def detectFace(img_path, threshold):
img = cv2.imread(img_path)
caffe_img = img.copy() - 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()
caffe.set_mode_cpu()
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)
return rectangles
def detectFace(img_path,threshold):
img = cv2.imread(img_path)
caffe_img = img.copy()-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]['cls_score'][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)
return rectangles
def detectFace(img_path, threshold):
img = cv2.imread(img_path)
caffe_img = img.copy() - 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)
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[rectangle[1]:rectangle[3],
rectangle[0]: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])
return rectangles
def detectFace(img_path, threshold):
fs = cv2.FileStorage(img_path, cv2.FileStorage_READ)
img = fs.getNode('depth').mat()
fs.release()
if img is None:
print("Fail to read XML file: " + depth_path)
return None
origin_h, origin_w = img.shape
scales = tools.calculateScales(img)
out = []
for scale in scales:
hs = int(origin_h * scale)
ws = int(origin_w * scale)
net_12.blobs['data'].reshape(1, 1, hs, ws)
net_12.blobs['data'].data[...] = preprocess(img, (ws, hs))
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: ", cls_prob.shape
roi = out[i]['conv4-2'][0]
out_h, out_w = cls_prob.shape
# print "out_h: ", out_h
# print "out_w: ", out_w
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)
rectangles.extend(rectangle)
rectangles = tools.NMS(rectangles, 0.7, 'iou')
if len(rectangles) == 0:
print "rect drop to 0 at 12net"
return rectangles
rectangles_new = []
for rectangle in rectangles:
rectangles_new.append(rectangular2square(rectangle, img))
return rectangles_new
def detectFace(img_path, threshold):
# img = cv2.imread(img_path)
fs = cv2.FileStorage(img_path, cv2.FileStorage_READ)
img = fs.getNode('depth').mat()
fs.release()
origin_h, origin_w= img.shape
scales = tools.calculateScales(img)
# scales = [0.1, 0.07]
out = []
for scale in scales:
hs = int(origin_h * scale)
ws = int(origin_w * scale)
net_12.blobs['data'].reshape(1, 1, hs, ws)
net_12.blobs['data'].data[...] = preprocess(img, (ws, hs))
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: ", cls_prob.shape
roi = out[i]['conv4-2'][0]
out_h, out_w = cls_prob.shape
print "out_h: ", out_h
print "out_w: ", out_w
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:
print "rect drop to 0 at 12net"
return rectangles
doc12 = open('/home/xingduan/YupengHan/inference/saving_docs/12/12doc.txt', 'w')
for temp_rectangle in rectangles:
doc12.write('%d %d %d %d %f\n' %(temp_rectangle[0], temp_rectangle[1], temp_rectangle[2], temp_rectangle[3], temp_rectangle[4]))
# Here might be a problme
net_24.blobs['data'].reshape(len(rectangles), 1, 24, 24)
crop_number = 0
for rectangle in rectangles:
rectangle = rectangular2square(rectangle, img)
crop_img = img[int(rectangle[1]):int(
rectangle[3]), int(rectangle[0]):int(rectangle[2])]
net_24.blobs['data'].data[crop_number] = preprocess(crop_img, (24, 24))
crop_number += 1
# Here might be a problme
out = net_24.forward()
cls_prob = out['prob1']
roi_prob = out['ip_roi']
rectangles = tools.filter_face_24net(
cls_prob, roi_prob, rectangles, origin_w, origin_h, threshold[1])
doc24 = open('/home/xingduan/YupengHan/inference/saving_docs/24/24doc.txt', 'w')
for temp_rectangle in rectangles:
doc24.write('%d %d %d %d %f\n' %(temp_rectangle[0], temp_rectangle[1], temp_rectangle[2], temp_rectangle [3], temp_rectangle[4]))
if len(rectangles) == 0:
print "rect drop to 0 at 24net"
return rectangles
net_48.blobs['data'].reshape(len(rectangles), 1, 48, 48)
crop_number = 0
for rectangle in rectangles:
rectangle = rectangular2square(rectangle, img)
crop_img = img[int(rectangle[1]):int(
rectangle[3]), int(rectangle[0]):int(rectangle[2])]
net_48.blobs['data'].data[crop_number] = preprocess(crop_img, (48, 48))
crop_number += 1
out = net_48.forward()
cls_prob = out['prob1']
roi_prob = out['ip_roi']
rectangles = tools.filter_face_48net(
cls_prob, roi_prob, rectangles, origin_w, origin_h, threshold[2])
return rectangles
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, threshold):
caffe_img = (img.copy() - 127.5) / 127.5
origin_h, origin_w, ch = caffe_img.shape
scales = 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))
input = scale_img.reshape(1, *scale_img.shape)
ouput = Pnet.predict(input)
out.append(ouput)
image_num = len(scales)
rectangles = []
for i in range(image_num):
cls_prob = out[i][0][0][:, :, 1]
roi = out[i][1][0]
out_h, out_w = cls_prob.shape
out_side = max(out_h, out_w)
cls_prob = np.swapaxes(cls_prob, 0, 1)
roi = np.swapaxes(roi, 0, 2)
rectangle = detect_face_12net(cls_prob, roi, out_side, 1 / scales[i],
origin_w, origin_h, threshold[0])
rectangles.extend(rectangle)
rectangles = NMS(rectangles, 0.7, 'iou')
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 = filter_face_24net(cls_prob, roi_prob, rectangles, origin_w,
origin_h, threshold[1])
if len(rectangles) == 0:
return rectangles
crop_number = 0
predict_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, (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]
rectangles = filter_face_48net(cls_prob, roi_prob, pts_prob, rectangles,
origin_w, origin_h, threshold[2])
return rectangles