Commit 45822265 authored by Alexander Mordvintsev's avatar Alexander Mordvintsev

work on obj_detect.py sample (in progress...)

parent 48a5599c
......@@ -3,17 +3,16 @@ import cv2, cv
import common
def detect(img, cascade):
min_size = (20, 20)
haar_scale = 1.1
min_neighbors = 3
haar_flags = 0
rects = cascade.detectMultiScale(img, haar_scale, min_neighbors, haar_flags, min_size)
rects = cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
if len(rects) == 0:
return
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)
......@@ -22,58 +21,68 @@ def detect_turned(img, cascade):
r_ccw = detect(img_ccw, cascade)
h, w = img.shape[:2]
if r_cw is not None:
r_cw[:,[0, 2]] = h - r_cw[:,[0, 2]] - 1
r_cw = r_cw[:,[1,0,3,2]]
if r_ccw is not None:
r_ccw[:,[1, 3]] = w - r_ccw[:,[1, 3]] - 1
r_ccw = r_ccw[:,[1,0,3,2]]
rects = np.vstack( [a for a in [r, r_cw, r_ccw] if a is not None] )
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):
pass
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 = common.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)
cv2.imwrite('out/%s_%02d.bmp' % (name, i), face)
return small, rects, faces
if __name__ == '__main__':
import sys
import getopt
from glob import glob
args, img_mask = getopt.getopt(sys.argv[1:], '', ['cascade='])
args = dict(args)
# "../../data/haarcascades/haarcascade_frontalface_default.xml" #haarcascade_frontalface_default
cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
cascade = cv2.CascadeClassifier(cascade_fn)
img = cv2.imread('test.jpg')
h, w = img.shape[:2]
r = 512.0 / max(h, w)
small = cv2.resize(img, (int(w*r), int(h*r)), interpolation=cv2.INTER_AREA)
rects = detect_turned(small, cascade)
print rects
for x1, y1, x2, y2 in rects:
cv2.rectangle(small, (x1, y1), (x2, y2), (0, 255, 0))
cv2.circle(small, (x1, y1), 2, (0, 0, 255), -1)
cv2.imshow('img', small)
cv2.waitKey()
mask = 'D:/Dropbox/Photos/2011-06-12 aero/img_08[2-9]*.jpg'
for fn in glob(mask):
print fn
vis, rects, faces = process_image(fn, cascade)
cv2.imshow('img', vis)
cv2.waitKey(100)
'''
img = cv2.imread('test.jpg')
h, w = img.shape[:2]
r = 512.0 / max(h, w)
small = cv2.resize(img, (w*r, h*r), interpolation=cv2.INTER_AREA)
cv2.imshow('img', small)
cv2.waitKey()
'''
\ No newline at end of file
#vis, rects = process_image('test.jpg', cascade)
#print rects
#cv2.imshow('img', vis)
cv2.waitKey()
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