**主要用途** 该项目的主要用途是制作自己脸部的VOC格式的数据集，并应用于人脸识别当中。**环境** tensorflow,opencv,numpy
**使用教程**
打开文件```$ python MTCNN_FACE.py --save_path="你要保存的目录" --dataset_name="你的数据名字"```加载完后，会出现在Video的窗口按 C 截取图片，按 Q 退出程序（注意每次截取4张以上，才有完整的标记数据文件）
from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom six import string_types, iteritemsimport numpy as npimport tensorflow as tf#from math import floorimport cv2import osdef layer(op): '''Decorator for composable network layers.''' def layer_decorated(self, *args, **kwargs): # Automatically set a name if not provided. name = kwargs.setdefault('name', self.get_unique_name(op.__name__)) # Figure out the layer inputs. if len(self.terminals) == 0: raise RuntimeError('No input variables found for layer %s.' % name) elif len(self.terminals) == 1: layer_input = self.terminals[0] else: layer_input = list(self.terminals) # Perform the operation and get the output. layer_output = op(self, layer_input, *args, **kwargs) # Add to layer LUT. self.layers[name] = layer_output # This output is now the input for the next layer. self.feed(layer_output) # Return self for chained calls. return self return layer_decoratedclass Network(object): def __init__(self, inputs, trainable=True): # The input nodes for this network self.inputs = inputs # The current list of terminal nodes self.terminals = [] # Mapping from layer names to layers self.layers = dict(inputs) # If true, the resulting variables are set as trainable self.trainable = trainable self.setup() def setup(self): '''Construct the network. ''' raise NotImplementedError('Must be implemented by the subclass.') def load(self, data_path, session, ignore_missing=False): '''Load network weights. data_path: The path to the numpy-serialized network weights session: The current TensorFlow session ignore_missing: If true, serialized weights for missing layers are ignored. ''' data_dict = np.load(data_path, encoding='latin1').item() #pylint: disable=no-member for op_name in data_dict: with tf.variable_scope(op_name, reuse=True): for param_name, data in iteritems(data_dict[op_name]): try: var = tf.get_variable(param_name) session.run(var.assign(data)) except ValueError: if not ignore_missing: raise def feed(self, *args): '''Set the input(s) for the next operation by replacing the terminal nodes. The arguments can be either layer names or the actual layers. ''' assert len(args) != 0 self.terminals = [] for fed_layer in args: if isinstance(fed_layer, string_types): try: fed_layer = self.layers[fed_layer] except KeyError: raise KeyError('Unknown layer name fed: %s' % fed_layer) self.terminals.append(fed_layer) return self def get_output(self): '''Returns the current network output.''' return self.terminals[-1] def get_unique_name(self, prefix): '''Returns an index-suffixed unique name for the given prefix. This is used for auto-generating layer names based on the type-prefix. ''' ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) 1 return '%s_%d' % (prefix, ident) def make_var(self, name, shape): '''Creates a new TensorFlow variable.''' return tf.get_variable(name, shape, trainable=self.trainable) def validate_padding(self, padding): '''Verifies that the padding is one of the supported ones.''' assert padding in ('SAME', 'VALID') @layer def conv(self, inp, k_h, k_w, c_o, s_h, s_w, name, relu=True, padding='SAME', group=1, biased=True): # Verify that the padding is acceptable self.validate_padding(padding) # Get the number of channels in the input c_i = int(inp.get_shape()[-1]) # Verify that the grouping parameter is valid assert c_i % group == 0 assert c_o % group == 0 # Convolution for a given input and kernel convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding) with tf.variable_scope(name) as scope: kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o]) # This is the common-case. Convolve the input without any further complications. output = convolve(inp, kernel) # Add the biases if biased: biases = self.make_var('biases', [c_o]) output = tf.nn.bias_add(output, biases) if relu: # ReLU non-linearity output = tf.nn.relu(output, name=scope.name) return output @layer def prelu(self, inp, name): with tf.variable_scope(name): i = int(inp.get_shape()[-1]) alpha = self.make_var('alpha', shape=(i,)) output = tf.nn.relu(inp) tf.multiply(alpha, -tf.nn.relu(-inp)) return output @layer def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'): self.validate_padding(padding) return tf.nn.max_pool(inp,ksize=[1, k_h, k_w, 1],strides=[1, s_h, s_w, 1],padding=padding,name=name) @layer def fc(self, inp, num_out, name, relu=True): with tf.variable_scope(name): input_shape = inp.get_shape() if input_shape.ndims == 4: # The input is spatial. Vectorize it first. dim = 1 for d in input_shape[1:].as_list(): dim *= int(d) feed_in = tf.reshape(inp, [-1, dim]) else: feed_in, dim = (inp, input_shape[-1].value) weights = self.make_var('weights', shape=[dim, num_out]) biases = self.make_var('biases', [num_out]) op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b fc = op(feed_in, weights, biases, name=name) return fc """ Multi dimensional softmax, refer to https://github.com/tensorflow/tensorflow/issues/210 compute softmax along the dimension of target the native softmax only supports batch_size x dimension """ @layer def softmax(self, target, axis, name=None): max_axis = tf.reduce_max(target, axis, keep_dims=True) target_exp = tf.exp(target-max_axis) normalize = tf.reduce_sum(target_exp, axis, keep_dims=True) softmax = tf.div(target_exp, normalize, name) return softmax class PNet(Network): def setup(self): (self.feed('data') #pylint: disable=no-value-for-parameter, no-member .conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1') .prelu(name='PReLU1') .max_pool(2, 2, 2, 2, name='pool1') .conv(3, 3, 16, 1, 1, padding='VALID', relu=False, name='conv2') .prelu(name='PReLU2') .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv3') .prelu(name='PReLU3') .conv(1, 1, 2, 1, 1, relu=False, name='conv4-1') .softmax(3,name='prob1')) (self.feed('PReLU3') #pylint: disable=no-value-for-parameter .conv(1, 1, 4, 1, 1, relu=False, name='conv4-2')) class RNet(Network): def setup(self): (self.feed('data') #pylint: disable=no-value-for-parameter, no-member .conv(3, 3, 28, 1, 1, padding='VALID', relu=False, name='conv1') .prelu(name='prelu1') .max_pool(3, 3, 2, 2, name='pool1') .conv(3, 3, 48, 1, 1, padding='VALID', relu=False, name='conv2') .prelu(name='prelu2') .max_pool(3, 3, 2, 2, padding='VALID', name='pool2') .conv(2, 2, 64, 1, 1, padding='VALID', relu=False, name='conv3') .prelu(name='prelu3') .fc(128, relu=False, name='conv4') .prelu(name='prelu4') .fc(2, relu=False, name='conv5-1') .softmax(1,name='prob1')) (self.feed('prelu4') #pylint: disable=no-value-for-parameter .fc(4, relu=False, name='conv5-2'))class ONet(Network): def setup(self): (self.feed('data') #pylint: disable=no-value-for-parameter, no-member .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv1') .prelu(name='prelu1') .max_pool(3, 3, 2, 2, name='pool1') .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv2') .prelu(name='prelu2') .max_pool(3, 3, 2, 2, padding='VALID', name='pool2') .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv3') .prelu(name='prelu3') .max_pool(2, 2, 2, 2, name='pool3') .conv(2, 2, 128, 1, 1, padding='VALID', relu=False, name='conv4') .prelu(name='prelu4') .fc(256, relu=False, name='conv5') .prelu(name='prelu5') .fc(2, relu=False, name='conv6-1') .softmax(1, name='prob1')) (self.feed('prelu5') #pylint: disable=no-value-for-parameter .fc(4, relu=False, name='conv6-2')) (self.feed('prelu5') #pylint: disable=no-value-for-parameter .fc(10, relu=False, name='conv6-3'))def create_mtcnn(sess, model_path): if not model_path: model_path,_ = os.path.split(os.path.realpath(__file__)) with tf.variable_scope('pnet'): data = tf.placeholder(tf.float32, (None,None,None,3), 'input') pnet = PNet({'data':data}) pnet.load(os.path.join(model_path, 'det1.npy'), sess) with tf.variable_scope('rnet'): data = tf.placeholder(tf.float32, (None,24,24,3), 'input') rnet = RNet({'data':data}) rnet.load(os.path.join(model_path, 'det2.npy'), sess) with tf.variable_scope('onet'): data = tf.placeholder(tf.float32, (None,48,48,3), 'input') onet = ONet({'data':data}) onet.load(os.path.join(model_path, 'det3.npy'), sess) pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img}) rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img}) onet_fun = lambda img : sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'), feed_dict={'onet/input:0':img}) return pnet_fun, rnet_fun, onet_fundef detect_face(img, minsize, pnet, rnet, onet, threshold, factor): # im: input image # minsize: minimum of faces' size # pnet, rnet, onet: caffemodel # threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold # fastresize: resize img from last scale (using in high-resolution images) if fastresize==true factor_count=0 total_boxes=np.empty((0,9)) points=[] h=img.shape[0] w=img.shape[1] minl=np.amin([h, w]) m=12.0/minsize minl=minl*m # creat scale pyramid scales=[] while minl>=12: scales = [m*np.power(factor, factor_count)] minl = minl*factor factor_count = 1 # first stage for j in range(len(scales)): scale=scales[j] hs=int(np.ceil(h*scale)) ws=int(np.ceil(w*scale)) im_data = imresample(img, (hs, ws)) im_data = (im_data-127.5)*0.0078125 img_x = np.expand_dims(im_data, 0) img_y = np.transpose(img_x, (0,2,1,3)) out = pnet(img_y) out0 = np.transpose(out[0], (0,2,1,3)) out1 = np.transpose(out[1], (0,2,1,3)) boxes, _ = generateBoundingBox(out1[0,:,:,1].copy(), out0[0,:,:,:].copy(), scale, threshold[0]) # inter-scale nms pick = nms(boxes.copy(), 0.5, 'Union') if boxes.size>0 and pick.size>0: boxes = boxes[pick,:] total_boxes = np.append(total_boxes, boxes, axis=0) numbox = total_boxes.shape[0] if numbox>0: pick = nms(total_boxes.copy(), 0.7, 'Union') total_boxes = total_boxes[pick,:] regw = total_boxes[:,2]-total_boxes[:,0] regh = total_boxes[:,3]-total_boxes[:,1] qq1 = total_boxes[:,0] total_boxes[:,5]*regw qq2 = total_boxes[:,1] total_boxes[:,6]*regh qq3 = total_boxes[:,2] total_boxes[:,7]*regw qq4 = total_boxes[:,3] total_boxes[:,8]*regh total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:,4]])) total_boxes = rerec(total_boxes.copy()) total_boxes[:,0:4] = np.fix(total_boxes[:,0:4]).astype(np.int32) dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h) numbox = total_boxes.shape[0] if numbox>0: # second stage tempimg = np.zeros((24,24,3,numbox)) for k in range(0,numbox): tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3)) tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:] if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0: tempimg[:,:,:,k] = imresample(tmp, (24, 24)) else: return np.empty() tempimg = (tempimg-127.5)*0.0078125 tempimg1 = np.transpose(tempimg, (3,1,0,2)) out = rnet(tempimg1) out0 = np.transpose(out[0]) out1 = np.transpose(out[1]) score = out1[1,:] ipass = np.where(score>threshold[1]) total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)]) mv = out0[:,ipass[0]] if total_boxes.shape[0]>0: pick = nms(total_boxes, 0.7, 'Union') total_boxes = total_boxes[pick,:] total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick])) total_boxes = rerec(total_boxes.copy()) numbox = total_boxes.shape[0] if numbox>0: # third stage total_boxes = np.fix(total_boxes).astype(np.int32) dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h) tempimg = np.zeros((48,48,3,numbox)) for k in range(0,numbox): tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3)) tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:] if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0: tempimg[:,:,:,k] = imresample(tmp, (48, 48)) else: return np.empty() tempimg = (tempimg-127.5)*0.0078125 tempimg1 = np.transpose(tempimg, (3,1,0,2)) out = onet(tempimg1) out0 = np.transpose(out[0]) out1 = np.transpose(out[1]) out2 = np.transpose(out[2]) score = out2[1,:] points = out1 ipass = np.where(score>threshold[2]) points = points[:,ipass[0]] total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)]) mv = out0[:,ipass[0]] w = total_boxes[:,2]-total_boxes[:,0] 1 h = total_boxes[:,3]-total_boxes[:,1] 1 points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] np.tile(total_boxes[:,0],(5, 1))-1 points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] np.tile(total_boxes[:,1],(5, 1))-1 if total_boxes.shape[0]>0: total_boxes = bbreg(total_boxes.copy(), np.transpose(mv)) pick = nms(total_boxes.copy(), 0.7, 'Min') total_boxes = total_boxes[pick,:] points = points[:,pick] return total_boxes, pointsdef bulk_detect_face(images, detection_window_size_ratio, pnet, rnet, onet, threshold, factor): # im: input image # minsize: minimum of faces' size # pnet, rnet, onet: caffemodel # threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold [0-1] all_scales = [None] * len(images) images_with_boxes = [None] * len(images) for i in range(len(images)): images_with_boxes[i] = {'total_boxes': np.empty((0, 9))} # create scale pyramid for index, img in enumerate(images): all_scales[index] = [] h = img.shape[0] w = img.shape[1] minsize = int(detection_window_size_ratio * np.minimum(w, h)) factor_count = 0 minl = np.amin([h, w]) if minsize <= 12: minsize = 12 m = 12.0 / minsize minl = minl * m while minl >= 12: all_scales[index].append(m * np.power(factor, factor_count)) minl = minl * factor factor_count = 1 # # # # # # # # # # # # # # first stage - fast proposal network (pnet) to obtain face candidates # # # # # # # # # # # # # images_obj_per_resolution = {} # TODO: use some type of rounding to number module 8 to increase probability that pyramid images will have the same resolution across input images for index, scales in enumerate(all_scales): h = images[index].shape[0] w = images[index].shape[1] for scale in scales: hs = int(np.ceil(h * scale)) ws = int(np.ceil(w * scale)) if (ws, hs) not in images_obj_per_resolution: images_obj_per_resolution[(ws, hs)] = [] im_data = imresample(images[index], (hs, ws)) im_data = (im_data - 127.5) * 0.0078125 img_y = np.transpose(im_data, (1, 0, 2)) # caffe uses different dimensions ordering images_obj_per_resolution[(ws, hs)].append({'scale': scale, 'image': img_y, 'index': index}) for resolution in images_obj_per_resolution: images_per_resolution = [i['image'] for i in images_obj_per_resolution[resolution]] outs = pnet(images_per_resolution) for index in range(len(outs[0])): scale = images_obj_per_resolution[resolution][index]['scale'] image_index = images_obj_per_resolution[resolution][index]['index'] out0 = np.transpose(outs[0][index], (1, 0, 2)) out1 = np.transpose(outs[1][index], (1, 0, 2)) boxes, _ = generateBoundingBox(out1[:, :, 1].copy(), out0[:, :, :].copy(), scale, threshold[0]) # inter-scale nms pick = nms(boxes.copy(), 0.5, 'Union') if boxes.size > 0 and pick.size > 0: boxes = boxes[pick, :] images_with_boxes[image_index]['total_boxes'] = np.append(images_with_boxes[image_index]['total_boxes'], boxes, axis=0) for index, image_obj in enumerate(images_with_boxes): numbox = image_obj['total_boxes'].shape[0] if numbox > 0: h = images[index].shape[0] w = images[index].shape[1] pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Union') image_obj['total_boxes'] = image_obj['total_boxes'][pick, :] regw = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0] regh = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1] qq1 = image_obj['total_boxes'][:, 0] image_obj['total_boxes'][:, 5] * regw qq2 = image_obj['total_boxes'][:, 1] image_obj['total_boxes'][:, 6] * regh qq3 = image_obj['total_boxes'][:, 2] image_obj['total_boxes'][:, 7] * regw qq4 = image_obj['total_boxes'][:, 3] image_obj['total_boxes'][:, 8] * regh image_obj['total_boxes'] = np.transpose(np.vstack([qq1, qq2, qq3, qq4, image_obj['total_boxes'][:, 4]])) image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy()) image_obj['total_boxes'][:, 0:4] = np.fix(image_obj['total_boxes'][:, 0:4]).astype(np.int32) dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h) numbox = image_obj['total_boxes'].shape[0] tempimg = np.zeros((24, 24, 3, numbox)) if numbox > 0: for k in range(0, numbox): tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3)) tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :] if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0: tempimg[:, :, :, k] = imresample(tmp, (24, 24)) else: return np.empty() tempimg = (tempimg - 127.5) * 0.0078125 image_obj['rnet_input'] = np.transpose(tempimg, (3, 1, 0, 2)) # # # # # # # # # # # # # # second stage - refinement of face candidates with rnet # # # # # # # # # # # # # bulk_rnet_input = np.empty((0, 24, 24, 3)) for index, image_obj in enumerate(images_with_boxes): if 'rnet_input' in image_obj: bulk_rnet_input = np.append(bulk_rnet_input, image_obj['rnet_input'], axis=0) out = rnet(bulk_rnet_input) out0 = np.transpose(out[0]) out1 = np.transpose(out[1]) score = out1[1, :] i = 0 for index, image_obj in enumerate(images_with_boxes): if 'rnet_input' not in image_obj: continue rnet_input_count = image_obj['rnet_input'].shape[0] score_per_image = score[i:i rnet_input_count] out0_per_image = out0[:, i:i rnet_input_count] ipass = np.where(score_per_image > threshold[1]) image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(), np.expand_dims(score_per_image[ipass].copy(), 1)]) mv = out0_per_image[:, ipass[0]] if image_obj['total_boxes'].shape[0] > 0: h = images[index].shape[0] w = images[index].shape[1] pick = nms(image_obj['total_boxes'], 0.7, 'Union') image_obj['total_boxes'] = image_obj['total_boxes'][pick, :] image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv[:, pick])) image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy()) numbox = image_obj['total_boxes'].shape[0] if numbox > 0: tempimg = np.zeros((48, 48, 3, numbox)) image_obj['total_boxes'] = np.fix(image_obj['total_boxes']).astype(np.int32) dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h) for k in range(0, numbox): tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3)) tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :] if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0: tempimg[:, :, :, k] = imresample(tmp, (48, 48)) else: return np.empty() tempimg = (tempimg - 127.5) * 0.0078125 image_obj['onet_input'] = np.transpose(tempimg, (3, 1, 0, 2)) i = rnet_input_count # # # # # # # # # # # # # # third stage - further refinement and facial landmarks positions with onet # # # # # # # # # # # # # bulk_onet_input = np.empty((0, 48, 48, 3)) for index, image_obj in enumerate(images_with_boxes): if 'onet_input' in image_obj: bulk_onet_input = np.append(bulk_onet_input, image_obj['onet_input'], axis=0) out = onet(bulk_onet_input) out0 = np.transpose(out[0]) out1 = np.transpose(out[1]) out2 = np.transpose(out[2]) score = out2[1, :] points = out1 i = 0 ret = [] for index, image_obj in enumerate(images_with_boxes): if 'onet_input' not in image_obj: ret.append(None) continue onet_input_count = image_obj['onet_input'].shape[0] out0_per_image = out0[:, i:i onet_input_count] score_per_image = score[i:i onet_input_count] points_per_image = points[:, i:i onet_input_count] ipass = np.where(score_per_image > threshold[2]) points_per_image = points_per_image[:, ipass[0]] image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(), np.expand_dims(score_per_image[ipass].copy(), 1)]) mv = out0_per_image[:, ipass[0]] w = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0] 1 h = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1] 1 points_per_image[0:5, :] = np.tile(w, (5, 1)) * points_per_image[0:5, :] np.tile( image_obj['total_boxes'][:, 0], (5, 1)) - 1 points_per_image[5:10, :] = np.tile(h, (5, 1)) * points_per_image[5:10, :] np.tile( image_obj['total_boxes'][:, 1], (5, 1)) - 1 if image_obj['total_boxes'].shape[0] > 0: image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv)) pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Min') image_obj['total_boxes'] = image_obj['total_boxes'][pick, :] points_per_image = points_per_image[:, pick] ret.append((image_obj['total_boxes'], points_per_image)) else: ret.append(None) i = onet_input_count return ret# function [boundingbox] = bbreg(boundingbox,reg)def bbreg(boundingbox,reg): # calibrate bounding boxes if reg.shape[1]==1: reg = np.reshape(reg, (reg.shape[2], reg.shape[3])) w = boundingbox[:,2]-boundingbox[:,0] 1 h = boundingbox[:,3]-boundingbox[:,1] 1 b1 = boundingbox[:,0] reg[:,0]*w b2 = boundingbox[:,1] reg[:,1]*h b3 = boundingbox[:,2] reg[:,2]*w b4 = boundingbox[:,3] reg[:,3]*h boundingbox[:,0:4] = np.transpose(np.vstack([b1, b2, b3, b4 ])) return boundingbox def generateBoundingBox(imap, reg, scale, t): # use heatmap to generate bounding boxes stride=2 cellsize=12 imap = np.transpose(imap) dx1 = np.transpose(reg[:,:,0]) dy1 = np.transpose(reg[:,:,1]) dx2 = np.transpose(reg[:,:,2]) dy2 = np.transpose(reg[:,:,3]) y, x = np.where(imap >= t) if y.shape[0]==1: dx1 = np.flipud(dx1) dy1 = np.flipud(dy1) dx2 = np.flipud(dx2) dy2 = np.flipud(dy2) score = imap[(y,x)] reg = np.transpose(np.vstack([ dx1[(y,x)], dy1[(y,x)], dx2[(y,x)], dy2[(y,x)] ])) if reg.size==0: reg = np.empty((0,3)) bb = np.transpose(np.vstack([y,x])) q1 = np.fix((stride*bb 1)/scale) q2 = np.fix((stride*bb cellsize-1 1)/scale) boundingbox = np.hstack([q1, q2, np.expand_dims(score,1), reg]) return boundingbox, reg # function pick = nms(boxes,threshold,type)def nms(boxes, threshold, method): if boxes.size==0: return np.empty((0,3)) x1 = boxes[:,0] y1 = boxes[:,1] x2 = boxes[:,2] y2 = boxes[:,3] s = boxes[:,4] area = (x2-x1 1) * (y2-y1 1) I = np.argsort(s) pick = np.zeros_like(s, dtype=np.int16) counter = 0 while I.size>0: i = I[-1] pick[counter] = i counter = 1 idx = I[0:-1] xx1 = np.maximum(x1[i], x1[idx]) yy1 = np.maximum(y1[i], y1[idx]) xx2 = np.minimum(x2[i], x2[idx]) yy2 = np.minimum(y2[i], y2[idx]) w = np.maximum(0.0, xx2-xx1 1) h = np.maximum(0.0, yy2-yy1 1) inter = w * h if method is 'Min': o = inter / np.minimum(area[i], area[idx]) else: o = inter / (area[i] area[idx] - inter) I = I[np.where(o<=threshold)] pick = pick[0:counter] return pick# function [dy edy dx edx y ey x ex tmpw tmph] = pad(total_boxes,w,h)def pad(total_boxes, w, h): # compute the padding coordinates (pad the bounding boxes to square) tmpw = (total_boxes[:,2]-total_boxes[:,0] 1).astype(np.int32) tmph = (total_boxes[:,3]-total_boxes[:,1] 1).astype(np.int32) numbox = total_boxes.shape[0] dx = np.ones((numbox), dtype=np.int32) dy = np.ones((numbox), dtype=np.int32) edx = tmpw.copy().astype(np.int32) edy = tmph.copy().astype(np.int32) x = total_boxes[:,0].copy().astype(np.int32) y = total_boxes[:,1].copy().astype(np.int32) ex = total_boxes[:,2].copy().astype(np.int32) ey = total_boxes[:,3].copy().astype(np.int32) tmp = np.where(ex>w) edx.flat[tmp] = np.expand_dims(-ex[tmp] w tmpw[tmp],1) ex[tmp] = w tmp = np.where(ey>h) edy.flat[tmp] = np.expand_dims(-ey[tmp] h tmph[tmp],1) ey[tmp] = h tmp = np.where(x<1) dx.flat[tmp] = np.expand_dims(2-x[tmp],1) x[tmp] = 1 tmp = np.where(y<1) dy.flat[tmp] = np.expand_dims(2-y[tmp],1) y[tmp] = 1 return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph# function [bboxA] = rerec(bboxA)def rerec(bboxA): # convert bboxA to square h = bboxA[:,3]-bboxA[:,1] w = bboxA[:,2]-bboxA[:,0] l = np.maximum(w, h) bboxA[:,0] = bboxA[:,0] w*0.5-l*0.5 bboxA[:,1] = bboxA[:,1] h*0.5-l*0.5 bboxA[:,2:4] = bboxA[:,0:2] np.transpose(np.tile(l,(2,1))) return bboxAdef imresample(img, sz): im_data = cv2.resize(img, (sz[1], sz[0]), interpolation=cv2.INTER_AREA) #@UndefinedVariable return im_data
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