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585 lines
22 KiB
Python
585 lines
22 KiB
Python
# Copyright (c) 2020, Baris Gecer. All rights reserved.
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#
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# This work is made available under the CC BY-NC-SA 4.0.
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# To view a copy of this license, see LICENSE
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow as tf
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import cv2
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import os
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import inspect
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class Face_Detector(object):
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def __init__(self, gpuid = -1):
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self.minsize = 40 # minimum size of face
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self.threshold = [0.6, 0.7, 0.7] # three steps's threshold
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self.factor = 0.709 # scale factor
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current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
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with tf.device('/'+('cpu' if gpuid<0 else 'gpu')+':'+('0' if gpuid<0 else str(gpuid))):
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with tf.Graph().as_default():
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sess = tf.Session(config= tf.ConfigProto(device_count = {'GPU': 0 if gpuid<0 else 1}))
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with sess.as_default():
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self.pnet, self.rnet, self.onet = create_detector(sess, '{}/mtcnn'.format(current_dir))
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print('MTCNN loaded')
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def face_detection(self,img):
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bounding_boxes, points = detect_face(img, self.minsize,
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self.pnet, self.rnet, self.onet, self.threshold,
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self.factor)
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return bounding_boxes, points.reshape([2,-1]).T
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def layer(op):
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'''Decorator for composable network layers.'''
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def layer_decorated(self, *args, **kwargs):
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# Automatically set a name if not provided.
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name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
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# Figure out the layer inputs.
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if len(self.terminals) == 0:
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raise RuntimeError('No input variables found for layer %s.' % name)
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elif len(self.terminals) == 1:
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layer_input = self.terminals[0]
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else:
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layer_input = list(self.terminals)
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# Perform the operation and get the output.
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layer_output = op(self, layer_input, *args, **kwargs)
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# Add to layer LUT.
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self.layers[name] = layer_output
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# This output is now the input for the next layer.
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self.feed(layer_output)
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# Return self for chained calls.
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return self
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return layer_decorated
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class Network(object):
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def __init__(self, inputs, trainable=True):
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# The input nodes for this network
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self.inputs = inputs
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# The current list of terminal nodes
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self.terminals = []
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# Mapping from layer names to layers
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self.layers = dict(inputs)
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# If true, the resulting variables are set as trainable
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self.trainable = trainable
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self.setup()
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def setup(self):
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'''Construct the network. '''
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raise NotImplementedError('Must be implemented by the subclass.')
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def load(self, data_path, session, ignore_missing=False):
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'''Load network weights.
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data_path: The path to the numpy-serialized network weights
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session: The current TensorFlow session
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ignore_missing: If true, serialized weights for missing layers are ignored.
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'''
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data_dict = np.load(data_path, encoding='latin1').item() #pylint: disable=no-member
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for op_name in data_dict:
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with tf.variable_scope(op_name, reuse=True):
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for param_name, data in data_dict[op_name].items():
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try:
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var = tf.get_variable(param_name)
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session.run(var.assign(data))
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except ValueError:
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if not ignore_missing:
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raise
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def feed(self, *args):
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'''Set the input(s) for the next operation by replacing the terminal nodes.
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The arguments can be either layer names or the actual layers.
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'''
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assert len(args) != 0
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self.terminals = []
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for fed_layer in args:
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if isinstance(fed_layer, str):
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try:
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fed_layer = self.layers[fed_layer]
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except KeyError:
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raise KeyError('Unknown layer name fed: %s' % fed_layer)
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self.terminals.append(fed_layer)
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return self
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def get_output(self):
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'''Returns the current network output.'''
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return self.terminals[-1]
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def get_unique_name(self, prefix):
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'''Returns an index-suffixed unique name for the given prefix.
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This is used for auto-generating layer names based on the type-prefix.
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'''
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ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
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return '%s_%d' % (prefix, ident)
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def make_var(self, name, shape):
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'''Creates a new TensorFlow variable.'''
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return tf.get_variable(name, shape, trainable=self.trainable)
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def validate_padding(self, padding):
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'''Verifies that the padding is one of the supported ones.'''
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assert padding in ('SAME', 'VALID')
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@layer
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def conv(self,
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inp,
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k_h,
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k_w,
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c_o,
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s_h,
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s_w,
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name,
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relu=True,
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padding='SAME',
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group=1,
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biased=True):
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# Verify that the padding is acceptable
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self.validate_padding(padding)
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# Get the number of channels in the input
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c_i = inp.get_shape()[-1]
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# Verify that the grouping parameter is valid
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assert c_i % group == 0
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assert c_o % group == 0
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# Convolution for a given input and kernel
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convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
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with tf.variable_scope(name) as scope:
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kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
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# This is the common-case. Convolve the input without any further complications.
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output = convolve(inp, kernel)
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# Add the biases
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if biased:
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biases = self.make_var('biases', [c_o])
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output = tf.nn.bias_add(output, biases)
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if relu:
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# ReLU non-linearity
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output = tf.nn.relu(output, name=scope.name)
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return output
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@layer
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def prelu(self, inp, name):
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with tf.variable_scope(name):
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i = inp.get_shape().as_list()
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alpha = self.make_var('alpha', shape=(i[-1]))
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output = tf.nn.relu(inp) + tf.multiply(alpha, -tf.nn.relu(-inp))
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return output
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@layer
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def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):
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self.validate_padding(padding)
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return tf.nn.max_pool(inp,
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ksize=[1, k_h, k_w, 1],
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strides=[1, s_h, s_w, 1],
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padding=padding,
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name=name)
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@layer
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def fc(self, inp, num_out, name, relu=True):
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with tf.variable_scope(name):
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input_shape = inp.get_shape()
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if input_shape.ndims == 4:
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# The input is spatial. Vectorize it first.
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dim = 1
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for d in input_shape[1:].as_list():
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dim *= d
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feed_in = tf.reshape(inp, [-1, dim])
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else:
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feed_in, dim = (inp, input_shape[-1].value)
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weights = self.make_var('weights', shape=[dim, num_out])
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biases = self.make_var('biases', [num_out])
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op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
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fc = op(feed_in, weights, biases, name=name)
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return fc
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"""
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Multi dimensional softmax,
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refer to https://github.com/tensorflow/tensorflow/issues/210
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compute softmax along the dimension of target
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the native softmax only supports batch_size x dimension
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"""
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@layer
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def softmax(self, target, axis, name=None):
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max_axis = tf.reduce_max(target, axis, keepdims=True)
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target_exp = tf.exp(target-max_axis)
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normalize = tf.reduce_sum(target_exp, axis, keepdims=True)
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softmax = tf.div(target_exp, normalize, name)
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return softmax
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class PNet(Network):
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def setup(self):
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(self.feed('data') #pylint: disable=no-value-for-parameter, no-member
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.conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1')
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.prelu(name='PReLU1')
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.max_pool(2, 2, 2, 2, name='pool1')
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.conv(3, 3, 16, 1, 1, padding='VALID', relu=False, name='conv2')
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.prelu(name='PReLU2')
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.conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv3')
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.prelu(name='PReLU3')
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.conv(1, 1, 2, 1, 1, relu=False, name='conv4-1')
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.softmax(3,name='prob1'))
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(self.feed('PReLU3') #pylint: disable=no-value-for-parameter
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.conv(1, 1, 4, 1, 1, relu=False, name='conv4-2'))
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class RNet(Network):
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def setup(self):
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(self.feed('data') #pylint: disable=no-value-for-parameter, no-member
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.conv(3, 3, 28, 1, 1, padding='VALID', relu=False, name='conv1')
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.prelu(name='prelu1')
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.max_pool(3, 3, 2, 2, name='pool1')
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.conv(3, 3, 48, 1, 1, padding='VALID', relu=False, name='conv2')
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.prelu(name='prelu2')
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.max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
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.conv(2, 2, 64, 1, 1, padding='VALID', relu=False, name='conv3')
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.prelu(name='prelu3')
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.fc(128, relu=False, name='conv4')
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.prelu(name='prelu4')
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.fc(2, relu=False, name='conv5-1')
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.softmax(1,name='prob1'))
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(self.feed('prelu4') #pylint: disable=no-value-for-parameter
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.fc(4, relu=False, name='conv5-2'))
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class ONet(Network):
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def setup(self):
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(self.feed('data') #pylint: disable=no-value-for-parameter, no-member
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.conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv1')
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.prelu(name='prelu1')
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.max_pool(3, 3, 2, 2, name='pool1')
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.conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv2')
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.prelu(name='prelu2')
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.max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
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.conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv3')
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.prelu(name='prelu3')
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.max_pool(2, 2, 2, 2, name='pool3')
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.conv(2, 2, 128, 1, 1, padding='VALID', relu=False, name='conv4')
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.prelu(name='prelu4')
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.fc(256, relu=False, name='conv5')
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.prelu(name='prelu5')
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.fc(2, relu=False, name='conv6-1')
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.softmax(1, name='prob1'))
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(self.feed('prelu5') #pylint: disable=no-value-for-parameter
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.fc(4, relu=False, name='conv6-2'))
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(self.feed('prelu5') #pylint: disable=no-value-for-parameter
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.fc(10, relu=False, name='conv6-3'))
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def create_detector(sess, model_path):
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with tf.variable_scope('pnet'):
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data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
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pnet = PNet({'data':data})
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pnet.load(os.path.join(model_path, 'cas1.npy'), sess)
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with tf.variable_scope('rnet'):
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data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
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rnet = RNet({'data':data})
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rnet.load(os.path.join(model_path, 'cas2.npy'), sess)
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with tf.variable_scope('onet'):
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data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
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onet = ONet({'data':data})
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onet.load(os.path.join(model_path, 'cas3.npy'), sess)
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pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})
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rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})
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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})
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return pnet_fun, rnet_fun, onet_fun
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def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
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# im: input image
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# minsize: minimum of faces' size
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# pnet, rnet, onet: model
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# threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold
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# factor: resize img to generate pyramid
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factor_count=0
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total_boxes=np.empty((0,9))
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points=[]
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h=img.shape[0]
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w=img.shape[1]
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minl=np.amin([h, w])
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m=12.0/minsize
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minl=minl*m
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# creat scale pyramid
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scales=[]
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while minl>=12:
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scales += [m*np.power(factor, factor_count)]
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minl = minl*factor
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factor_count += 1
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# first stage
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for j in range(len(scales)):
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scale=scales[j]
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hs=int(np.ceil(h*scale))
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ws=int(np.ceil(w*scale))
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im_data = imresample(img, (hs, ws))
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im_data = (im_data-127.5)*0.0078125
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img_x = np.expand_dims(im_data, 0)
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img_y = np.transpose(img_x, (0,2,1,3))
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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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boxes = boxes[pick,:]
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total_boxes = np.append(total_boxes, boxes, axis=0)
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numbox = total_boxes.shape[0]
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if numbox>0:
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pick = nms(total_boxes.copy(), 0.7, 'Union')
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total_boxes = total_boxes[pick,:]
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regw = total_boxes[:,2]-total_boxes[:,0]
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regh = total_boxes[:,3]-total_boxes[:,1]
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qq1 = total_boxes[:,0]+total_boxes[:,5]*regw
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qq2 = total_boxes[:,1]+total_boxes[:,6]*regh
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qq3 = total_boxes[:,2]+total_boxes[:,7]*regw
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qq4 = total_boxes[:,3]+total_boxes[:,8]*regh
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total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:,4]]))
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total_boxes = rerec(total_boxes.copy())
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total_boxes[:,0:4] = np.fix(total_boxes[:,0:4]).astype(np.int32)
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dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
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numbox = total_boxes.shape[0]
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if numbox>0:
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# second stage
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tempimg = np.zeros((24,24,3,numbox))
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for k in range(0,numbox):
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tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
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tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
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if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
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tempimg[:,:,:,k] = imresample(tmp, (24, 24))
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else:
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return np.empty()
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tempimg = (tempimg-127.5)*0.0078125
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tempimg1 = np.transpose(tempimg, (3,1,0,2))
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out = rnet(tempimg1)
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out0 = np.transpose(out[0])
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out1 = np.transpose(out[1])
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score = out1[1,:]
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ipass = np.where(score>threshold[1])
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total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
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mv = out0[:,ipass[0]]
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if total_boxes.shape[0]>0:
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pick = nms(total_boxes, 0.7, 'Union')
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total_boxes = total_boxes[pick,:]
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total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick]))
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total_boxes = rerec(total_boxes.copy())
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numbox = total_boxes.shape[0]
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if numbox>0:
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# third stage
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total_boxes = np.fix(total_boxes).astype(np.int32)
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dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
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tempimg = np.zeros((48,48,3,numbox))
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for k in range(0,numbox):
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tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
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tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
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if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
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tempimg[:,:,:,k] = imresample(tmp, (48, 48))
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else:
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return np.empty()
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tempimg = (tempimg-127.5)*0.0078125
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tempimg1 = np.transpose(tempimg, (3,1,0,2))
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out = onet(tempimg1)
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out0 = np.transpose(out[0])
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out1 = np.transpose(out[1])
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out2 = np.transpose(out[2])
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score = out2[1,:]
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points = out1
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ipass = np.where(score>threshold[2])
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points = points[:,ipass[0]]
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total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
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mv = out0[:,ipass[0]]
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w = total_boxes[:,2]-total_boxes[:,0]+1
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h = total_boxes[:,3]-total_boxes[:,1]+1
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points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] + np.tile(total_boxes[:,0],(5, 1))-1
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points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] + np.tile(total_boxes[:,1],(5, 1))-1
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if total_boxes.shape[0]>0:
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total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))
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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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def box_regression(img, onet, total_boxes, threshold):
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# im: input image
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# onet: model
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# total_boxes: [x1 y1 x2 y2 score, 5]
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# threshold: 0.7
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points=[]
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h=img.shape[0]
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w=img.shape[1]
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|
|
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numbox = total_boxes.shape[0]
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if numbox>0:
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total_boxes = rerec(total_boxes)
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total_boxes = np.fix(total_boxes).astype(np.int32)
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dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
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tempimg = np.zeros((48,48,3,numbox))
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for k in range(0,numbox):
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tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
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tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
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if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
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tempimg[:,:,:,k] = imresample(tmp, (48, 48))
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else:
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return np.empty()
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tempimg = (tempimg-127.5)*0.0078125
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tempimg1 = np.transpose(tempimg, (3,1,0,2))
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out = onet(tempimg1)
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|
out0 = np.transpose(out[0])
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|
out1 = np.transpose(out[1])
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out2 = np.transpose(out[2])
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|
score = out2[1,:]
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|
points = out1
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|
ipass = np.where(score>threshold)
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|
points = points[:,ipass[0]]
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|
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
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|
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
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|
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, points
|
|
|
|
|
|
def bbreg(boundingbox,reg):
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|
# 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
|
|
|
|
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
|
|
|
|
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[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],0)
|
|
ex[tmp] = w
|
|
|
|
tmp = np.where(ey>h)
|
|
edy[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],0)
|
|
ey[tmp] = h
|
|
|
|
tmp = np.where(x<1)
|
|
dx[tmp] = np.expand_dims(2-x[tmp],0)
|
|
x[tmp] = 1
|
|
|
|
tmp = np.where(y<1)
|
|
dy[tmp] = np.expand_dims(2-y[tmp],0)
|
|
y[tmp] = 1
|
|
|
|
return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph
|
|
|
|
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 bboxA
|
|
|
|
def imresample(img, sz):
|
|
im_data = cv2.resize(img, (sz[1], sz[0]), interpolation=cv2.INTER_AREA)
|
|
return im_data
|