cv2.waitKey(50)\r
- cv2.waitKey()\r