13 |
12 |
13 |
38 | 
39 |
61 |
62 | Copyright © 2021 Robin Reni. All rights reserved
63 |
64 |
65 |
66 |
67 |
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/align/__pycache__/detect_face.cpython-36.pyc:
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/align/align_dataset_mtcnn.py:
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1 | """Performs face alignment and stores face thumbnails in the output directory."""
2 | # MIT License
3 | #
4 | # Copyright (c) 2016 David Sandberg
5 | #
6 | # Permission is hereby granted, free of charge, to any person obtaining a copy
7 | # of this software and associated documentation files (the "Software"), to deal
8 | # in the Software without restriction, including without limitation the rights
9 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
10 | # copies of the Software, and to permit persons to whom the Software is
11 | # furnished to do so, subject to the following conditions:
12 | #
13 | # The above copyright notice and this permission notice shall be included in all
14 | # copies or substantial portions of the Software.
15 | #
16 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 | # SOFTWARE.
23 |
24 | from __future__ import absolute_import
25 | from __future__ import division
26 | from __future__ import print_function
27 |
28 | from scipy import misc
29 | import sys
30 | import os
31 | import argparse
32 | import tensorflow as tf
33 | import numpy as np
34 | import facenet
35 | import align.detect_face
36 | import random
37 | from time import sleep
38 |
39 | def main(args):
40 | sleep(random.random())
41 | output_dir = os.path.expanduser(args.output_dir)
42 | if not os.path.exists(output_dir):
43 | os.makedirs(output_dir)
44 | # Store some git revision info in a text file in the log directory
45 | src_path,_ = os.path.split(os.path.realpath(__file__))
46 | facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
47 | dataset = facenet.get_dataset(args.input_dir)
48 |
49 | print('Creating networks and loading parameters')
50 |
51 | with tf.Graph().as_default():
52 | gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
53 | sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
54 | with sess.as_default():
55 | pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
56 |
57 | minsize = 20 # minimum size of face
58 | threshold = [ 0.6, 0.7, 0.7 ] # three steps's threshold
59 | factor = 0.709 # scale factor
60 |
61 | # Add a random key to the filename to allow alignment using multiple processes
62 | random_key = np.random.randint(0, high=99999)
63 | bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
64 |
65 | with open(bounding_boxes_filename, "w") as text_file:
66 | nrof_images_total = 0
67 | nrof_successfully_aligned = 0
68 | if args.random_order:
69 | random.shuffle(dataset)
70 | for cls in dataset:
71 | output_class_dir = os.path.join(output_dir, cls.name)
72 | if not os.path.exists(output_class_dir):
73 | os.makedirs(output_class_dir)
74 | if args.random_order:
75 | random.shuffle(cls.image_paths)
76 | for image_path in cls.image_paths:
77 | nrof_images_total += 1
78 | filename = os.path.splitext(os.path.split(image_path)[1])[0]
79 | output_filename = os.path.join(output_class_dir, filename+'.png')
80 | print(image_path)
81 | if not os.path.exists(output_filename):
82 | try:
83 | img = misc.imread(image_path)
84 | except (IOError, ValueError, IndexError) as e:
85 | errorMessage = '{}: {}'.format(image_path, e)
86 | print(errorMessage)
87 | else:
88 | if img.ndim<2:
89 | print('Unable to align "%s"' % image_path)
90 | text_file.write('%s\n' % (output_filename))
91 | continue
92 | if img.ndim == 2:
93 | img = facenet.to_rgb(img)
94 | img = img[:,:,0:3]
95 |
96 | bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
97 | nrof_faces = bounding_boxes.shape[0]
98 | if nrof_faces>0:
99 | det = bounding_boxes[:,0:4]
100 | det_arr = []
101 | img_size = np.asarray(img.shape)[0:2]
102 | if nrof_faces>1:
103 | if args.detect_multiple_faces:
104 | for i in range(nrof_faces):
105 | det_arr.append(np.squeeze(det[i]))
106 | else:
107 | bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1])
108 | img_center = img_size / 2
109 | offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])
110 | offset_dist_squared = np.sum(np.power(offsets,2.0),0)
111 | index = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering
112 | det_arr.append(det[index,:])
113 | else:
114 | det_arr.append(np.squeeze(det))
115 |
116 | for i, det in enumerate(det_arr):
117 | det = np.squeeze(det)
118 | bb = np.zeros(4, dtype=np.int32)
119 | bb[0] = np.maximum(det[0]-args.margin/2, 0)
120 | bb[1] = np.maximum(det[1]-args.margin/2, 0)
121 | bb[2] = np.minimum(det[2]+args.margin/2, img_size[1])
122 | bb[3] = np.minimum(det[3]+args.margin/2, img_size[0])
123 | cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
124 | scaled = misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
125 | nrof_successfully_aligned += 1
126 | filename_base, file_extension = os.path.splitext(output_filename)
127 | if args.detect_multiple_faces:
128 | output_filename_n = "{}_{}{}".format(filename_base, i, file_extension)
129 | else:
130 | output_filename_n = "{}{}".format(filename_base, file_extension)
131 | misc.imsave(output_filename_n, scaled)
132 | text_file.write('%s %d %d %d %d\n' % (output_filename_n, bb[0], bb[1], bb[2], bb[3]))
133 | else:
134 | print('Unable to align "%s"' % image_path)
135 | text_file.write('%s\n' % (output_filename))
136 |
137 | print('Total number of images: %d' % nrof_images_total)
138 | print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
139 |
140 |
141 | def parse_arguments(argv):
142 | parser = argparse.ArgumentParser()
143 |
144 | parser.add_argument('input_dir', type=str, help='Directory with unaligned images.')
145 | parser.add_argument('output_dir', type=str, help='Directory with aligned face thumbnails.')
146 | parser.add_argument('--image_size', type=int,
147 | help='Image size (height, width) in pixels.', default=182)
148 | parser.add_argument('--margin', type=int,
149 | help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
150 | parser.add_argument('--random_order',
151 | help='Shuffles the order of images to enable alignment using multiple processes.', action='store_true')
152 | parser.add_argument('--gpu_memory_fraction', type=float,
153 | help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
154 | parser.add_argument('--detect_multiple_faces', type=bool,
155 | help='Detect and align multiple faces per image.', default=False)
156 | return parser.parse_args(argv)
157 |
158 | if __name__ == '__main__':
159 | main(parse_arguments(sys.argv[1:]))
160 |
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/align/det1.npy:
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/align/det2.npy:
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/align/det3.npy:
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https://raw.githubusercontent.com/robinreni96/Automatic-Face-Detection-Annotation-and-Preprocessing/3a8e62c9a58de44043a7968e8d658d5d5d566f3e/align/det3.npy
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/align/detect_face.py:
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1 | """ Tensorflow implementation of the face detection / alignment algorithm found at
2 | https://github.com/kpzhang93/MTCNN_face_detection_alignment
3 | """
4 | # MIT License
5 | #
6 | # Copyright (c) 2016 David Sandberg
7 | #
8 | # Permission is hereby granted, free of charge, to any person obtaining a copy
9 | # of this software and associated documentation files (the "Software"), to deal
10 | # in the Software without restriction, including without limitation the rights
11 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
12 | # copies of the Software, and to permit persons to whom the Software is
13 | # furnished to do so, subject to the following conditions:
14 | #
15 | # The above copyright notice and this permission notice shall be included in all
16 | # copies or substantial portions of the Software.
17 | #
18 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
19 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
20 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
21 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
22 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
23 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
24 | # SOFTWARE.
25 |
26 | from __future__ import absolute_import
27 | from __future__ import division
28 | from __future__ import print_function
29 | from six import string_types, iteritems
30 |
31 | import numpy as np
32 | import tensorflow as tf
33 | #from math import floor
34 | import cv2
35 | import os
36 |
37 | def layer(op):
38 | """Decorator for composable network layers."""
39 |
40 | def layer_decorated(self, *args, **kwargs):
41 | # Automatically set a name if not provided.
42 | name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
43 | # Figure out the layer inputs.
44 | if len(self.terminals) == 0:
45 | raise RuntimeError('No input variables found for layer %s.' % name)
46 | elif len(self.terminals) == 1:
47 | layer_input = self.terminals[0]
48 | else:
49 | layer_input = list(self.terminals)
50 | # Perform the operation and get the output.
51 | layer_output = op(self, layer_input, *args, **kwargs)
52 | # Add to layer LUT.
53 | self.layers[name] = layer_output
54 | # This output is now the input for the next layer.
55 | self.feed(layer_output)
56 | # Return self for chained calls.
57 | return self
58 |
59 | return layer_decorated
60 |
61 | class Network(object):
62 |
63 | def __init__(self, inputs, trainable=True):
64 | # The input nodes for this network
65 | self.inputs = inputs
66 | # The current list of terminal nodes
67 | self.terminals = []
68 | # Mapping from layer names to layers
69 | self.layers = dict(inputs)
70 | # If true, the resulting variables are set as trainable
71 | self.trainable = trainable
72 |
73 | self.setup()
74 |
75 | def setup(self):
76 | """Construct the network. """
77 | raise NotImplementedError('Must be implemented by the subclass.')
78 |
79 | def load(self, data_path, session, ignore_missing=False):
80 | """Load network weights.
81 | data_path: The path to the numpy-serialized network weights
82 | session: The current TensorFlow session
83 | ignore_missing: If true, serialized weights for missing layers are ignored.
84 | """
85 | data_dict = np.load(data_path, encoding='latin1').item() #pylint: disable=no-member
86 |
87 | for op_name in data_dict:
88 | with tf.variable_scope(op_name, reuse=True):
89 | for param_name, data in iteritems(data_dict[op_name]):
90 | try:
91 | var = tf.get_variable(param_name)
92 | session.run(var.assign(data))
93 | except ValueError:
94 | if not ignore_missing:
95 | raise
96 |
97 | def feed(self, *args):
98 | """Set the input(s) for the next operation by replacing the terminal nodes.
99 | The arguments can be either layer names or the actual layers.
100 | """
101 | assert len(args) != 0
102 | self.terminals = []
103 | for fed_layer in args:
104 | if isinstance(fed_layer, string_types):
105 | try:
106 | fed_layer = self.layers[fed_layer]
107 | except KeyError:
108 | raise KeyError('Unknown layer name fed: %s' % fed_layer)
109 | self.terminals.append(fed_layer)
110 | return self
111 |
112 | def get_output(self):
113 | """Returns the current network output."""
114 | return self.terminals[-1]
115 |
116 | def get_unique_name(self, prefix):
117 | """Returns an index-suffixed unique name for the given prefix.
118 | This is used for auto-generating layer names based on the type-prefix.
119 | """
120 | ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
121 | return '%s_%d' % (prefix, ident)
122 |
123 | def make_var(self, name, shape):
124 | """Creates a new TensorFlow variable."""
125 | return tf.get_variable(name, shape, trainable=self.trainable)
126 |
127 | def validate_padding(self, padding):
128 | """Verifies that the padding is one of the supported ones."""
129 | assert padding in ('SAME', 'VALID')
130 |
131 | @layer
132 | def conv(self,
133 | inp,
134 | k_h,
135 | k_w,
136 | c_o,
137 | s_h,
138 | s_w,
139 | name,
140 | relu=True,
141 | padding='SAME',
142 | group=1,
143 | biased=True):
144 | # Verify that the padding is acceptable
145 | self.validate_padding(padding)
146 | # Get the number of channels in the input
147 | c_i = int(inp.get_shape()[-1])
148 | # Verify that the grouping parameter is valid
149 | assert c_i % group == 0
150 | assert c_o % group == 0
151 | # Convolution for a given input and kernel
152 | convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
153 | with tf.variable_scope(name) as scope:
154 | kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
155 | # This is the common-case. Convolve the input without any further complications.
156 | output = convolve(inp, kernel)
157 | # Add the biases
158 | if biased:
159 | biases = self.make_var('biases', [c_o])
160 | output = tf.nn.bias_add(output, biases)
161 | if relu:
162 | # ReLU non-linearity
163 | output = tf.nn.relu(output, name=scope.name)
164 | return output
165 |
166 | @layer
167 | def prelu(self, inp, name):
168 | with tf.variable_scope(name):
169 | i = int(inp.get_shape()[-1])
170 | alpha = self.make_var('alpha', shape=(i,))
171 | output = tf.nn.relu(inp) + tf.multiply(alpha, -tf.nn.relu(-inp))
172 | return output
173 |
174 | @layer
175 | def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):
176 | self.validate_padding(padding)
177 | return tf.nn.max_pool(inp,
178 | ksize=[1, k_h, k_w, 1],
179 | strides=[1, s_h, s_w, 1],
180 | padding=padding,
181 | name=name)
182 |
183 | @layer
184 | def fc(self, inp, num_out, name, relu=True):
185 | with tf.variable_scope(name):
186 | input_shape = inp.get_shape()
187 | if input_shape.ndims == 4:
188 | # The input is spatial. Vectorize it first.
189 | dim = 1
190 | for d in input_shape[1:].as_list():
191 | dim *= int(d)
192 | feed_in = tf.reshape(inp, [-1, dim])
193 | else:
194 | feed_in, dim = (inp, input_shape[-1].value)
195 | weights = self.make_var('weights', shape=[dim, num_out])
196 | biases = self.make_var('biases', [num_out])
197 | op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
198 | fc = op(feed_in, weights, biases, name=name)
199 | return fc
200 |
201 |
202 | """
203 | Multi dimensional softmax,
204 | refer to https://github.com/tensorflow/tensorflow/issues/210
205 | compute softmax along the dimension of target
206 | the native softmax only supports batch_size x dimension
207 | """
208 | @layer
209 | def softmax(self, target, axis, name=None):
210 | max_axis = tf.reduce_max(target, axis, keepdims=True)
211 | target_exp = tf.exp(target-max_axis)
212 | normalize = tf.reduce_sum(target_exp, axis, keepdims=True)
213 | softmax = tf.div(target_exp, normalize, name)
214 | return softmax
215 |
216 | class PNet(Network):
217 | def setup(self):
218 | (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
219 | .conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1')
220 | .prelu(name='PReLU1')
221 | .max_pool(2, 2, 2, 2, name='pool1')
222 | .conv(3, 3, 16, 1, 1, padding='VALID', relu=False, name='conv2')
223 | .prelu(name='PReLU2')
224 | .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv3')
225 | .prelu(name='PReLU3')
226 | .conv(1, 1, 2, 1, 1, relu=False, name='conv4-1')
227 | .softmax(3,name='prob1'))
228 |
229 | (self.feed('PReLU3') #pylint: disable=no-value-for-parameter
230 | .conv(1, 1, 4, 1, 1, relu=False, name='conv4-2'))
231 |
232 | class RNet(Network):
233 | def setup(self):
234 | (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
235 | .conv(3, 3, 28, 1, 1, padding='VALID', relu=False, name='conv1')
236 | .prelu(name='prelu1')
237 | .max_pool(3, 3, 2, 2, name='pool1')
238 | .conv(3, 3, 48, 1, 1, padding='VALID', relu=False, name='conv2')
239 | .prelu(name='prelu2')
240 | .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
241 | .conv(2, 2, 64, 1, 1, padding='VALID', relu=False, name='conv3')
242 | .prelu(name='prelu3')
243 | .fc(128, relu=False, name='conv4')
244 | .prelu(name='prelu4')
245 | .fc(2, relu=False, name='conv5-1')
246 | .softmax(1,name='prob1'))
247 |
248 | (self.feed('prelu4') #pylint: disable=no-value-for-parameter
249 | .fc(4, relu=False, name='conv5-2'))
250 |
251 | class ONet(Network):
252 | def setup(self):
253 | (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
254 | .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv1')
255 | .prelu(name='prelu1')
256 | .max_pool(3, 3, 2, 2, name='pool1')
257 | .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv2')
258 | .prelu(name='prelu2')
259 | .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
260 | .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv3')
261 | .prelu(name='prelu3')
262 | .max_pool(2, 2, 2, 2, name='pool3')
263 | .conv(2, 2, 128, 1, 1, padding='VALID', relu=False, name='conv4')
264 | .prelu(name='prelu4')
265 | .fc(256, relu=False, name='conv5')
266 | .prelu(name='prelu5')
267 | .fc(2, relu=False, name='conv6-1')
268 | .softmax(1, name='prob1'))
269 |
270 | (self.feed('prelu5') #pylint: disable=no-value-for-parameter
271 | .fc(4, relu=False, name='conv6-2'))
272 |
273 | (self.feed('prelu5') #pylint: disable=no-value-for-parameter
274 | .fc(10, relu=False, name='conv6-3'))
275 |
276 | def create_mtcnn(sess, model_path):
277 | if not model_path:
278 | model_path,_ = os.path.split(os.path.realpath(__file__))
279 |
280 | with tf.variable_scope('pnet'):
281 | data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
282 | pnet = PNet({'data':data})
283 | pnet.load(os.path.join(model_path, 'det1.npy'), sess)
284 | with tf.variable_scope('rnet'):
285 | data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
286 | rnet = RNet({'data':data})
287 | rnet.load(os.path.join(model_path, 'det2.npy'), sess)
288 | with tf.variable_scope('onet'):
289 | data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
290 | onet = ONet({'data':data})
291 | onet.load(os.path.join(model_path, 'det3.npy'), sess)
292 |
293 | pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})
294 | rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})
295 | 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})
296 | return pnet_fun, rnet_fun, onet_fun
297 |
298 | def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
299 | """Detects faces in an image, and returns bounding boxes and points for them.
300 | img: input image
301 | minsize: minimum faces' size
302 | pnet, rnet, onet: caffemodel
303 | threshold: threshold=[th1, th2, th3], th1-3 are three steps's threshold
304 | factor: the factor used to create a scaling pyramid of face sizes to detect in the image.
305 | """
306 | factor_count=0
307 | total_boxes=np.empty((0,9))
308 | points=np.empty(0)
309 | h=img.shape[0]
310 | w=img.shape[1]
311 | minl=np.amin([h, w])
312 | m=12.0/minsize
313 | minl=minl*m
314 | # create scale pyramid
315 | scales=[]
316 | while minl>=12:
317 | scales += [m*np.power(factor, factor_count)]
318 | minl = minl*factor
319 | factor_count += 1
320 |
321 | # first stage
322 | for scale in scales:
323 | hs=int(np.ceil(h*scale))
324 | ws=int(np.ceil(w*scale))
325 | im_data = imresample(img, (hs, ws))
326 | im_data = (im_data-127.5)*0.0078125
327 | img_x = np.expand_dims(im_data, 0)
328 | img_y = np.transpose(img_x, (0,2,1,3))
329 | out = pnet(img_y)
330 | out0 = np.transpose(out[0], (0,2,1,3))
331 | out1 = np.transpose(out[1], (0,2,1,3))
332 |
333 | boxes, _ = generateBoundingBox(out1[0,:,:,1].copy(), out0[0,:,:,:].copy(), scale, threshold[0])
334 |
335 | # inter-scale nms
336 | pick = nms(boxes.copy(), 0.5, 'Union')
337 | if boxes.size>0 and pick.size>0:
338 | boxes = boxes[pick,:]
339 | total_boxes = np.append(total_boxes, boxes, axis=0)
340 |
341 | numbox = total_boxes.shape[0]
342 | if numbox>0:
343 | pick = nms(total_boxes.copy(), 0.7, 'Union')
344 | total_boxes = total_boxes[pick,:]
345 | regw = total_boxes[:,2]-total_boxes[:,0]
346 | regh = total_boxes[:,3]-total_boxes[:,1]
347 | qq1 = total_boxes[:,0]+total_boxes[:,5]*regw
348 | qq2 = total_boxes[:,1]+total_boxes[:,6]*regh
349 | qq3 = total_boxes[:,2]+total_boxes[:,7]*regw
350 | qq4 = total_boxes[:,3]+total_boxes[:,8]*regh
351 | total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:,4]]))
352 | total_boxes = rerec(total_boxes.copy())
353 | total_boxes[:,0:4] = np.fix(total_boxes[:,0:4]).astype(np.int32)
354 | dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
355 |
356 | numbox = total_boxes.shape[0]
357 | if numbox>0:
358 | # second stage
359 | tempimg = np.zeros((24,24,3,numbox))
360 | for k in range(0,numbox):
361 | tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
362 | tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
363 | if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
364 | tempimg[:,:,:,k] = imresample(tmp, (24, 24))
365 | else:
366 | return np.empty()
367 | tempimg = (tempimg-127.5)*0.0078125
368 | tempimg1 = np.transpose(tempimg, (3,1,0,2))
369 | out = rnet(tempimg1)
370 | out0 = np.transpose(out[0])
371 | out1 = np.transpose(out[1])
372 | score = out1[1,:]
373 | ipass = np.where(score>threshold[1])
374 | total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
375 | mv = out0[:,ipass[0]]
376 | if total_boxes.shape[0]>0:
377 | pick = nms(total_boxes, 0.7, 'Union')
378 | total_boxes = total_boxes[pick,:]
379 | total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick]))
380 | total_boxes = rerec(total_boxes.copy())
381 |
382 | numbox = total_boxes.shape[0]
383 | if numbox>0:
384 | # third stage
385 | total_boxes = np.fix(total_boxes).astype(np.int32)
386 | dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
387 | tempimg = np.zeros((48,48,3,numbox))
388 | for k in range(0,numbox):
389 | tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
390 | tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
391 | if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
392 | tempimg[:,:,:,k] = imresample(tmp, (48, 48))
393 | else:
394 | return np.empty()
395 | tempimg = (tempimg-127.5)*0.0078125
396 | tempimg1 = np.transpose(tempimg, (3,1,0,2))
397 | out = onet(tempimg1)
398 | out0 = np.transpose(out[0])
399 | out1 = np.transpose(out[1])
400 | out2 = np.transpose(out[2])
401 | score = out2[1,:]
402 | points = out1
403 | ipass = np.where(score>threshold[2])
404 | points = points[:,ipass[0]]
405 | total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
406 | mv = out0[:,ipass[0]]
407 |
408 | w = total_boxes[:,2]-total_boxes[:,0]+1
409 | h = total_boxes[:,3]-total_boxes[:,1]+1
410 | points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] + np.tile(total_boxes[:,0],(5, 1))-1
411 | points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] + np.tile(total_boxes[:,1],(5, 1))-1
412 | if total_boxes.shape[0]>0:
413 | total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))
414 | pick = nms(total_boxes.copy(), 0.7, 'Min')
415 | total_boxes = total_boxes[pick,:]
416 | points = points[:,pick]
417 |
418 | return total_boxes, points
419 |
420 |
421 | def bulk_detect_face(images, detection_window_size_ratio, pnet, rnet, onet, threshold, factor):
422 | """Detects faces in a list of images
423 | images: list containing input images
424 | detection_window_size_ratio: ratio of minimum face size to smallest image dimension
425 | pnet, rnet, onet: caffemodel
426 | threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold [0-1]
427 | factor: the factor used to create a scaling pyramid of face sizes to detect in the image.
428 | """
429 | all_scales = [None] * len(images)
430 | images_with_boxes = [None] * len(images)
431 |
432 | for i in range(len(images)):
433 | images_with_boxes[i] = {'total_boxes': np.empty((0, 9))}
434 |
435 | # create scale pyramid
436 | for index, img in enumerate(images):
437 | all_scales[index] = []
438 | h = img.shape[0]
439 | w = img.shape[1]
440 | minsize = int(detection_window_size_ratio * np.minimum(w, h))
441 | factor_count = 0
442 | minl = np.amin([h, w])
443 | if minsize <= 12:
444 | minsize = 12
445 |
446 | m = 12.0 / minsize
447 | minl = minl * m
448 | while minl >= 12:
449 | all_scales[index].append(m * np.power(factor, factor_count))
450 | minl = minl * factor
451 | factor_count += 1
452 |
453 | # # # # # # # # # # # # #
454 | # first stage - fast proposal network (pnet) to obtain face candidates
455 | # # # # # # # # # # # # #
456 |
457 | images_obj_per_resolution = {}
458 |
459 | # TODO: use some type of rounding to number module 8 to increase probability that pyramid images will have the same resolution across input images
460 |
461 | for index, scales in enumerate(all_scales):
462 | h = images[index].shape[0]
463 | w = images[index].shape[1]
464 |
465 | for scale in scales:
466 | hs = int(np.ceil(h * scale))
467 | ws = int(np.ceil(w * scale))
468 |
469 | if (ws, hs) not in images_obj_per_resolution:
470 | images_obj_per_resolution[(ws, hs)] = []
471 |
472 | im_data = imresample(images[index], (hs, ws))
473 | im_data = (im_data - 127.5) * 0.0078125
474 | img_y = np.transpose(im_data, (1, 0, 2)) # caffe uses different dimensions ordering
475 | images_obj_per_resolution[(ws, hs)].append({'scale': scale, 'image': img_y, 'index': index})
476 |
477 | for resolution in images_obj_per_resolution:
478 | images_per_resolution = [i['image'] for i in images_obj_per_resolution[resolution]]
479 | outs = pnet(images_per_resolution)
480 |
481 | for index in range(len(outs[0])):
482 | scale = images_obj_per_resolution[resolution][index]['scale']
483 | image_index = images_obj_per_resolution[resolution][index]['index']
484 | out0 = np.transpose(outs[0][index], (1, 0, 2))
485 | out1 = np.transpose(outs[1][index], (1, 0, 2))
486 |
487 | boxes, _ = generateBoundingBox(out1[:, :, 1].copy(), out0[:, :, :].copy(), scale, threshold[0])
488 |
489 | # inter-scale nms
490 | pick = nms(boxes.copy(), 0.5, 'Union')
491 | if boxes.size > 0 and pick.size > 0:
492 | boxes = boxes[pick, :]
493 | images_with_boxes[image_index]['total_boxes'] = np.append(images_with_boxes[image_index]['total_boxes'],
494 | boxes,
495 | axis=0)
496 |
497 | for index, image_obj in enumerate(images_with_boxes):
498 | numbox = image_obj['total_boxes'].shape[0]
499 | if numbox > 0:
500 | h = images[index].shape[0]
501 | w = images[index].shape[1]
502 | pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Union')
503 | image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
504 | regw = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0]
505 | regh = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1]
506 | qq1 = image_obj['total_boxes'][:, 0] + image_obj['total_boxes'][:, 5] * regw
507 | qq2 = image_obj['total_boxes'][:, 1] + image_obj['total_boxes'][:, 6] * regh
508 | qq3 = image_obj['total_boxes'][:, 2] + image_obj['total_boxes'][:, 7] * regw
509 | qq4 = image_obj['total_boxes'][:, 3] + image_obj['total_boxes'][:, 8] * regh
510 | image_obj['total_boxes'] = np.transpose(np.vstack([qq1, qq2, qq3, qq4, image_obj['total_boxes'][:, 4]]))
511 | image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
512 | image_obj['total_boxes'][:, 0:4] = np.fix(image_obj['total_boxes'][:, 0:4]).astype(np.int32)
513 | dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
514 |
515 | numbox = image_obj['total_boxes'].shape[0]
516 | tempimg = np.zeros((24, 24, 3, numbox))
517 |
518 | if numbox > 0:
519 | for k in range(0, numbox):
520 | tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
521 | tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
522 | if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
523 | tempimg[:, :, :, k] = imresample(tmp, (24, 24))
524 | else:
525 | return np.empty()
526 |
527 | tempimg = (tempimg - 127.5) * 0.0078125
528 | image_obj['rnet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
529 |
530 | # # # # # # # # # # # # #
531 | # second stage - refinement of face candidates with rnet
532 | # # # # # # # # # # # # #
533 |
534 | bulk_rnet_input = np.empty((0, 24, 24, 3))
535 | for index, image_obj in enumerate(images_with_boxes):
536 | if 'rnet_input' in image_obj:
537 | bulk_rnet_input = np.append(bulk_rnet_input, image_obj['rnet_input'], axis=0)
538 |
539 | out = rnet(bulk_rnet_input)
540 | out0 = np.transpose(out[0])
541 | out1 = np.transpose(out[1])
542 | score = out1[1, :]
543 |
544 | i = 0
545 | for index, image_obj in enumerate(images_with_boxes):
546 | if 'rnet_input' not in image_obj:
547 | continue
548 |
549 | rnet_input_count = image_obj['rnet_input'].shape[0]
550 | score_per_image = score[i:i + rnet_input_count]
551 | out0_per_image = out0[:, i:i + rnet_input_count]
552 |
553 | ipass = np.where(score_per_image > threshold[1])
554 | image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
555 | np.expand_dims(score_per_image[ipass].copy(), 1)])
556 |
557 | mv = out0_per_image[:, ipass[0]]
558 |
559 | if image_obj['total_boxes'].shape[0] > 0:
560 | h = images[index].shape[0]
561 | w = images[index].shape[1]
562 | pick = nms(image_obj['total_boxes'], 0.7, 'Union')
563 | image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
564 | image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv[:, pick]))
565 | image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
566 |
567 | numbox = image_obj['total_boxes'].shape[0]
568 |
569 | if numbox > 0:
570 | tempimg = np.zeros((48, 48, 3, numbox))
571 | image_obj['total_boxes'] = np.fix(image_obj['total_boxes']).astype(np.int32)
572 | dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
573 |
574 | for k in range(0, numbox):
575 | tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
576 | tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
577 | if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
578 | tempimg[:, :, :, k] = imresample(tmp, (48, 48))
579 | else:
580 | return np.empty()
581 | tempimg = (tempimg - 127.5) * 0.0078125
582 | image_obj['onet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
583 |
584 | i += rnet_input_count
585 |
586 | # # # # # # # # # # # # #
587 | # third stage - further refinement and facial landmarks positions with onet
588 | # # # # # # # # # # # # #
589 |
590 | bulk_onet_input = np.empty((0, 48, 48, 3))
591 | for index, image_obj in enumerate(images_with_boxes):
592 | if 'onet_input' in image_obj:
593 | bulk_onet_input = np.append(bulk_onet_input, image_obj['onet_input'], axis=0)
594 |
595 | out = onet(bulk_onet_input)
596 |
597 | out0 = np.transpose(out[0])
598 | out1 = np.transpose(out[1])
599 | out2 = np.transpose(out[2])
600 | score = out2[1, :]
601 | points = out1
602 |
603 | i = 0
604 | ret = []
605 | for index, image_obj in enumerate(images_with_boxes):
606 | if 'onet_input' not in image_obj:
607 | ret.append(None)
608 | continue
609 |
610 | onet_input_count = image_obj['onet_input'].shape[0]
611 |
612 | out0_per_image = out0[:, i:i + onet_input_count]
613 | score_per_image = score[i:i + onet_input_count]
614 | points_per_image = points[:, i:i + onet_input_count]
615 |
616 | ipass = np.where(score_per_image > threshold[2])
617 | points_per_image = points_per_image[:, ipass[0]]
618 |
619 | image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
620 | np.expand_dims(score_per_image[ipass].copy(), 1)])
621 | mv = out0_per_image[:, ipass[0]]
622 |
623 | w = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0] + 1
624 | h = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1] + 1
625 | points_per_image[0:5, :] = np.tile(w, (5, 1)) * points_per_image[0:5, :] + np.tile(
626 | image_obj['total_boxes'][:, 0], (5, 1)) - 1
627 | points_per_image[5:10, :] = np.tile(h, (5, 1)) * points_per_image[5:10, :] + np.tile(
628 | image_obj['total_boxes'][:, 1], (5, 1)) - 1
629 |
630 | if image_obj['total_boxes'].shape[0] > 0:
631 | image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv))
632 | pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Min')
633 | image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
634 | points_per_image = points_per_image[:, pick]
635 |
636 | ret.append((image_obj['total_boxes'], points_per_image))
637 | else:
638 | ret.append(None)
639 |
640 | i += onet_input_count
641 |
642 | return ret
643 |
644 |
645 | # function [boundingbox] = bbreg(boundingbox,reg)
646 | def bbreg(boundingbox,reg):
647 | """Calibrate bounding boxes"""
648 | if reg.shape[1]==1:
649 | reg = np.reshape(reg, (reg.shape[2], reg.shape[3]))
650 |
651 | w = boundingbox[:,2]-boundingbox[:,0]+1
652 | h = boundingbox[:,3]-boundingbox[:,1]+1
653 | b1 = boundingbox[:,0]+reg[:,0]*w
654 | b2 = boundingbox[:,1]+reg[:,1]*h
655 | b3 = boundingbox[:,2]+reg[:,2]*w
656 | b4 = boundingbox[:,3]+reg[:,3]*h
657 | boundingbox[:,0:4] = np.transpose(np.vstack([b1, b2, b3, b4 ]))
658 | return boundingbox
659 |
660 | def generateBoundingBox(imap, reg, scale, t):
661 | """Use heatmap to generate bounding boxes"""
662 | stride=2
663 | cellsize=12
664 |
665 | imap = np.transpose(imap)
666 | dx1 = np.transpose(reg[:,:,0])
667 | dy1 = np.transpose(reg[:,:,1])
668 | dx2 = np.transpose(reg[:,:,2])
669 | dy2 = np.transpose(reg[:,:,3])
670 | y, x = np.where(imap >= t)
671 | if y.shape[0]==1:
672 | dx1 = np.flipud(dx1)
673 | dy1 = np.flipud(dy1)
674 | dx2 = np.flipud(dx2)
675 | dy2 = np.flipud(dy2)
676 | score = imap[(y,x)]
677 | reg = np.transpose(np.vstack([ dx1[(y,x)], dy1[(y,x)], dx2[(y,x)], dy2[(y,x)] ]))
678 | if reg.size==0:
679 | reg = np.empty((0,3))
680 | bb = np.transpose(np.vstack([y,x]))
681 | q1 = np.fix((stride*bb+1)/scale)
682 | q2 = np.fix((stride*bb+cellsize-1+1)/scale)
683 | boundingbox = np.hstack([q1, q2, np.expand_dims(score,1), reg])
684 | return boundingbox, reg
685 |
686 | # function pick = nms(boxes,threshold,type)
687 | def nms(boxes, threshold, method):
688 | if boxes.size==0:
689 | return np.empty((0,3))
690 | x1 = boxes[:,0]
691 | y1 = boxes[:,1]
692 | x2 = boxes[:,2]
693 | y2 = boxes[:,3]
694 | s = boxes[:,4]
695 | area = (x2-x1+1) * (y2-y1+1)
696 | I = np.argsort(s)
697 | pick = np.zeros_like(s, dtype=np.int16)
698 | counter = 0
699 | while I.size>0:
700 | i = I[-1]
701 | pick[counter] = i
702 | counter += 1
703 | idx = I[0:-1]
704 | xx1 = np.maximum(x1[i], x1[idx])
705 | yy1 = np.maximum(y1[i], y1[idx])
706 | xx2 = np.minimum(x2[i], x2[idx])
707 | yy2 = np.minimum(y2[i], y2[idx])
708 | w = np.maximum(0.0, xx2-xx1+1)
709 | h = np.maximum(0.0, yy2-yy1+1)
710 | inter = w * h
711 | if method is 'Min':
712 | o = inter / np.minimum(area[i], area[idx])
713 | else:
714 | o = inter / (area[i] + area[idx] - inter)
715 | I = I[np.where(o<=threshold)]
716 | pick = pick[0:counter]
717 | return pick
718 |
719 | # function [dy edy dx edx y ey x ex tmpw tmph] = pad(total_boxes,w,h)
720 | def pad(total_boxes, w, h):
721 | """Compute the padding coordinates (pad the bounding boxes to square)"""
722 | tmpw = (total_boxes[:,2]-total_boxes[:,0]+1).astype(np.int32)
723 | tmph = (total_boxes[:,3]-total_boxes[:,1]+1).astype(np.int32)
724 | numbox = total_boxes.shape[0]
725 |
726 | dx = np.ones((numbox), dtype=np.int32)
727 | dy = np.ones((numbox), dtype=np.int32)
728 | edx = tmpw.copy().astype(np.int32)
729 | edy = tmph.copy().astype(np.int32)
730 |
731 | x = total_boxes[:,0].copy().astype(np.int32)
732 | y = total_boxes[:,1].copy().astype(np.int32)
733 | ex = total_boxes[:,2].copy().astype(np.int32)
734 | ey = total_boxes[:,3].copy().astype(np.int32)
735 |
736 | tmp = np.where(ex>w)
737 | edx.flat[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],1)
738 | ex[tmp] = w
739 |
740 | tmp = np.where(ey>h)
741 | edy.flat[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],1)
742 | ey[tmp] = h
743 |
744 | tmp = np.where(x<1)
745 | dx.flat[tmp] = np.expand_dims(2-x[tmp],1)
746 | x[tmp] = 1
747 |
748 | tmp = np.where(y<1)
749 | dy.flat[tmp] = np.expand_dims(2-y[tmp],1)
750 | y[tmp] = 1
751 |
752 | return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph
753 |
754 | # function [bboxA] = rerec(bboxA)
755 | def rerec(bboxA):
756 | """Convert bboxA to square."""
757 | h = bboxA[:,3]-bboxA[:,1]
758 | w = bboxA[:,2]-bboxA[:,0]
759 | l = np.maximum(w, h)
760 | bboxA[:,0] = bboxA[:,0]+w*0.5-l*0.5
761 | bboxA[:,1] = bboxA[:,1]+h*0.5-l*0.5
762 | bboxA[:,2:4] = bboxA[:,0:2] + np.transpose(np.tile(l,(2,1)))
763 | return bboxA
764 |
765 | def imresample(img, sz):
766 | im_data = cv2.resize(img, (sz[1], sz[0]), interpolation=cv2.INTER_AREA) #@UndefinedVariable
767 | return im_data
768 |
769 | # This method is kept for debugging purpose
770 | # h=img.shape[0]
771 | # w=img.shape[1]
772 | # hs, ws = sz
773 | # dx = float(w) / ws
774 | # dy = float(h) / hs
775 | # im_data = np.zeros((hs,ws,3))
776 | # for a1 in range(0,hs):
777 | # for a2 in range(0,ws):
778 | # for a3 in range(0,3):
779 | # im_data[a1,a2,a3] = img[int(floor(a1*dy)),int(floor(a2*dx)),a3]
780 | # return im_data
781 |
782 |
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/bounding.py:
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1 | import cv2
2 | import os
3 |
4 | def draw_bounding_box(filename,width,height,label,xmin,ymin,xmax,ymax,save_path):
5 |
6 | # get the file path
7 | images = "images"
8 |
9 | # Image Path
10 | img_path = os.path.join(os.getcwd(),images,label,filename)
11 |
12 | # Reading the Image
13 | im = cv2.imread(img_path)
14 | cv2.rectangle(im,(xmin,ymin),(xmax,ymax),(0,255,0),2)
15 | cv2.imwrite(save_path, im)
16 |
17 | # for i in range(0, len(contours)):
18 | # if (i % 2 == 0):
19 | # cnt = contours[i]
20 | # #mask = np.zeros(im2.shape,np.uint8)
21 | # #cv2.drawContours(mask,[cnt],0,255,-1)
22 | # x,y,w,h = cv2.boundingRect(cnt)
23 | cv2.waitKey()
24 | cv2.destroyAllWindows()
25 |
26 |
27 |
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/cascade.py:
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1 | import cv2
2 | import os
3 | import numpy as np
4 |
5 | CASE_PATH=os.path.join(os.getcwd(),"haarcascade_frontalface_default.xml")
6 | face_size = 64
7 |
8 | def crop_face( imgarray, section, margin=40, size=64):
9 | """
10 | :param imgarray: full image
11 | :param section: face detected area (x, y, w, h)
12 | :param margin: add some margin to the face detected area to include a full head
13 | :param size: the result image resolution with be (size x size)
14 | :return: resized image in numpy array with shape (size x size x 3)
15 | """
16 | img_h, img_w, _ = imgarray.shape
17 | if section is None:
18 | section = [0, 0, img_w, img_h]
19 | (x, y, w, h) = section
20 | margin = int(min(w,h) * margin / 100)
21 | x_a = x - margin
22 | y_a = y - margin
23 | x_b = x + w + margin
24 | y_b = y + h + margin
25 | if x_a < 0:
26 | x_b = min(x_b - x_a, img_w-1)
27 | x_a = 0
28 | if y_a < 0:
29 | y_b = min(y_b - y_a, img_h-1)
30 | y_a = 0
31 | if x_b > img_w:
32 | x_a = max(x_a - (x_b - img_w), 0)
33 | x_b = img_w
34 | if y_b > img_h:
35 | y_a = max(y_a - (y_b - img_h), 0)
36 | y_b = img_h
37 | cropped = imgarray[y_a: y_b, x_a: x_b]
38 | resized_img = cv2.resize(cropped, (size, size), interpolation=cv2.INTER_AREA)
39 | resized_img = np.array(resized_img)
40 | return resized_img, (x_a, y_a, x_b - x_a, y_b - y_a)
41 |
42 |
43 |
44 |
45 |
46 | def detect_face(image):
47 | xmin = ymin = xmax = ymax = 0
48 | frame = np.asarray(image)
49 | face_cascade = cv2.CascadeClassifier(CASE_PATH)
50 | gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
51 | faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5,minSize=(face_size, face_size), flags=cv2.CASCADE_SCALE_IMAGE)
52 | face_imgs = np.empty((len(faces), face_size, face_size, 3))
53 | for i, face in enumerate(faces):
54 | face_img, cropped = crop_face(frame, face, margin=40, size=face_size)
55 | (x, y, w, h) = cropped
56 | if i == 0:
57 | xmin = x
58 | ymin = y
59 | xmax = x+w
60 | ymax = y+h
61 |
62 | return xmin , ymin , xmax , ymax
--------------------------------------------------------------------------------
/check.py:
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1 | import os
2 | import sys
3 | import argparse
4 | from bounding import draw_bounding_box
5 |
6 |
7 | def read_csv(csv_name):
8 | # reading the file
9 | file = open(csv_name, 'r')
10 | file_data = file.read()
11 |
12 | # Split lines into list.
13 | file_data_lines = file_data.split('\n')
14 | file_data_lines
15 |
16 | # Create the final cleaned list.
17 | cleaned_file = []
18 | # Loop to iterate and process each line.
19 | for line in file_data_lines:
20 | processed_line = line.split(',')
21 | cleaned_file.append(processed_line)
22 |
23 | return cleaned_file
24 |
25 | def create_output_dirs(class_label):
26 | # defining the output directory
27 | output_path=os.path.join(os.getcwd(),"output")
28 | # defining the output label directory
29 | for i in class_label:
30 | # saving directory
31 | save_path=os.path.join(output_path,i)
32 | if not os.path.exists(save_path):
33 | os.mkdir(save_path)
34 |
35 | return output_path
36 |
37 |
38 |
39 | def export(csv_name,class_label):
40 |
41 | # read the csv and return the list
42 | data_list = read_csv(csv_name)
43 |
44 | # setting up the output directory
45 | primary_path = create_output_dirs(class_label)
46 |
47 | # Drawing boundary box for the images
48 | for i in range(1,len(data_list)):
49 | if len(data_list[i]) == 8 :
50 | filename=data_list[i][0]
51 | width=int(float(data_list[i][1]))
52 | height=int(float(data_list[i][2]))
53 | label=data_list[i][3]
54 | xmin=int(float(data_list[i][4]))
55 | ymin=int(float(data_list[i][5]))
56 | xmax=int(float(data_list[i][6]))
57 | ymax=int(float(data_list[i][7]))
58 |
59 | save_path = os.path.join(primary_path,label,filename)
60 | draw_bounding_box(filename,width,height,label,xmin,ymin,xmax,ymax,save_path)
61 |
62 | print("Successfully Exported the Output")
63 |
64 |
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/coordinates.py:
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1 | from scipy import misc
2 | import tensorflow as tf
3 | import numpy as np
4 | import os
5 | import align.detect_face
6 |
7 |
8 |
9 |
10 | def embeddings(image_path,image_size,margin,gpu_memory_fraction):
11 |
12 | minsize = 10 # minimum size of face
13 | threshold = [0.6,0.7,0.7] # p,r,o nets threshold
14 | factor = 0.709 # Standard Scaling Factor
15 | x = y = w = h = 0
16 |
17 | with tf.Graph().as_default():
18 | #creating a tf graph and also setting the gpu options
19 | gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
20 |
21 | #Defining the session to run
22 | sess= tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,log_device_placement=False))
23 | with sess.as_default():
24 | # Structuring pnet , rnet and onet
25 | pnet , rnet , onet = align.detect_face.create_mtcnn(sess,None)
26 |
27 | # Reading the image using misc
28 | img=misc.imread(image_path)
29 | img = img[:,:,0:3]
30 | img_size = np.asarray(img.shape)[0:2]
31 |
32 | # detecting the face and getting the coordinates of the bounding box
33 | bounding_boxes , _ = align.detect_face.detect_face(img,minsize,pnet,rnet,onet,threshold,factor)
34 | nrof_faces = bounding_boxes.shape[0]
35 |
36 | if nrof_faces > 0:
37 | det = bounding_boxes[:,0:4]
38 | det_arr =[]
39 | det_arr.append(np.squeeze(det))
40 |
41 | for i , det in enumerate(det_arr):
42 | det = np.squeeze(det)
43 |
44 | if (len(det.shape)==1):
45 | bb = np.zeros(4,dtype=np.int32)
46 | bb[0] = x = np.maximum(det[0]-margin/2,0)
47 | bb[1] = y = np.maximum(det[1]-margin/2,0)
48 | bb[2] = w = np.minimum(det[2]+margin/2, img_size[1])
49 | bb[3] = h = np.minimum(det[3]+margin/2, img_size[0])
50 |
51 | else :
52 | x = y = w = h = 0
53 |
54 | # Calculating the coordinates
55 | xmin , ymin , xmax , ymax = x , y , w , h
56 |
57 | return xmin , ymin , xmax , ymax
58 |
59 |
60 |
61 |
62 |
63 |
64 |
--------------------------------------------------------------------------------
/dataset_util.py:
--------------------------------------------------------------------------------
1 | """Utility functions for creating TFRecord data sets."""
2 |
3 | import tensorflow as tf
4 |
5 |
6 | def int64_feature(value):
7 | return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
8 |
9 |
10 | def int64_list_feature(value):
11 | return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
12 |
13 |
14 | def bytes_feature(value):
15 | return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
16 |
17 |
18 | def bytes_list_feature(value):
19 | return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
20 |
21 |
22 | def float_list_feature(value):
23 | return tf.train.Feature(float_list=tf.train.FloatList(value=value))
24 |
25 |
26 | def read_examples_list(path):
27 | """Read list of training or validation examples.
28 |
29 | The file is assumed to contain a single example per line where the first
30 | token in the line is an identifier that allows us to find the image and
31 | annotation xml for that example.
32 |
33 | For example, the line:
34 | xyz 3
35 | would allow us to find files xyz.jpg and xyz.xml (the 3 would be ignored).
36 |
37 | Args:
38 | path: absolute path to examples list file.
39 |
40 | Returns:
41 | list of example identifiers (strings).
42 | """
43 | with tf.gfile.GFile(path) as fid:
44 | lines = fid.readlines()
45 | return [line.strip().split(' ')[0] for line in lines]
46 |
47 |
48 | def recursive_parse_xml_to_dict(xml):
49 | """Recursively parses XML contents to python dict.
50 |
51 | We assume that `object` tags are the only ones that can appear
52 | multiple times at the same level of a tree.
53 |
54 | Args:
55 | xml: xml tree obtained by parsing XML file contents using lxml.etree
56 |
57 | Returns:
58 | Python dictionary holding XML contents.
59 | """
60 | if not xml:
61 | return {xml.tag: xml.text}
62 | result = {}
63 | for child in xml:
64 | child_result = recursive_parse_xml_to_dict(child)
65 | if child.tag != 'object':
66 | result[child.tag] = child_result[child.tag]
67 | else:
68 | if child.tag not in result:
69 | result[child.tag] = []
70 | result[child.tag].append(child_result[child.tag])
71 | return {xml.tag: result}
72 |
73 |
74 |
--------------------------------------------------------------------------------
/generate_tfrecord.py:
--------------------------------------------------------------------------------
1 | import os
2 | import io
3 | import pandas as pd
4 | import tensorflow as tf
5 |
6 | from PIL import Image
7 | import dataset_util
8 | from collections import namedtuple , OrderedDict
9 |
10 |
11 | def class_img_dict(path):
12 | # Getting the class as a list
13 | class_list = os.listdir(path)
14 |
15 | class_dict ={}
16 |
17 | for i in range(0,len(class_list)):
18 | class_dict[class_list[i]] = i+1
19 |
20 | return class_dict
21 |
22 |
23 |
24 | def split(df,group):
25 | # Spliting the object from the files
26 | data = namedtuple('data',['filename','label','object'])
27 | gb = df.groupby(group)
28 | li=[]
29 | for key, x in zip(gb.groups.keys(), gb.groups):
30 | d = data(key[0],key[1],gb.get_group(x))
31 | li.append(d)
32 | return li
33 |
34 | def create_tf_example(group, path):
35 | # Class numeric labels as dict
36 | class_dict=class_img_dict(path)
37 |
38 | #Opening and readinf the files
39 | with tf.gfile.GFile(os.path.join(path,'{}/{}'.format(group.label,group.filename)),'rb') as fid:
40 | encoded_jpg = fid.read()
41 |
42 | # Encode the image in jpeg format to array values
43 | encoded_jpg_io= io.BytesIO(encoded_jpg)
44 | image = Image.open(encoded_jpg_io)
45 |
46 | # Setting up the image size
47 | width , height = image.size
48 |
49 | #Creating the boundary box coordinate instances such as xmin,ymin,xmax,ymax
50 | filename = group.filename.encode('utf8')
51 | image_format = b'jpg'
52 | xmins = []
53 | xmaxs = []
54 | ymins = []
55 | ymaxs = []
56 | classes_text = []
57 | classes = []
58 |
59 | for index, row in group.object.iterrows():
60 | xmins.append(row['xmin'] / width)
61 | xmaxs.append(row['xmax'] /width)
62 | ymins.append(row['ymin'] / height)
63 | ymaxs.append(row['ymax'] / height)
64 | classes_text.append(row['class'].encode('utf8'))
65 | classes.append(class_dict[row['class']])
66 |
67 | # This is already exisiting code to convert csv to tfrecord
68 | tf_example = tf.train.Example(features=tf.train.Features(feature={
69 | 'image/height': dataset_util.int64_feature(height),
70 | 'image/width': dataset_util.int64_feature(width),
71 | 'image/filename': dataset_util.bytes_feature(filename),
72 | 'image/source_id': dataset_util.bytes_feature(filename),
73 | 'image/encoded': dataset_util.bytes_feature(encoded_jpg),
74 | 'image/format': dataset_util.bytes_feature(image_format),
75 | 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
76 | 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
77 | 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
78 | 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
79 | 'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
80 | 'image/object/class/label': dataset_util.int64_list_feature(classes),
81 | }))
82 | return tf_example
83 |
84 |
85 | def generate_tf(csv_name,tf_name,img_dir):
86 | #Creating a TFRecordWriter
87 | writer = tf.python_io.TFRecordWriter(tf_name)
88 |
89 | # selecting the path to the image folder
90 | path = os.path.join(os.getcwd(),'images')
91 |
92 | # Reading the csv from the data folder
93 | examples = pd.read_csv(csv_name)
94 | grouped = split(examples, ['filename','class'])
95 | for group in grouped:
96 | tf_example = create_tf_example(group,path)
97 | writer.write(tf_example.SerializeToString())
98 |
99 | writer.close()
100 |
101 | # After the conversion display the message
102 | output_path = os.path.join(os.getcwd(),tf_name)
103 | print('Successfully created the tfrecord for the images: {}'.format(output_path))
104 |
105 |
106 | if __name__ == '__main__':
107 | tf.app.run()
108 |
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/main.py:
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1 | import sys
2 | import os
3 | import argparse
4 | import csv
5 | from PIL import Image
6 | from coordinates import embeddings
7 | from generate_tfrecord import generate_tf
8 | from cascade import detect_face
9 | import cv2
10 | import numpy as np
11 | from check import export
12 |
13 |
14 | def writeCsvFile(file_name,data,*args,**kwargs):
15 | # Opening and writing the csv file
16 | with open(file_name,"w") as f:
17 | writer =csv.writer(f)
18 | writer.writerows(data)
19 |
20 | print ("****** Successfully CSV File Generated *************")
21 |
22 |
23 | def standard_size(image_dir):
24 | for dirs in os.listdir(image_dir):
25 | dir_path=os.path.join(image_dir,dirs)
26 | for f in os.listdir(dir_path):
27 | img_p = os.path.join(image_dir,dirs,f)
28 | im = Image.open(img_p)
29 | image_data = np.asarray(im)
30 | file_name=f
31 | imResize = cv2.resize(image_data, (500, 500))
32 | try:
33 | im = Image.fromarray(imResize, 'RGB')
34 | except ValueError:
35 | os.remove(img_p)
36 | print("Bad Image Format, Removing")
37 | continue
38 | im.save(dir_path+'/'+f, 'JPEG', quality=90)
39 |
40 |
41 | def image_process(image_dir,data_list,method):
42 |
43 | margin = 44 # Default Margin Value of the image
44 | counter = 0 # To count how many faces detected
45 | gpu_memory_fraction =1.0 # if we use GPU ,we define the upper bound of the GPU Memory
46 | standard_size(image_dir) # Converting all the image to standard size
47 | for dirs in os.listdir(image_dir):
48 | dir_path=os.path.join(image_dir,dirs)
49 | for f in os.listdir(dir_path):
50 | img_p = os.path.join(image_dir,dirs,f)
51 | label=img_p.split('/')[-2]
52 | file_name=f
53 | im = Image.open(img_p) # Opening the image using PILLOW
54 | w,h = im.size # getting the width and the height of the image
55 | size = im.size # for passing the face embeddings parameters
56 | if method == "harr":
57 | xmin,ymin,xmax,ymax=detect_face(im) # Using opencv method
58 | if method == "facenet":
59 | xmin,ymin,xmax,ymax = embeddings(img_p,size,margin,gpu_memory_fraction) # Calling the facenet embedding function
60 |
61 | if xmin == ymin == xmax == ymax == 0:
62 | # It will remove the undetected and error image
63 | os.remove(img_p)
64 | print("*"+img_p+"*")
65 | print("********** Error With the Image , So Removing **********")
66 |
67 | else:
68 | # It will add the detected image
69 | counter += 1
70 | print("Face Detected and Processed : {}".format(counter))
71 | data_list.append([file_name,w,h,label,xmin,ymin,xmax,ymax]) # Appending in a list format
72 |
73 | print("**** Successfully image processed *********")
74 |
75 | return data_list
76 |
77 |
78 | def parse_arguments(argv):
79 | # Defining the parser
80 | parser = argparse.ArgumentParser()
81 | parser.add_argument('csv_name',type=str,help='Name of the csv')
82 | parser.add_argument('tfrecord_name',type=str,help='Name of the tfrecord')
83 | parser.add_argument('method',type=str,help='Method to use to detect Face')
84 |
85 | return parser.parse_args(argv)
86 |
87 | # image directory
88 | image_dir = os.path.join(os.getcwd(),"images")
89 |
90 | # parsing the arguments
91 | args=parse_arguments(sys.argv[1:])
92 |
93 | # defining the list structure to convert to csv
94 | data_list = [['filename','width','height','class','xmin','ymin','xmax','ymax']]
95 |
96 | # process the images and getting the coordinate values as list
97 | coordinate_list = image_process(image_dir,data_list,args.method)
98 |
99 |
100 |
101 | # writing into csv file
102 | writeCsvFile(args.csv_name, coordinate_list)
103 |
104 | # conversion to tfrecord
105 | generate_tf(args.csv_name,args.tfrecord_name,image_dir)
106 |
107 | # Exporting the output
108 | class_labels=[]
109 | for dirs in os.listdir(image_dir):
110 | class_labels.append(dirs)
111 | export(args.csv_name,class_labels) # Calling the export function from check
112 |
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1 | absl-py==0.5.0
2 | astor==0.7.1
3 | gast==0.2.0
4 | grpcio==1.15.0
5 | Markdown==2.6.11
6 | numpy==1.14.5
7 | opencv-python==3.4.3.18
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13 | scipy==1.1.0
14 | six==1.11.0
15 | tensorboard==1.10.0
16 | tensorflow==1.5
17 | termcolor==1.1.0
18 | Werkzeug==0.14.1
19 |
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21 | 9.jpg,500,500,Steve Jobs,146.68806380033493,77.73356232047081,315.03020241856575,335.83591252565384
22 | 10.jpg,500,500,Steve Jobs,225.13706648349762,2.398385599255562,361.0492430701852,154.7256064414978
23 | 6.jpg,500,500,Steve Jobs,218.12086181342602,8.11365981400013,343.0373011380434,144.2709012106061
24 | 11.jpg,500,500,Steve Jobs,244.65993185341358,84.82998438179493,457.49497958272696,391.64134995639324
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26 | 25.jpg,500,500,Steve Jobs,65.07906196266413,118.83178769052029,305.5486790537834,452.98920045234263
27 | 5.jpeg,500,500,Steve Jobs,258.1336739063263,82.46733379364014,412.70067942142487,295.176632123068
28 | 29.jpg,500,500,Steve Jobs,290.2734102010727,14.02329371869564,392.66675551980734,151.8797261044383
29 | 32.jpg,500,500,Steve Jobs,174.5612851381302,64.4969094991684,399.8769828081131,365.0464195907116
30 | 14.jpg,500,500,Steve Jobs,120.8039962053299,0.0,360.8931660503149,332.9454622119665
31 | 33.jpg,500,500,Steve Jobs,175.4041292965412,46.94836711883545,400.03346379101276,323.7934452742338
32 | 24.jpg,500,500,Steve Jobs,109.88684584200382,134.08298152685165,360.8996148407459,446.91195073723793
33 | 20.jpg,500,500,Steve Jobs,133.7601788789034,76.41618722677231,269.9057053029537,272.0421134829521
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35 | 59.jpg,500,500,Tim Cook,209.26165553927422,99.50566458702087,311.23967906832695,236.30761414766312
36 | 65.jpg,500,500,Tim Cook,171.1068091392517,58.0843586102128,260.09109196066856,173.77179324626923
37 | 36.jpg,500,500,Tim Cook,94.24210131168365,134.7042466700077,294.74460627138615,401.6068183928728
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39 | 1.jpg,500,500,Tim Cook,138.7424283400178,57.43666069209576,356.4918710887432,364.8453649505973
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51 | 16.jpg,500,500,Tim Cook,90.1915619969368,49.568554759025574,365.0864183306694,395.9880170226097
52 | 46.jpg,500,500,Tim Cook,74.5439283773303,112.7702138274908,234.0143654793501,335.4605439007282
53 | 7.jpg,500,500,Tim Cook,85.71998123824596,108.36914601922035,281.6766269803047,339.00113795511425
54 | 23.jpg,500,500,Tim Cook,178.53562933206558,57.87383475899696,328.01815658807755,253.42727002501488
55 | 37.jpg,500,500,Tim Cook,103.07761377096176,80.29253044724464,339.8693901002407,373.1692569553852
56 | 18.jpg,500,500,Tim Cook,98.20617580413818,54.846071004867554,322.1488095521927,361.17918434739113
57 | 48.jpg,500,500,Tim Cook,62.90743473172188,77.87760281562805,354.9854444563389,427.6464519947767
58 | 8.jpg,500,500,Tim Cook,136.35785099864006,47.5033420920372,403.55128115415573,448.85420718602836
59 | 49.jpeg,500,500,Tim Cook,96.78456282615662,43.11342504620552,380.62895504385233,395.05495394580066
60 | 9.jpg,500,500,Tim Cook,154.25770449638367,58.94842228293419,333.88705982267857,323.98244766145945
61 | 19.jpeg,500,500,Tim Cook,131.16991011798382,71.66776690632105,324.49768133461475,317.0154584888369
62 | 10.jpg,500,500,Tim Cook,229.72305999696255,105.6425204128027,416.722112134099,369.4993640705943
63 | 6.jpg,500,500,Tim Cook,134.0563516393304,31.391966372728348,348.24291322380304,319.5538585484028
64 | 67.jpg,500,500,Tim Cook,68.7516679763794,105.971385627985,262.9316173195839,367.1877672225237
65 | 50.jpg,500,500,Tim Cook,208.72644520550966,123.21461126208305,317.6124521046877,261.56570184230804
66 | 35.jpg,500,500,Tim Cook,189.03446793556213,26.41620969772339,322.59043407440186,194.43461275100708
67 | 11.jpg,500,500,Tim Cook,216.23747350275517,107.8433733060956,346.9579378515482,245.98768024146557
68 | 60.jpg,500,500,Tim Cook,160.69035148620605,60.01768986880779,344.6268826946616,309.40651085972786
69 | 54.jpg,500,500,Tim Cook,106.15717387199402,102.04869528114796,247.98449602723122,284.8523946925998
70 | 43.jpg,500,500,Tim Cook,205.6414727345109,81.81095653772354,346.5953520089388,271.2438712120056
71 | 56.jpg,500,500,Tim Cook,190.26858639717102,80.96642634272575,384.7701907157898,350.5161276701838
72 | 5.jpeg,500,500,Tim Cook,173.45566235482693,82.91859129071236,319.0773280262947,265.87047067284584
73 | 69.jpg,500,500,Tim Cook,192.77310627698898,260.121173620224,328.89927838742733,429.2760783396661
74 | 44.jpg,500,500,Tim Cook,255.5412304699421,99.52657423913479,349.57018576562405,211.28186763823032
75 | 13.jpg,500,500,Tim Cook,143.15577447414398,129.9544226527214,265.2668823748827,260.149150878191
76 | 32.jpg,500,500,Tim Cook,86.37660647928715,55.35516995936632,351.10457775741816,371.5751672629267
77 | 68.jpg,500,500,Tim Cook,151.32515504956245,0.0,307.6344681829214,178.8307080771774
78 | 27.jpg,500,500,Tim Cook,42.70212519168854,116.69068264961243,222.66294527053833,367.2019245624542
79 | 33.jpg,500,500,Tim Cook,189.63145922124386,103.35713970661163,301.883116543293,244.2731357961893
80 | 55.jpg,500,500,Tim Cook,179.6229516863823,79.94993408024311,321.0401841402054,275.98883505165577
81 | 61.jpg,500,500,Tim Cook,171.21220479160547,53.7367187589407,299.2701509743929,216.05458861589432
82 | 24.jpg,500,500,Tim Cook,304.19089172780514,152.54564601182938,428.0053465887904,325.58133359253407
83 |
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/train.record:
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https://raw.githubusercontent.com/robinreni96/Automatic-Face-Detection-Annotation-and-Preprocessing/3a8e62c9a58de44043a7968e8d658d5d5d566f3e/train.record
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