def get(self, img):
ret = self.detector.detect_face(img, det_type=0)
if ret is None:
return None
bbox, points = ret
if bbox.shape[0] == 0:
return None
bbox = bbox[0, 0:4]
points = points[0, :].reshape((2, 5)).T
M = img_helper.estimate_trans_bbox(bbox, self.image_size[0], s=2.0)
rimg = cv2.warpAffine(img, M, self.image_size, borderValue=0.0)
img = cv2.cvtColor(rimg, cv2.COLOR_BGR2RGB)
img = np.transpose(img, (2, 0, 1)) #3*112*112, RGB
input_blob = np.zeros((1, 3, self.image_size[1], self.image_size[0]),
dtype=np.uint8)
input_blob[0] = img
ta = datetime.datetime.now()
data = mx.nd.array(input_blob)
db = mx.io.DataBatch(data=(data, ))
self.model.forward(db, is_train=False)
alabel = self.model.get_outputs()[-1].asnumpy()[0]
tb = datetime.datetime.now()
print('time cost', (tb - ta).total_seconds())
ret = np.zeros((alabel.shape[0], 2), dtype=np.float32)
for i in xrange(alabel.shape[0]):
a = cv2.resize(alabel[i], (self.image_size[1], self.image_size[0]))
ind = np.unravel_index(np.argmax(a, axis=None), a.shape)
#ret[i] = (ind[0], ind[1]) #h, w
ret[i] = (ind[1], ind[0]) #w, h
return ret, M
def get(self, img):
# ret = self.detector.detect_face(img)
ret = self.detector.detect(img, threshold=0.5)
if ret is None:
return None
bbox = ret[:, 0:4]
points = ret[:, 5:15].reshape(-1, 5, 2)
# bbox, points = ret
# if bbox.shape[0]==0:
# return None
# bbox = bbox[:,0:4]
# points = points[:,:].reshape((-1,2,5))
# points = np.transpose(points, (0,2,1))
# for b in bbox:
# cv2.rectangle(img, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])), (0, 255, 0), 2)
# for p in points:
# for i in range(5):
# cv2.circle(img, (p[i][0], p[i][1]), 1, (0, 0, 255), 2)
# cv2.imshow("detection result", img)
# cv2.waitKey(0)
M = np.zeros((bbox.shape[0], 2, 3), dtype=np.float32)
ret = np.zeros((bbox.shape[0], 68, 2), dtype=np.float32)
for i in range(bbox.shape[0]):
M[i] = img_helper.estimate_trans_bbox(bbox[i, :],
self.image_size[0],
s=1.2)
rimg = cv2.warpAffine(img, M[i], self.image_size, borderValue=0.0)
img2 = cv2.cvtColor(rimg, cv2.COLOR_BGR2RGB)
img2 = np.transpose(img2, (2, 0, 1)) #3*112*112, RGB
# cv2.imshow("detection result", rimg)
# cv2.waitKey(0)
# filename = 'sample-images/%d.jpg'%(i+1)
# cv2.imwrite(filename, rimg)
input_blob = np.zeros(
(1, 3, self.image_size[1], self.image_size[0]), dtype=np.uint8)
input_blob[0] = img2
ta = datetime.datetime.now()
data = mx.nd.array(input_blob)
db = mx.io.DataBatch(data=(data, ))
self.model.forward(db, is_train=False)
alabel = self.model.get_outputs()[-1].asnumpy()[0]
tb = datetime.datetime.now()
print('module time cost', (tb - ta).total_seconds())
# ret = np.zeros( (alabel.shape[0], 2), dtype=np.float32)
for j in xrange(alabel.shape[0]):
a = cv2.resize(alabel[j],
(self.image_size[1], self.image_size[0]))
ind = np.unravel_index(np.argmax(a, axis=None), a.shape)
#ret[i] = (ind[0], ind[1]) #h, w
ret[i, j] = (ind[1], ind[0]) #w, h
return ret, M
