def detectFace(img_path,threshold):
img = cv2.imread(img_path)
caffe_img = (img.copy()-127.5)/128
origin_h,origin_w,ch = caffe_img.shape
scales = tools.calculateScales(img)
out = []
for scale in scales:
hs = int(origin_h*scale)
ws = int(origin_w*scale)
scale_img = cv2.resize(caffe_img,(ws,hs))
scale_img = np.swapaxes(scale_img, 0, 2)
net_12.blobs['data'].reshape(1,3,ws,hs)
net_12.blobs['data'].data[...]=scale_img
caffe.set_device(0)
caffe.set_mode_gpu()
out_ = net_12.forward()
out.append(out_)
image_num = len(scales)
rectangles = []
for i in range(image_num):
cls_prob = out[i]['prob1'][0][1]
roi = out[i]['conv4-2'][0]
out_h,out_w = cls_prob.shape
out_side = max(out_h,out_w)
rectangle = tools.detect_face_12net(cls_prob,roi,out_side,1/scales[i],origin_w,origin_h,threshold[0])
rectangles.extend(rectangle)
rectangles = tools.NMS(rectangles,0.7,'iou')
if len(rectangles)==0:
return rectangles
net_24.blobs['data'].reshape(len(rectangles),3,24,24)
crop_number = 0
for rectangle in rectangles:
crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img,(24,24))
scale_img = np.swapaxes(scale_img, 0, 2)
net_24.blobs['data'].data[crop_number] =scale_img
crop_number += 1
out = net_24.forward()
cls_prob = out['prob1']
roi_prob = out['conv5-2']
rectangles = tools.filter_face_24net(cls_prob,roi_prob,rectangles,origin_w,origin_h,threshold[1])
if len(rectangles)==0:
return rectangles
net_48.blobs['data'].reshape(len(rectangles),3,48,48)
crop_number = 0
for rectangle in rectangles:
crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img,(48,48))
scale_img = np.swapaxes(scale_img, 0, 2)
net_48.blobs['data'].data[crop_number] =scale_img
crop_number += 1
out = net_48.forward()
cls_prob = out['prob1']
roi_prob = out['conv6-2']
pts_prob = out['conv6-3']
rectangles = tools.filter_face_48net(cls_prob,roi_prob,pts_prob,rectangles,origin_w,origin_h,threshold[2])
return rectangles
def PNet_(self, caffe_img):
origin_h, origin_w, ch = caffe_img.shape
scales = tools.calculateScales(caffe_img, self.min_size)
out = []
for scale in scales:
hs = int(origin_h * scale)
ws = int(origin_w * scale)
#print(hs,ws)
if self.test:
scale_img = cv2.resize(caffe_img, (ws, hs))
scale_img = np.swapaxes(scale_img, 0, 2)
self.net_12.blobs['data'].reshape(1, 3, ws, hs)
else:
scale_img = cv2.resize(caffe_img, (ws, hs))
scale_img = np.transpose(scale_img, (2, 0, 1))
self.net_12.blobs['data'].reshape(1, 3, hs, ws)
scale_img = np.asarray(scale_img, dtype=np.float32)
self.net_12.blobs['data'].data[...] = scale_img
out_ = self.net_12.forward()
out.append(out_)
image_num = len(scales)
rectangles = []
for i in range(image_num):
cls_prob = out[i]['prob1'][0][1]
if self.test:
roi = out[i]['conv4-2'][0]
#roi = out[i]['conv4_2'][0]
else:
roi = out[i]['conv4_2'][0]
out_h, out_w = cls_prob.shape
out_side = max(out_h, out_w)
rectangle = tools.detect_face_12net(cls_prob, roi, out_side,
1 / scales[i], origin_w,
origin_h, self.threshold[0])
rectangles.extend(rectangle)
rectangles_box = tools.NMS(rectangles, 0.7, 'iou')
return rectangles_box
def detectFace(img, threshold):
caffe_img = (img.copy() - 127.5) / 127.5
origin_h, origin_w, ch = caffe_img.shape
scales = tools.calculateScales(img)
out = []
t0 = time.time()
# del scales[:4]
for scale in scales:
hs = int(origin_h * scale)
ws = int(origin_w * scale)
scale_img = cv2.resize(caffe_img, (ws, hs))
input = scale_img.reshape(1, *scale_img.shape)
ouput = Pnet.predict(
input
) # .transpose(0,2,1,3) should add, but seems after process is wrong then.
out.append(ouput)
image_num = len(scales)
rectangles = []
for i in range(image_num):
cls_prob = out[i][0][
0][:, :,
1] # i = #scale, first 0 select cls score, second 0 = batchnum, alway=0. 1 one hot repr
roi = out[i][1][0]
out_h, out_w = cls_prob.shape
out_side = max(out_h, out_w)
# print('calculating img scale #:', i)
cls_prob = np.swapaxes(cls_prob, 0, 1)
roi = np.swapaxes(roi, 0, 2)
rectangle = tools.detect_face_12net(cls_prob, roi, out_side,
1 / scales[i], origin_w, origin_h,
threshold[0])
rectangles.extend(rectangle)
rectangles = tools.NMS(rectangles, 0.7, 'iou')
t1 = time.time()
print('time for 12 net is: ', t1 - t0)
if len(rectangles) == 0:
return rectangles
crop_number = 0
out = []
predict_24_batch = []
for rectangle in rectangles:
crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img, (24, 24))
predict_24_batch.append(scale_img)
crop_number += 1
predict_24_batch = np.array(predict_24_batch)
out = Rnet.predict(predict_24_batch)
cls_prob = out[
0] # first 0 is to select cls, second batch number, always =0
cls_prob = np.array(cls_prob) # convert to numpy
roi_prob = out[
1] # first 0 is to select roi, second batch number, always =0
roi_prob = np.array(roi_prob)
rectangles = tools.filter_face_24net(cls_prob, roi_prob, rectangles,
origin_w, origin_h, threshold[1])
t2 = time.time()
print('time for 24 net is: ', t2 - t1)
if len(rectangles) == 0:
return rectangles
crop_number = 0
predict_batch = []
for rectangle in rectangles:
# print('calculating net 48 crop_number:', crop_number)
crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img, (48, 48))
predict_batch.append(scale_img)
crop_number += 1
predict_batch = np.array(predict_batch)
output = Onet.predict(predict_batch)
cls_prob = output[0]
roi_prob = output[1]
pts_prob = output[2] # index
# rectangles = tools.filter_face_48net_newdef(cls_prob, roi_prob, pts_prob, rectangles, origin_w, origin_h,
# threshold[2])
rectangles = tools.filter_face_48net(cls_prob, roi_prob, pts_prob,
rectangles, origin_w, origin_h,
threshold[2])
t3 = time.time()
print('time for 48 net is: ', t3 - t2)
return rectangles
def detectFace(img_path,threshold):
img = cv2.imread(img_path)
caffe_img = (img.copy()-127.5)/127.5
origin_h,origin_w,ch = caffe_img.shape
scales = tools.calculateScales(img)
out = []
for scale in scales:
hs = int(origin_h*scale)
ws = int(origin_w*scale)
scale_img = cv2.resize(caffe_img,(ws,hs))
scale_img = np.swapaxes(scale_img, 0, 2)
caffe_img_2 = img.copy()
scale_img_2 = cv2.resize(caffe_img_2,(ws,hs))
scale_img_2 = np.swapaxes(scale_img_2, 0, 2)
scale_img_2 = scale_img_2.astype(np.float32)
scale_img_2[0,:,:] = (scale_img_2[0,:,:] - 104.146) / 127.5
scale_img_2[1,:,:] = (scale_img_2[1,:,:] - 110.807) / 127.5
scale_img_2[2,:,:] = (scale_img_2[2,:,:] - 119.856) / 127.5
net_12.blobs['data'].reshape(1,3,ws,hs)
net_12.blobs['data'].data[...]=scale_img_2
caffe.set_device(0)
caffe.set_mode_gpu()
out_ = net_12.forward()
out.append(out_)
image_num = len(scales)
rectangles = []
for i in range(image_num):
cls_prob = out[i]['prob1'][0][1]
# print(cls_prob.shape)
roi = out[i]['conv4-2'][0]
out_h,out_w = cls_prob.shape
out_side = max(out_h,out_w)
#print("mean0:%f" % out[i]['prob1'][0][0].mean())
#print("mean0:%f" % out[i]['prob1'][0][1].mean())
#print("shape:",out[i]['prob1'][0][0].shape)
#print(len(np.where(out[i]['prob1'][0][0] > threshold[0])[0]))
#print(len(np.where(out[i]['prob1'][0][0] < threshold[0])[0]))
##continue
#print(cls_prob)
rectangle = tools.detect_face_12net(cls_prob,roi,out_side,1/scales[i],origin_w,origin_h,threshold[0])
rectangles.extend(rectangle)
print("-------------")
print(len(rectangles))
rectangles = tools.NMS(rectangles,0.7,'iou')
print(len(rectangles))
# if len(rectangles)==0:
# return rectangles
# net_24.blobs['data'].reshape(len(rectangles),3,24,24)
# crop_number = 0
# for rectangle in rectangles:
# crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
# scale_img = cv2.resize(crop_img,(24,24))
# scale_img = np.swapaxes(scale_img, 0, 2)
# net_24.blobs['data'].data[crop_number] =scale_img
# crop_number += 1
# out = net_24.forward()
# cls_prob = out['prob1']
# #roi_prob = out['fc5-2']
# roi_prob = out['conv5-2']
# print(len(rectangles))
# print(cls_prob.shape)
# rectangles = tools.filter_face_24net(cls_prob,roi_prob,rectangles,origin_w,origin_h,threshold[1])
# print(len(rectangles))
#
#
# if len(rectangles)==0:
# return rectangles
# net_48.blobs['data'].reshape(len(rectangles),3,48,48)
# crop_number = 0
# for rectangle in rectangles:
# crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
# scale_img = cv2.resize(crop_img,(48,48))
# scale_img = np.swapaxes(scale_img, 0, 2)
# net_48.blobs['data'].data[crop_number] =scale_img
# crop_number += 1
# out = net_48.forward()
# cls_prob = out['prob1']
# roi_prob = out['conv6-2']
# pts_prob = out['conv6-3']
# #roi_prob = out['fc6-2']
# #pts_prob = out['fc6-3']
# #roi_prob = roi_prob - roi_prob
# #print(roi_prob)
# print(cls_prob.shape)
# rectangles = tools.filter_face_48net(cls_prob,roi_prob,pts_prob,rectangles,origin_w,origin_h,threshold[2])
# rectangles = tools.NMS(rectangles,0.7,'iou')
# print(len(rectangles))
#
#####################################################
## tmp code:
# if len(rectangles)==0:
# return rectangles
# net_48.blobs['data'].reshape(len(rectangles),3,48,48)
# crop_number = 0
# bbox = "531.0 215.0 574.0 277.0 363.0 219.0 421.0 296.0 251.0 262.0 320.0 354.0 116.0 252.0 196.0 351.0 707.0 248.0 786.0 338.0 870.0 250.0 960.0 376.0 280.0 124.0 308.0 155.0"
# boxes = np.array(bbox.split(), dtype=np.float32).reshape(-1, 4)
# for rectangle in rectangles:
# print("%d %f" % (crop_number,np.max(IoU(rectangle,boxes))))
# crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
# cv2.imwrite('./crop_file2/%d.jpg' % crop_number,crop_img*127.5 + 127.5)
# scale_img = cv2.resize(crop_img,(48,48))
# cv2.imwrite('./crop_file/%d.jpg' % crop_number,scale_img*127.5 + 127.5)
# scale_img = np.swapaxes(scale_img, 0, 2)
# net_48.blobs['data'].data[crop_number] =scale_img
# crop_number += 1
# out = net_48.forward()
# cls_prob = out['prob1']
# #roi_prob = out['conv6-2']
# #pts_prob = out['conv6-3']
# roi_prob = out['fc6-2']
# pts_prob = out['fc6-3']
# roi_prob = roi_prob - roi_prob
# print(roi_prob)
# print(cls_prob.shape)
# print(len(rectangles))
# rectangles = tools.filter_face_48net(cls_prob,roi_prob,pts_prob,rectangles,origin_w,origin_h,threshold[2])
# rectangles = tools.NMS(rectangles,0.7,'iou')
# print(len(rectangles))
return rectangles
def detect_like(img):
draw = img.copy()
threshold = [0.19, 0.4, 0.4]
caffe_img = (img.copy() - 127.5) / 128
origin_h, origin_w, ch = caffe_img.shape
out = []
for scale in scales:
hs = int(origin_h * scale)
ws = int(origin_w * scale)
scale_img = cv2.resize(caffe_img, (ws, hs))
scale_img = np.swapaxes(scale_img, 0, 2)
net_12.blobs['data'].reshape(1, 3, ws, hs)
net_12.blobs['data'].data[...] = scale_img
caffe.set_device(0)
caffe.set_mode_gpu()
out_ = net_12.forward()
out.append(out_)
image_num = len(scales)
rectangles = []
for i in range(image_num):
cls_prob = out[i]['prob1'][0][1]
roi = out[i]['conv4-2'][0]
out_h, out_w = cls_prob.shape
out_side = max(out_h, out_w)
rectangle = tools.detect_face_12net(cls_prob, out_side, 1 / scales[i],
origin_w, origin_h, threshold[0])
rectangles.extend(rectangle)
rectangles = tools.NMS(rectangles, 0.7, 'iou')
#print("like")
#print(rectangles)
if len(rectangles) == 0:
return rectangles, draw
'''
net_24.blobs['data'].reshape(len(rectangles),3,24,24)
crop_number = 0
for rectangle in rectangles:
crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img,(24,24))
scale_img = np.swapaxes(scale_img, 0, 2)
net_24.blobs['data'].data[crop_number] =scale_img
crop_number += 1
out = net_24.forward()
cls_prob = out['prob1']
roi_prob = out['conv5-2']
rectangles = tools.filter_face_24net(cls_prob,roi_prob,rectangles,origin_w,origin_h,threshold[1])
#print("lik1")
#print(rectangles)
if len(rectangles)==0:
return rectangles, draw
net_48.blobs['data'].reshape(len(rectangles),3,48,48)
crop_number = 0
for rectangle in rectangles:
crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img,(48,48))
scale_img = np.swapaxes(scale_img, 0, 2)
net_48.blobs['data'].data[crop_number] =scale_img
crop_number += 1
out = net_48.forward()
cls_prob = out['prob1']
roi_prob = out['conv6-2']
pts_prob = out['conv6-3']
rectangles = tools.filter_face_48net(cls_prob,roi_prob,pts_prob,rectangles,origin_w,origin_h,threshold[2])
'''
#print("like2")
#print(rectangles)
for rectangle in rectangles:
# cv2.putText(img,str(rectangle[4]),(int(rectangle[0]),int(rectangle[1])),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0))
cv2.rectangle(draw, (int(rectangle[0]), int(rectangle[1])),
(int(rectangle[2]), int(rectangle[3])), (0, 0, 255), 1)
cv2.imshow('draw', draw)
cv2.waitKey(1)
return rectangles, draw
#----laod images---
img = cv2.imread(image_name)
#imgPad = tools.PadImg(img)
imgPad = img.copy()
cv2.imwrite("imgPad.jpg", imgPad)
img180 = cv2.flip(imgPad, 0)
img90 = cv2.transpose(imgPad)
imgNeg90 = cv2.flip(img90, 0)
cv2.imwrite("img180.jpg", img180)
cv2.imwrite("img90.jpg", img90)
cv2.imwrite("imgNeg90.jpg", imgNeg90)
rectangles = tools.detect_face_pnet(img, imgPad, pnet, 0.5, device_id=6)
rectangles = tools.NMS(rectangles, 0.5, 'iou')
rectangles = rectangles.numpy()
count = 0
imgpad_copy = imgPad.copy()
for a_rectangle in rectangles:
tmp_image = imgPad.copy()
x1, y1, x2, y2, cls_score, rotate_score = a_rectangle
print("cls_score:\t", cls_score)
print("rotate_score:\t", rotate_score)
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
cv2.rectangle(tmp_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.rectangle(imgpad_copy, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(tmp_image, str(cls_score), (x1, y1 + 5),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255), 1)
cv2.putText(tmp_image, str(rotate_score), (x1, y1 + 30),
def detectFace(img, threshold):
caffe_img = (img.copy() - 127.5) / 127.5
origin_h, origin_w, ch = caffe_img.shape
scales = tools.calculateScales(img)
print("scales: ", scales)
out = []
t0 = time.time()
# del scales[:4]
for scale in scales:
hs = int(origin_h * scale)
ws = int(origin_w * scale)
scale_img = cv2.resize(caffe_img, (ws, hs))
input = scale_img.reshape(1, *scale_img.shape)
#print("DEBUG 00: input.shape: ",input.shape)
ouput = Pnet.predict(
input
) # .transpose(0,2,1,3) should add, but seems after process is wrong then.
#------------------------------
# --------- rmaria -----------
# For every scale, the we have 2 predictions and 4 bbox regressor coordinates
classifier = Pnet.predict(
input
)[0] # shape (1,outshape1, outshape2,2) where outshape1 = the output of the image after applying KaoPnet forward pass
bbox_regressor = Pnet.predict(input)[
1] # shape (1, outshape1, outshape2,4)
#print("DEBUG 0")
classifier = np.array(classifier)
bbox_regressor = np.array(bbox_regressor)
#print("classifier.shape: ",classifier.shape)
#print("bbox_regressor.shape: ",bbox_regressor.shape)
#print("------------------------")
#print("classifier = :",classifier)
#print("------------------------")
# ---------------------------
out.append(ouput)
image_num = len(scales)
rectangles = []
for i in range(image_num):
cls_prob = out[i][0][
0][:, :,
1] # i = #scale, first 0 select cls score, second 0 = batchnum, alway=0. 1 one hot repr
#print("cls_prob.shape: ",cls_prob.shape)
roi = out[i][1][0]
out_h, out_w = cls_prob.shape
out_side = max(out_h, out_w)
# print('calculating img scale #:', i)
cls_prob = np.swapaxes(cls_prob, 0, 1)
roi = np.swapaxes(roi, 0, 2)
rectangle = tools.detect_face_12net(cls_prob, roi, out_side,
1 / scales[i], origin_w, origin_h,
threshold[0])
rectangles.extend(rectangle)
rectangles = tools.NMS(rectangles, 0.7, 'iou')
t1 = time.time()
print('time for 12 net is: ', t1 - t0)
if len(rectangles) == 0:
return rectangles
crop_number = 0
out = []
predict_24_batch = []
for rectangle in rectangles:
crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img, (24, 24))
predict_24_batch.append(scale_img)
crop_number += 1
predict_24_batch = np.array(predict_24_batch)
out = Rnet.predict(predict_24_batch)
cls_prob = out[
0] # first 0 is to select cls, second batch number, always =0
cls_prob = np.array(cls_prob) # convert to numpy
roi_prob = out[
1] # first 0 is to select roi, second batch number, always =0
roi_prob = np.array(roi_prob)
rectangles = tools.filter_face_24net(cls_prob, roi_prob, rectangles,
origin_w, origin_h, threshold[1])
t2 = time.time()
print('time for 24 net is: ', t2 - t1)
if len(rectangles) == 0:
return rectangles
crop_number = 0
predict_batch = []
for rectangle in rectangles:
# print('calculating net 48 crop_number:', crop_number)
crop_img = caffe_img[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img, (48, 48))
predict_batch.append(scale_img)
crop_number += 1
predict_batch = np.array(predict_batch)
if (use_custom_model):
output = custom_Onet_original(weights_biases_original_model,
predict_batch)
else:
output = Onet.predict(predict_batch)
cls_prob = output[0]
roi_prob = output[1]
pts_prob = output[2] # index
# rectangles = tools.filter_face_48net_newdef(cls_prob, roi_prob, pts_prob, rectangles, origin_w, origin_h,
# threshold[2])
rectangles = tools.filter_face_48net(cls_prob, roi_prob, pts_prob,
rectangles, origin_w, origin_h,
threshold[2])
t3 = time.time()
print('time for 48 net is: ', t3 - t2)
return rectangles
