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removing trailing spaces
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iperov
parent
fa4e579b95
commit
a3df04999c
61 changed files with 2110 additions and 2103 deletions
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@ -5,10 +5,10 @@ import cv2
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from pathlib import Path
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from nnlib import nnlib
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class MTCExtractor(object):
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class MTCExtractor(object):
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def __init__(self):
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self.scale_to = 1920
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self.min_face_size = self.scale_to * 0.042
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self.thresh1 = 0.7
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self.thresh2 = 0.85
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@ -26,12 +26,12 @@ class MTCExtractor(object):
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x = Conv2D (32, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv3")(x)
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x = PReLU (shared_axes=[1,2], name="PReLU3" )(x)
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prob = Conv2D (2, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv41")(x)
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prob = Softmax()(prob)
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prob = Softmax()(prob)
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x = Conv2D (4, kernel_size=(1,1), strides=(1,1), padding='valid', name="conv42")(x)
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PNet_model = Model(PNet_Input, [x,prob] )
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PNet_model = Model(PNet_Input, [x,prob] )
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PNet_model.load_weights ( (Path(__file__).parent / 'mtcnn_pnet.h5').__str__() )
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RNet_Input = Input ( (24, 24, 3) )
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x = RNet_Input
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x = Conv2D (28, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
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@ -39,18 +39,18 @@ class MTCExtractor(object):
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x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='same' ) (x)
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x = Conv2D (48, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
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x = PReLU (shared_axes=[1,2], name="prelu2" )(x)
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x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='valid' ) (x)
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x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='valid' ) (x)
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x = Conv2D (64, kernel_size=(2,2), strides=(1,1), padding='valid', name="conv3")(x)
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x = PReLU (shared_axes=[1,2], name="prelu3" )(x)
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x = Lambda ( lambda x: K.reshape (x, (-1, np.prod(K.int_shape(x)[1:]),) ), output_shape=(np.prod(K.int_shape(x)[1:]),) ) (x)
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x = Dense (128, name='conv4')(x)
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x = Dense (128, name='conv4')(x)
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x = PReLU (name="prelu4" )(x)
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prob = Dense (2, name='conv51')(x)
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prob = Softmax()(prob)
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x = Dense (4, name='conv52')(x)
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RNet_model = Model(RNet_Input, [x,prob] )
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prob = Softmax()(prob)
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x = Dense (4, name='conv52')(x)
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RNet_model = Model(RNet_Input, [x,prob] )
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RNet_model.load_weights ( (Path(__file__).parent / 'mtcnn_rnet.h5').__str__() )
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ONet_Input = Input ( (48, 48, 3) )
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x = ONet_Input
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x = Conv2D (32, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv1")(x)
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@ -58,20 +58,20 @@ class MTCExtractor(object):
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x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='same' ) (x)
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x = Conv2D (64, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv2")(x)
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x = PReLU (shared_axes=[1,2], name="prelu2" )(x)
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x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='valid' ) (x)
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x = MaxPooling2D( pool_size=(3,3), strides=(2,2), padding='valid' ) (x)
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x = Conv2D (64, kernel_size=(3,3), strides=(1,1), padding='valid', name="conv3")(x)
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x = PReLU (shared_axes=[1,2], name="prelu3" )(x)
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x = MaxPooling2D( pool_size=(2,2), strides=(2,2), padding='same' ) (x)
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x = MaxPooling2D( pool_size=(2,2), strides=(2,2), padding='same' ) (x)
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x = Conv2D (128, kernel_size=(2,2), strides=(1,1), padding='valid', name="conv4")(x)
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x = PReLU (shared_axes=[1,2], name="prelu4" )(x)
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x = Lambda ( lambda x: K.reshape (x, (-1, np.prod(K.int_shape(x)[1:]),) ), output_shape=(np.prod(K.int_shape(x)[1:]),) ) (x)
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x = Lambda ( lambda x: K.reshape (x, (-1, np.prod(K.int_shape(x)[1:]),) ), output_shape=(np.prod(K.int_shape(x)[1:]),) ) (x)
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x = Dense (256, name='conv5')(x)
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x = PReLU (name="prelu5" )(x)
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prob = Dense (2, name='conv61')(x)
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prob = Softmax()(prob)
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prob = Softmax()(prob)
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x1 = Dense (4, name='conv62')(x)
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x2 = Dense (10, name='conv63')(x)
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ONet_model = Model(ONet_Input, [x1,x2,prob] )
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x2 = Dense (10, name='conv63')(x)
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ONet_model = Model(ONet_Input, [x1,x2,prob] )
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ONet_model.load_weights ( (Path(__file__).parent / 'mtcnn_onet.h5').__str__() )
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self.pnet_fun = K.function ( PNet_model.inputs, PNet_model.outputs )
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@ -79,13 +79,13 @@ class MTCExtractor(object):
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self.onet_fun = K.function ( ONet_model.inputs, ONet_model.outputs )
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def __enter__(self):
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faces, pnts = detect_face ( np.zeros ( (self.scale_to, self.scale_to, 3)), self.min_face_size, self.pnet_fun, self.rnet_fun, self.onet_fun, [ self.thresh1, self.thresh2, self.thresh3 ], self.scale_factor )
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faces, pnts = detect_face ( np.zeros ( (self.scale_to, self.scale_to, 3)), self.min_face_size, self.pnet_fun, self.rnet_fun, self.onet_fun, [ self.thresh1, self.thresh2, self.thresh3 ], self.scale_factor )
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return self
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def __exit__(self, exc_type=None, exc_value=None, traceback=None):
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return False #pass exception between __enter__ and __exit__ to outter level
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def extract_from_bgr (self, input_image):
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input_image = input_image[:,:,::-1].copy()
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(h, w, ch) = input_image.shape
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@ -95,7 +95,7 @@ class MTCExtractor(object):
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detected_faces, pnts = detect_face ( input_image, self.min_face_size, self.pnet_fun, self.rnet_fun, self.onet_fun, [ self.thresh1, self.thresh2, self.thresh3 ], self.scale_factor )
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detected_faces = [ ( int(face[0]/input_scale), int(face[1]/input_scale), int(face[2]/input_scale), int(face[3]/input_scale)) for face in detected_faces ]
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return detected_faces
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def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
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@ -132,9 +132,9 @@ def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
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out = pnet([img_y])
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out0 = np.transpose(out[0], (0,2,1,3))
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out1 = np.transpose(out[1], (0,2,1,3))
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boxes, _ = generateBoundingBox(out1[0,:,:,1].copy(), out0[0,:,:,:].copy(), scale, threshold[0])
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# inter-scale nms
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pick = nms(boxes.copy(), 0.5, 'Union')
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if boxes.size>0 and pick.size>0:
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@ -217,7 +217,7 @@ def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
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pick = nms(total_boxes.copy(), 0.7, 'Min')
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total_boxes = total_boxes[pick,:]
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points = points[:,pick]
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return total_boxes, points
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@ -235,7 +235,7 @@ def bbreg(boundingbox,reg):
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b4 = boundingbox[:,3]+reg[:,3]*h
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boundingbox[:,0:4] = np.transpose(np.vstack([b1, b2, b3, b4 ]))
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return boundingbox
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def generateBoundingBox(imap, reg, scale, t):
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"""Use heatmap to generate bounding boxes"""
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stride=2
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@ -261,7 +261,7 @@ def generateBoundingBox(imap, reg, scale, t):
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q2 = np.fix((stride*bb+cellsize-1+1)/scale)
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boundingbox = np.hstack([q1, q2, np.expand_dims(score,1), reg])
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return boundingbox, reg
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# function pick = nms(boxes,threshold,type)
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def nms(boxes, threshold, method):
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if boxes.size==0:
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@ -315,7 +315,7 @@ def pad(total_boxes, w, h):
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tmp = np.where(ex>w)
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edx.flat[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],1)
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ex[tmp] = w
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tmp = np.where(ey>h)
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edy.flat[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],1)
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ey[tmp] = h
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@ -327,7 +327,7 @@ def pad(total_boxes, w, h):
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tmp = np.where(y<1)
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dy.flat[tmp] = np.expand_dims(2-y[tmp],1)
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y[tmp] = 1
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return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph
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# function [bboxA] = rerec(bboxA)
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