├── .gitignore
├── LICENSE
├── README.md
├── model
├── det1.caffemodel
├── det1.png
├── det1.prototxt
├── det2.caffemodel
├── det2.png
├── det2.prototxt
├── det3.caffemodel
├── det3.png
└── det3.prototxt
└── pymtcnn.py
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | *.egg-info/
24 | .installed.cfg
25 | *.egg
26 | MANIFEST
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 | .pytest_cache/
49 |
50 | # Translations
51 | *.mo
52 | *.pot
53 |
54 | # Django stuff:
55 | *.log
56 | local_settings.py
57 | db.sqlite3
58 |
59 | # Flask stuff:
60 | instance/
61 | .webassets-cache
62 |
63 | # Scrapy stuff:
64 | .scrapy
65 |
66 | # Sphinx documentation
67 | docs/_build/
68 |
69 | # PyBuilder
70 | target/
71 |
72 | # Jupyter Notebook
73 | .ipynb_checkpoints
74 |
75 | # pyenv
76 | .python-version
77 |
78 | # celery beat schedule file
79 | celerybeat-schedule
80 |
81 | # SageMath parsed files
82 | *.sage.py
83 |
84 | # Environments
85 | .env
86 | .venv
87 | env/
88 | venv/
89 | ENV/
90 | env.bak/
91 | venv.bak/
92 |
93 | # Spyder project settings
94 | .spyderproject
95 | .spyproject
96 |
97 | # Rope project settings
98 | .ropeproject
99 |
100 | # mkdocs documentation
101 | /site
102 |
103 | # mypy
104 | .mypy_cache/
105 |
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--------------------------------------------------------------------------------
/README.md:
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1 | # pymtcnn
2 | - pymtcnn is a caffe based python implementation for MTCNN (Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks);
3 | - You can visit https://zhuanlan.zhihu.com/p/39499030 for more detail.
4 |
5 | # Functions
6 | - detect faces;
7 | - align faces with cv2.warpAffine API.
8 |
9 |
10 | # Usage
11 |
12 | ## pre configure
13 | There has some options you need pre configure, of course you can leave them as default value, but I don't think that is what you want;
14 | - multi scale values: `self.d['scales'] = [0.3, 0.15, 0.075]`
15 | - PNet detect threshold: `self.detect_thresh_value = 0.8`
16 | - RNet detect threshold: `self.detect_thresh_value = 0.9`
17 | - ONet detect threshold: `self.detect_thresh_value = 0.95`
18 |
19 | ## call the API
20 | ```python
21 | videoCapture = cv2.VideoCapture(video_path)
22 | status, frame = videoCapture.read()
23 | # Mtcnn instance
24 | mtcnn = Mtcnn()
25 | # AlignFace instance
26 | alignFace = AlignFace()
27 | frame_num = 0
28 | while status:
29 | frame_num += 1
30 | print('process {}'.format(frame_num))
31 | # do the mtcnn detect job with this API
32 | rc = mtcnn.process(frame)
33 | if not rc:
34 | status, frame = videoCapture.read()
35 | continue
36 | # draw the boxes just for test purpose
37 | mtcnn.drawBoxes(frame, frame_num)
38 | points = mtcnn.d['points']
39 | if points.shape[0] > 0:
40 | for i in range(points.shape[0]):
41 | # align the faces
42 | alignFace.drawBoxes(alignFace(frame, points[i,:]), frame_num)
43 | status, frame = videoCapture.read()
44 | else:
45 | print('End for some reasons...')
46 | ```
47 |
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/model/det1.prototxt:
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1 | name: "PNet"
2 | input: "data"
3 | input_dim: 1
4 | input_dim: 3
5 | input_dim: 12
6 | input_dim: 12
7 |
8 | layer {
9 | name: "conv1"
10 | type: "Convolution"
11 | bottom: "data"
12 | top: "conv1"
13 | param {
14 | lr_mult: 1
15 | decay_mult: 1
16 | }
17 | param {
18 | lr_mult: 2
19 | decay_mult: 0
20 | }
21 | convolution_param {
22 | num_output: 10
23 | kernel_size: 3
24 | stride: 1
25 | weight_filler {
26 | type: "xavier"
27 | }
28 | bias_filler {
29 | type: "constant"
30 | value: 0
31 | }
32 | }
33 | }
34 | layer {
35 | name: "PReLU1"
36 | type: "PReLU"
37 | bottom: "conv1"
38 | top: "conv1"
39 | }
40 | layer {
41 | name: "pool1"
42 | type: "Pooling"
43 | bottom: "conv1"
44 | top: "pool1"
45 | pooling_param {
46 | pool: MAX
47 | kernel_size: 2
48 | stride: 2
49 | }
50 | }
51 |
52 | layer {
53 | name: "conv2"
54 | type: "Convolution"
55 | bottom: "pool1"
56 | top: "conv2"
57 | param {
58 | lr_mult: 1
59 | decay_mult: 1
60 | }
61 | param {
62 | lr_mult: 2
63 | decay_mult: 0
64 | }
65 | convolution_param {
66 | num_output: 16
67 | kernel_size: 3
68 | stride: 1
69 | weight_filler {
70 | type: "xavier"
71 | }
72 | bias_filler {
73 | type: "constant"
74 | value: 0
75 | }
76 | }
77 | }
78 | layer {
79 | name: "PReLU2"
80 | type: "PReLU"
81 | bottom: "conv2"
82 | top: "conv2"
83 | }
84 |
85 | layer {
86 | name: "conv3"
87 | type: "Convolution"
88 | bottom: "conv2"
89 | top: "conv3"
90 | param {
91 | lr_mult: 1
92 | decay_mult: 1
93 | }
94 | param {
95 | lr_mult: 2
96 | decay_mult: 0
97 | }
98 | convolution_param {
99 | num_output: 32
100 | kernel_size: 3
101 | stride: 1
102 | weight_filler {
103 | type: "xavier"
104 | }
105 | bias_filler {
106 | type: "constant"
107 | value: 0
108 | }
109 | }
110 | }
111 | layer {
112 | name: "PReLU3"
113 | type: "PReLU"
114 | bottom: "conv3"
115 | top: "conv3"
116 | }
117 |
118 |
119 | layer {
120 | name: "conv4-1"
121 | type: "Convolution"
122 | bottom: "conv3"
123 | top: "conv4-1"
124 | param {
125 | lr_mult: 1
126 | decay_mult: 1
127 | }
128 | param {
129 | lr_mult: 2
130 | decay_mult: 0
131 | }
132 | convolution_param {
133 | num_output: 2
134 | kernel_size: 1
135 | stride: 1
136 | weight_filler {
137 | type: "xavier"
138 | }
139 | bias_filler {
140 | type: "constant"
141 | value: 0
142 | }
143 | }
144 | }
145 |
146 | layer {
147 | name: "conv4-2"
148 | type: "Convolution"
149 | bottom: "conv3"
150 | top: "conv4-2"
151 | param {
152 | lr_mult: 1
153 | decay_mult: 1
154 | }
155 | param {
156 | lr_mult: 2
157 | decay_mult: 0
158 | }
159 | convolution_param {
160 | num_output: 4
161 | kernel_size: 1
162 | stride: 1
163 | weight_filler {
164 | type: "xavier"
165 | }
166 | bias_filler {
167 | type: "constant"
168 | value: 0
169 | }
170 | }
171 | }
172 | layer {
173 | name: "prob1"
174 | type: "Softmax"
175 | bottom: "conv4-1"
176 | top: "prob1"
177 | }
178 |
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/model/det2.prototxt:
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1 | name: "RNet"
2 | input: "data"
3 | input_dim: 1
4 | input_dim: 3
5 | input_dim: 24
6 | input_dim: 24
7 |
8 |
9 | ##########################
10 | ######################
11 | layer {
12 | name: "conv1"
13 | type: "Convolution"
14 | bottom: "data"
15 | top: "conv1"
16 | param {
17 | lr_mult: 0
18 | decay_mult: 0
19 | }
20 | param {
21 | lr_mult: 0
22 | decay_mult: 0
23 | }
24 | convolution_param {
25 | num_output: 28
26 | kernel_size: 3
27 | stride: 1
28 | weight_filler {
29 | type: "xavier"
30 | }
31 | bias_filler {
32 | type: "constant"
33 | value: 0
34 | }
35 | }
36 | }
37 | layer {
38 | name: "prelu1"
39 | type: "PReLU"
40 | bottom: "conv1"
41 | top: "conv1"
42 | propagate_down: true
43 | }
44 | layer {
45 | name: "pool1"
46 | type: "Pooling"
47 | bottom: "conv1"
48 | top: "pool1"
49 | pooling_param {
50 | pool: MAX
51 | kernel_size: 3
52 | stride: 2
53 | }
54 | }
55 |
56 | layer {
57 | name: "conv2"
58 | type: "Convolution"
59 | bottom: "pool1"
60 | top: "conv2"
61 | param {
62 | lr_mult: 0
63 | decay_mult: 0
64 | }
65 | param {
66 | lr_mult: 0
67 | decay_mult: 0
68 | }
69 | convolution_param {
70 | num_output: 48
71 | kernel_size: 3
72 | stride: 1
73 | weight_filler {
74 | type: "xavier"
75 | }
76 | bias_filler {
77 | type: "constant"
78 | value: 0
79 | }
80 | }
81 | }
82 | layer {
83 | name: "prelu2"
84 | type: "PReLU"
85 | bottom: "conv2"
86 | top: "conv2"
87 | propagate_down: true
88 | }
89 | layer {
90 | name: "pool2"
91 | type: "Pooling"
92 | bottom: "conv2"
93 | top: "pool2"
94 | pooling_param {
95 | pool: MAX
96 | kernel_size: 3
97 | stride: 2
98 | }
99 | }
100 | ####################################
101 |
102 | ##################################
103 | layer {
104 | name: "conv3"
105 | type: "Convolution"
106 | bottom: "pool2"
107 | top: "conv3"
108 | param {
109 | lr_mult: 0
110 | decay_mult: 0
111 | }
112 | param {
113 | lr_mult: 0
114 | decay_mult: 0
115 | }
116 | convolution_param {
117 | num_output: 64
118 | kernel_size: 2
119 | stride: 1
120 | weight_filler {
121 | type: "xavier"
122 | }
123 | bias_filler {
124 | type: "constant"
125 | value: 0
126 | }
127 | }
128 | }
129 | layer {
130 | name: "prelu3"
131 | type: "PReLU"
132 | bottom: "conv3"
133 | top: "conv3"
134 | propagate_down: true
135 | }
136 | ###############################
137 |
138 | ###############################
139 |
140 | layer {
141 | name: "conv4"
142 | type: "InnerProduct"
143 | bottom: "conv3"
144 | top: "conv4"
145 | param {
146 | lr_mult: 0
147 | decay_mult: 0
148 | }
149 | param {
150 | lr_mult: 0
151 | decay_mult: 0
152 | }
153 | inner_product_param {
154 | num_output: 128
155 | weight_filler {
156 | type: "xavier"
157 | }
158 | bias_filler {
159 | type: "constant"
160 | value: 0
161 | }
162 | }
163 | }
164 | layer {
165 | name: "prelu4"
166 | type: "PReLU"
167 | bottom: "conv4"
168 | top: "conv4"
169 | }
170 |
171 | layer {
172 | name: "conv5-1"
173 | type: "InnerProduct"
174 | bottom: "conv4"
175 | top: "conv5-1"
176 | param {
177 | lr_mult: 0
178 | decay_mult: 0
179 | }
180 | param {
181 | lr_mult: 0
182 | decay_mult: 0
183 | }
184 | inner_product_param {
185 | num_output: 2
186 | #kernel_size: 1
187 | #stride: 1
188 | weight_filler {
189 | type: "xavier"
190 | }
191 | bias_filler {
192 | type: "constant"
193 | value: 0
194 | }
195 | }
196 | }
197 | layer {
198 | name: "conv5-2"
199 | type: "InnerProduct"
200 | bottom: "conv4"
201 | top: "conv5-2"
202 | param {
203 | lr_mult: 1
204 | decay_mult: 1
205 | }
206 | param {
207 | lr_mult: 2
208 | decay_mult: 1
209 | }
210 | inner_product_param {
211 | num_output: 4
212 | #kernel_size: 1
213 | #stride: 1
214 | weight_filler {
215 | type: "xavier"
216 | }
217 | bias_filler {
218 | type: "constant"
219 | value: 0
220 | }
221 | }
222 | }
223 | layer {
224 | name: "prob1"
225 | type: "Softmax"
226 | bottom: "conv5-1"
227 | top: "prob1"
228 | }
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/model/det3.prototxt:
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1 | name: "ONet"
2 | input: "data"
3 | input_dim: 1
4 | input_dim: 3
5 | input_dim: 48
6 | input_dim: 48
7 | ##################################
8 | layer {
9 | name: "conv1"
10 | type: "Convolution"
11 | bottom: "data"
12 | top: "conv1"
13 | param {
14 | lr_mult: 1
15 | decay_mult: 1
16 | }
17 | param {
18 | lr_mult: 2
19 | decay_mult: 1
20 | }
21 | convolution_param {
22 | num_output: 32
23 | kernel_size: 3
24 | stride: 1
25 | weight_filler {
26 | type: "xavier"
27 | }
28 | bias_filler {
29 | type: "constant"
30 | value: 0
31 | }
32 | }
33 | }
34 | layer {
35 | name: "prelu1"
36 | type: "PReLU"
37 | bottom: "conv1"
38 | top: "conv1"
39 | }
40 | layer {
41 | name: "pool1"
42 | type: "Pooling"
43 | bottom: "conv1"
44 | top: "pool1"
45 | pooling_param {
46 | pool: MAX
47 | kernel_size: 3
48 | stride: 2
49 | }
50 | }
51 | layer {
52 | name: "conv2"
53 | type: "Convolution"
54 | bottom: "pool1"
55 | top: "conv2"
56 | param {
57 | lr_mult: 1
58 | decay_mult: 1
59 | }
60 | param {
61 | lr_mult: 2
62 | decay_mult: 1
63 | }
64 | convolution_param {
65 | num_output: 64
66 | kernel_size: 3
67 | stride: 1
68 | weight_filler {
69 | type: "xavier"
70 | }
71 | bias_filler {
72 | type: "constant"
73 | value: 0
74 | }
75 | }
76 | }
77 |
78 | layer {
79 | name: "prelu2"
80 | type: "PReLU"
81 | bottom: "conv2"
82 | top: "conv2"
83 | }
84 | layer {
85 | name: "pool2"
86 | type: "Pooling"
87 | bottom: "conv2"
88 | top: "pool2"
89 | pooling_param {
90 | pool: MAX
91 | kernel_size: 3
92 | stride: 2
93 | }
94 | }
95 |
96 | layer {
97 | name: "conv3"
98 | type: "Convolution"
99 | bottom: "pool2"
100 | top: "conv3"
101 | param {
102 | lr_mult: 1
103 | decay_mult: 1
104 | }
105 | param {
106 | lr_mult: 2
107 | decay_mult: 1
108 | }
109 | convolution_param {
110 | num_output: 64
111 | kernel_size: 3
112 | weight_filler {
113 | type: "xavier"
114 | }
115 | bias_filler {
116 | type: "constant"
117 | value: 0
118 | }
119 | }
120 | }
121 | layer {
122 | name: "prelu3"
123 | type: "PReLU"
124 | bottom: "conv3"
125 | top: "conv3"
126 | }
127 | layer {
128 | name: "pool3"
129 | type: "Pooling"
130 | bottom: "conv3"
131 | top: "pool3"
132 | pooling_param {
133 | pool: MAX
134 | kernel_size: 2
135 | stride: 2
136 | }
137 | }
138 | layer {
139 | name: "conv4"
140 | type: "Convolution"
141 | bottom: "pool3"
142 | top: "conv4"
143 | param {
144 | lr_mult: 1
145 | decay_mult: 1
146 | }
147 | param {
148 | lr_mult: 2
149 | decay_mult: 1
150 | }
151 | convolution_param {
152 | num_output: 128
153 | kernel_size: 2
154 | weight_filler {
155 | type: "xavier"
156 | }
157 | bias_filler {
158 | type: "constant"
159 | value: 0
160 | }
161 | }
162 | }
163 | layer {
164 | name: "prelu4"
165 | type: "PReLU"
166 | bottom: "conv4"
167 | top: "conv4"
168 | }
169 |
170 |
171 | layer {
172 | name: "conv5"
173 | type: "InnerProduct"
174 | bottom: "conv4"
175 | top: "conv5"
176 | param {
177 | lr_mult: 1
178 | decay_mult: 1
179 | }
180 | param {
181 | lr_mult: 2
182 | decay_mult: 1
183 | }
184 | inner_product_param {
185 | #kernel_size: 3
186 | num_output: 256
187 | weight_filler {
188 | type: "xavier"
189 | }
190 | bias_filler {
191 | type: "constant"
192 | value: 0
193 | }
194 | }
195 | }
196 |
197 | layer {
198 | name: "drop5"
199 | type: "Dropout"
200 | bottom: "conv5"
201 | top: "conv5"
202 | dropout_param {
203 | dropout_ratio: 0.25
204 | }
205 | }
206 | layer {
207 | name: "prelu5"
208 | type: "PReLU"
209 | bottom: "conv5"
210 | top: "conv5"
211 | }
212 |
213 |
214 | layer {
215 | name: "conv6-1"
216 | type: "InnerProduct"
217 | bottom: "conv5"
218 | top: "conv6-1"
219 | param {
220 | lr_mult: 1
221 | decay_mult: 1
222 | }
223 | param {
224 | lr_mult: 2
225 | decay_mult: 1
226 | }
227 | inner_product_param {
228 | #kernel_size: 1
229 | num_output: 2
230 | weight_filler {
231 | type: "xavier"
232 | }
233 | bias_filler {
234 | type: "constant"
235 | value: 0
236 | }
237 | }
238 | }
239 | layer {
240 | name: "conv6-2"
241 | type: "InnerProduct"
242 | bottom: "conv5"
243 | top: "conv6-2"
244 | param {
245 | lr_mult: 1
246 | decay_mult: 1
247 | }
248 | param {
249 | lr_mult: 2
250 | decay_mult: 1
251 | }
252 | inner_product_param {
253 | #kernel_size: 1
254 | num_output: 4
255 | weight_filler {
256 | type: "xavier"
257 | }
258 | bias_filler {
259 | type: "constant"
260 | value: 0
261 | }
262 | }
263 | }
264 | layer {
265 | name: "conv6-3"
266 | type: "InnerProduct"
267 | bottom: "conv5"
268 | top: "conv6-3"
269 | param {
270 | lr_mult: 1
271 | decay_mult: 1
272 | }
273 | param {
274 | lr_mult: 2
275 | decay_mult: 1
276 | }
277 | inner_product_param {
278 | #kernel_size: 1
279 | num_output: 10
280 | weight_filler {
281 | type: "xavier"
282 | }
283 | bias_filler {
284 | type: "constant"
285 | value: 0
286 | }
287 | }
288 | }
289 | layer {
290 | name: "prob1"
291 | type: "Softmax"
292 | bottom: "conv6-1"
293 | top: "prob1"
294 | }
295 |
--------------------------------------------------------------------------------
/pymtcnn.py:
--------------------------------------------------------------------------------
1 | # -*- coding: utf-8 -*-
2 | import os
3 | import sys
4 | import numpy as np
5 | from numpy.linalg import inv, norm, lstsq
6 | from numpy.linalg import matrix_rank as rank
7 | import caffe
8 | import time
9 | import cv2
10 |
11 | caffe.set_device(0)
12 | caffe.set_mode_gpu()
13 |
14 | class Net(object):
15 | def __init__(self, stage):
16 | self.caffemodel = './model/det{}.caffemodel'.format(stage)
17 | self.prototxt = './model/det{}.prototxt'.format(stage)
18 | self.net = caffe.Net(self.prototxt, self.caffemodel, caffe.TEST)
19 | self.input_size = None
20 | self.detect_thresh_value = None
21 |
22 | def bbreg(self, boundingbox, reg):
23 | #reg now is (numbox, 4), gemfield
24 | reg = reg.T
25 |
26 | w = boundingbox[:, 2] - boundingbox[:, 0] + 1
27 | h = boundingbox[:, 3] - boundingbox[:, 1] + 1
28 |
29 | bb0 = boundingbox[:, 0] + reg[:, 0] * w
30 | bb1 = boundingbox[:, 1] + reg[:, 1] * h
31 | bb2 = boundingbox[:, 2] + reg[:, 2] * w
32 | bb3 = boundingbox[:, 3] + reg[:, 3] * h
33 |
34 | boundingbox[:, 0:4] = np.array([bb0, bb1, bb2, bb3]).T
35 | return boundingbox
36 |
37 | def pad(self, boxesA, w, h):
38 | boxes = boxesA.copy()
39 | tmph = boxes[:, 3] - boxes[:, 1] + 1
40 | tmpw = boxes[:, 2] - boxes[:, 0] + 1
41 | numbox = boxes.shape[0]
42 |
43 | dx = np.ones(numbox)
44 | dy = np.ones(numbox)
45 | edx = tmpw
46 | edy = tmph
47 | x = boxes[:, 0:1][:, 0]
48 | y = boxes[:, 1:2][:, 0]
49 | ex = boxes[:, 2:3][:, 0]
50 | ey = boxes[:, 3:4][:, 0]
51 |
52 | tmp = np.where(ex > w)[0]
53 | if tmp.shape[0] != 0:
54 | edx[tmp] = -ex[tmp] + w - 1 + tmpw[tmp]
55 | ex[tmp] = w - 1
56 |
57 | tmp = np.where(ey > h)[0]
58 | if tmp.shape[0] != 0:
59 | edy[tmp] = -ey[tmp] + h - 1 + tmph[tmp]
60 | ey[tmp] = h - 1
61 |
62 | tmp = np.where(x < 1)[0]
63 | if tmp.shape[0] != 0:
64 | dx[tmp] = 2 - x[tmp]
65 | x[tmp] = np.ones_like(x[tmp])
66 |
67 | tmp = np.where(y < 1)[0]
68 | if tmp.shape[0] != 0:
69 | dy[tmp] = 2 - y[tmp]
70 | y[tmp] = np.ones_like(y[tmp])
71 |
72 | dy = np.maximum(0, dy - 1)
73 | dx = np.maximum(0, dx - 1)
74 | y = np.maximum(0, y - 1)
75 | x = np.maximum(0, x - 1)
76 | edy = np.maximum(0, edy - 1)
77 | edx = np.maximum(0, edx - 1)
78 | ey = np.maximum(0, ey - 1)
79 | ex = np.maximum(0, ex - 1)
80 | return [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph]
81 |
82 | def rerec(self, bboxA):
83 | w = bboxA[:, 2] - bboxA[:, 0]
84 | h = bboxA[:, 3] - bboxA[:, 1]
85 | l = np.maximum(w, h).T
86 | bboxA[:, 0] = bboxA[:, 0] + w * 0.5 - l * 0.5
87 | bboxA[:, 1] = bboxA[:, 1] + h * 0.5 - l * 0.5
88 | bboxA[:, 2:4] = bboxA[:, 0:2] + np.repeat([l], 2, axis=0).T
89 | return bboxA
90 |
91 | def nms(self, dets, thresh, type):
92 | x1 = dets[:, 0]
93 | y1 = dets[:, 1]
94 | x2 = dets[:, 2]
95 | y2 = dets[:, 3]
96 | scores = dets[:, 4]
97 |
98 | areas = (x2 - x1 + 1) * (y2 - y1 + 1)
99 | order = scores.argsort()[::-1]
100 | keep = []
101 | while order.size > 0:
102 | i = order[0]
103 | keep.append(i)
104 | xx1 = np.maximum(x1[i], x1[order[1:]])
105 | yy1 = np.maximum(y1[i], y1[order[1:]])
106 | xx2 = np.minimum(x2[i], x2[order[1:]])
107 | yy2 = np.minimum(y2[i], y2[order[1:]])
108 |
109 | w = np.maximum(0.0, xx2 - xx1 + 1)
110 | h = np.maximum(0.0, yy2 - yy1 + 1)
111 | inter = w * h
112 | if type == 'Min':
113 | ovr = inter / np.minimum(areas[i], areas[order[1:]])
114 | else:
115 | ovr = inter / (areas[i] + areas[order[1:]] - inter)
116 | inds = np.where(ovr <= thresh)[0]
117 | order = order[inds + 1]
118 |
119 | end = time.time()
120 | return keep
121 |
122 | def forward(self, d):
123 | total_boxes = d['boxes']
124 | numbox = total_boxes.shape[0]
125 | h = d['frame'].shape[0]
126 | w = d['frame'].shape[1]
127 |
128 | [dy, edy, dx, edx, y, ey, x, ex, tmpw,tmph] = self.pad(total_boxes, w, h)
129 | input_img = np.zeros((numbox, self.input_size, self.input_size, 3))
130 | for k in range(numbox):
131 | gemfield_tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
132 | gemfield_tmp[int(dy[k]):int(edy[k]) + 1, int(dx[k]):int(edx[k]) + 1] = d['frame'][int(y[k]):int(ey[k]) + 1, int(x[k]):int(ex[k]) + 1]
133 | input_img[k, :, :, :] = cv2.resize(gemfield_tmp, (self.input_size, self.input_size))
134 |
135 | input_img = (input_img - 127.5) / 128
136 | input_img = np.swapaxes(input_img, 1, 3)
137 | self.net.blobs['data'].reshape(numbox, 3, self.input_size, self.input_size)
138 | self.net.blobs['data'].data[...] = input_img
139 | out = self.net.forward()
140 | return out
141 |
142 | class PNet(Net):
143 | def __init__(self):
144 | super(PNet, self).__init__(1)
145 | self.detect_thresh_value = 0.8
146 |
147 | def generateBoundingBox(self, map, reg, scale):
148 | stride = 2
149 | cellsize = 12
150 | # y*x -> x*y
151 | map = map.T
152 |
153 | dx1 = reg[0, :, :].T
154 | dy1 = reg[1, :, :].T
155 | dx2 = reg[2, :, :].T
156 | dy2 = reg[3, :, :].T
157 |
158 | (x, y) = np.where(map >= self.detect_thresh_value)
159 | score = map[x, y]
160 | # reg shape is (4, numbox)
161 | reg = np.array([dx1[x, y], dy1[x, y], dx2[x, y], dy2[x, y]])
162 | # boundingbox shape is (933, 2)
163 | boundingbox = np.array([y, x]).T
164 | # mapping to original image, shape is (2, numbox)
165 | bb1 = np.fix((stride * (boundingbox) + 1) / scale).T
166 | # 12 pixel toward right and bottom, quare is generated, shape is (2, numbox), by gemfield
167 | bb2 = np.fix((stride * (boundingbox) + cellsize - 1 + 1) /scale).T
168 | # shape is (1, numbox), notice the [score], it reform (numbox,) to (1,numbox), by gemfield
169 | score = np.array([score])
170 | # (9, numbox), 2 + 2 + 1 + 4 = 9
171 | boundingbox_out = np.concatenate((bb1, bb2, score, reg), axis=0)
172 | return boundingbox_out.T
173 |
174 | def processOne(self, frame, scale):
175 | h = frame.shape[0]
176 | w = frame.shape[1]
177 | hs = int(np.ceil(h * scale))
178 | ws = int(np.ceil(w * scale))
179 | #RNet need greater than 11
180 | if hs < 11 or ws < 11:
181 | return np.array([])
182 |
183 | im_data = cv2.resize(frame, (ws, hs))
184 | im_data = (im_data - 127.5)/128
185 |
186 | # hwc -> cwh
187 | im_data = np.swapaxes(im_data, 0, 2)
188 |
189 | # PNet.blobs['data'] is caffe._caffe.Blob
190 | self.net.blobs['data'].reshape(1, 3, ws, hs)
191 | # c, w, h broadcast to 1, c, w, h
192 | self.net.blobs['data'].data[...] = np.array([im_data], dtype=np.float)
193 | # out is dict
194 | out = self.net.forward()
195 |
196 | # 0 means non face, 1 means face
197 | boxes = self.generateBoundingBox(out['prob1'][0, 1, :, :], out['conv4-2'][0], scale)
198 | if boxes.shape[0] != 0:
199 | pick = self.nms(boxes, 0.5, 'Union')
200 | if len(pick) > 0:
201 | boxes = boxes[pick, :]
202 | return boxes
203 |
204 | def process(self, d):
205 | frame = d['frame']
206 | for scale in d['scales']:
207 | boxes = self.processOne(frame, scale)
208 | if boxes.shape[0] != 0:
209 | d['boxes'] = np.concatenate((d['boxes'], boxes), axis=0)
210 |
211 | numbox = d['boxes'].shape[0]
212 |
213 | if numbox <= 0:
214 | return False
215 |
216 | total_boxes = d['boxes']
217 | pick = self.nms(total_boxes, 0.7, 'Union')
218 | total_boxes = total_boxes[pick, :]
219 |
220 | if total_boxes.shape[0] <= 0:
221 | return False
222 |
223 | total_boxes = self.bbreg(total_boxes[:,0:4], total_boxes[:,5:9].T )
224 | total_boxes = self.rerec(total_boxes)
225 | total_boxes = np.fix(total_boxes)
226 | d['boxes'] = total_boxes
227 | return True
228 |
229 | class RNet(Net):
230 | def __init__(self):
231 | super(RNet, self).__init__(2)
232 | self.input_size = 24
233 | self.detect_thresh_value = 0.9
234 |
235 | def process(self, d):
236 | out = self.forward(d)
237 | # score shape is (numbox,)
238 | score = out['prob1'][:, 1]
239 | pass_t = np.where(score > self.detect_thresh_value)[0]
240 | # score shape now is (numbox, 1)
241 | score = np.array([score[pass_t]]).T
242 | #total_boxes shape: (numbox, 5)
243 | total_boxes = d['boxes']
244 | total_boxes = np.concatenate((total_boxes[pass_t, 0:4], score), axis=1)
245 | #out['conv5-2'] shape is (numbox, 4), and mv shape is (4, numbox)
246 | mv = out['conv5-2'][pass_t, :].T
247 | if total_boxes.shape[0] <= 0:
248 | return False
249 |
250 | pick = self.nms(total_boxes, 0.7, 'Union')
251 | if len(pick) > 0:
252 | total_boxes = total_boxes[pick, :]
253 | total_boxes = self.bbreg(total_boxes, mv[:, pick])
254 | total_boxes = self.rerec(total_boxes)
255 | if total_boxes.shape[0] <= 0:
256 | return False
257 |
258 | total_boxes = np.fix(total_boxes)
259 | d['boxes'] = total_boxes
260 | return True
261 |
262 | class ONet(Net):
263 | def __init__(self):
264 | super(ONet, self).__init__(3)
265 | self.input_size = 48
266 | self.detect_thresh_value = 0.95
267 |
268 | def process(self, d):
269 | out = self.forward(d)
270 | score = out['prob1'][:, 1]
271 | points = out['conv6-3']
272 | pass_t = np.where(score > self.detect_thresh_value)[0]
273 | points = points[pass_t, :]
274 | score = np.array([score[pass_t]]).T
275 | total_boxes = d['boxes']
276 | total_boxes = np.concatenate((total_boxes[pass_t, 0:4], score), axis=1)
277 | mv = out['conv6-2'][pass_t, :].T
278 |
279 | w = total_boxes[:, 3] - total_boxes[:, 1] + 1
280 | h = total_boxes[:, 2] - total_boxes[:, 0] + 1
281 |
282 | points[:, 0:5] = np.tile(w, (5, 1)).T * points[:, 0:5] + np.tile(total_boxes[:, 0], (5, 1)).T - 1
283 | points[:, 5:10] = np.tile(h, (5, 1)).T * points[:, 5:10] + np.tile(total_boxes[:, 1], (5, 1)).T - 1
284 | if total_boxes.shape[0] <= 0:
285 | return False
286 |
287 | total_boxes = self.bbreg(total_boxes, mv[:, :])
288 | pick = self.nms(total_boxes, 0.7, 'Min')
289 | if len(pick) > 0:
290 | total_boxes = total_boxes[pick, :]
291 | points = points[pick, :]
292 | d['boxes'] = total_boxes
293 | d['points'] = points
294 | return True
295 |
296 | class Mtcnn(object):
297 | def __init__(self):
298 | self.nets = [PNet(), RNet(), ONet()]
299 | self.d = {}
300 | self.d['scales'] = [0.3, 0.15, 0.075]
301 |
302 | def preprocess(self, d):
303 | frame = cv2.cvtColor(d['frame'], cv2.COLOR_BGR2RGB)
304 | d['frame'] = frame.astype(float)
305 |
306 | def postprocess(self, d):
307 | self.d['boxes'] = np.maximum(d['boxes'], 0)
308 |
309 | def process(self, frame):
310 | self.d['frame'] = frame
311 | self.d['boxes'] = np.zeros((0, 9), np.float)
312 | self.d['points'] = np.zeros((1, 1))
313 |
314 | self.preprocess(self.d)
315 |
316 | for net in self.nets:
317 | rc = net.process(self.d)
318 | if not rc:
319 | return False
320 | self.postprocess(self.d)
321 | return True
322 |
323 | def drawBoxes(self, frame, imgname):
324 | boxes = self.d['boxes']
325 | points = self.d['points']
326 | x1 = boxes[:, 0]
327 | y1 = boxes[:, 1]
328 | x2 = boxes[:, 2]
329 | y2 = boxes[:, 3]
330 | for i in range(x1.shape[0]):
331 | rightEyeCenter = (points[i][0], points[i][5])
332 | leftEyeCenter = (points[i][1], points[i][6])
333 | p3 = (points[i][2], points[i][7])
334 | p4 = (points[i][3], points[i][8])
335 | p5 = (points[i][4], points[i][9])
336 | cv2.rectangle(frame, (int(x1[i]), int(y1[i])),(int(x2[i]), int(y2[i])), (0, 255, 0), 1)
337 | cv2.circle(frame, rightEyeCenter, 2, (0, 0, 255), 3)
338 | cv2.circle(frame, leftEyeCenter, 2, (0, 255, 0), 3)
339 | cv2.circle(frame, p3, 2, (255, 0, 0), 3)
340 | cv2.circle(frame, p4, 2, (255, 255, 0), 3)
341 | cv2.circle(frame, p5, 2, (0, 255, 255), 3)
342 |
343 | write_path = './results/{}/bbox/'.format( time.strftime("%Y%m%d", time.localtime()) )
344 | if not os.path.exists(write_path):
345 | os.makedirs(write_path)
346 | cv2.imwrite('{}{}.jpg'.format(write_path, imgname), frame)
347 |
348 | class AlignFace(object):
349 | def __init__(self):
350 | # reference facial points, a list of coordinates (x,y)
351 | self.REFERENCE_FACIAL_POINTS_96x112 = [
352 | [30.29459953, 51.69630051],
353 | [65.53179932, 51.50139999],
354 | [48.02519989, 71.73660278],
355 | [33.54930115, 92.3655014],
356 | [62.72990036, 92.20410156]
357 | ]
358 | # made by gemfield
359 | self.REFERENCE_FACIAL_POINTS_112x112 = [
360 | [38.29459953, 51.69630051],
361 | [73.53179932, 51.50139999],
362 | [56.02519989, 71.73660278],
363 | [41.54930115, 92.3655014 ],
364 | [70.72990036, 92.20410156]
365 | ]
366 |
367 | def __call__(self, frame, facial_5pts):
368 | # shape from (10,) to (2, 5)
369 | facial_5pts = np.reshape(facial_5pts, (2, -1))
370 | dst_img = self.warpAndCrop(frame, facial_5pts, (112, 112))
371 | return dst_img
372 |
373 | def warpAndCrop(self, src_img, facial_pts, crop_size):
374 | reference_pts = self.REFERENCE_FACIAL_POINTS_112x112
375 | ref_pts = np.float32(reference_pts)
376 | ref_pts_shp = ref_pts.shape
377 |
378 | if ref_pts_shp[0] == 2:
379 | ref_pts = ref_pts.T
380 |
381 | src_pts = np.float32(facial_pts)
382 | src_pts_shp = src_pts.shape
383 | if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
384 | raise Exception('facial_pts.shape must be (K,2) or (2,K) and K>2')
385 | # 2*5 to 5*2
386 | if src_pts_shp[0] == 2:
387 | src_pts = src_pts.T
388 |
389 | if src_pts.shape != ref_pts.shape:
390 | raise Exception('facial_pts and reference_pts must have the same shape: {} vs {}'.format(src_pts.shape, ref_pts.shape) )
391 |
392 | tfm = self.getAffineTransform(src_pts, ref_pts)
393 |
394 | face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
395 | return face_img
396 |
397 | def getAffineTransform(self, uv, xy):
398 | options = {'K': 2}
399 | # Solve for trans1
400 | trans1, trans1_inv = self.findNonreflectiveSimilarity(uv, xy, options)
401 | # manually reflect the xy data across the Y-axis
402 | xyR = xy
403 | xyR[:, 0] = -1 * xyR[:, 0]
404 |
405 | trans2r, trans2r_inv = self.findNonreflectiveSimilarity(uv, xyR, options)
406 |
407 | # manually reflect the tform to undo the reflection done on xyR
408 | TreflectY = np.array([
409 | [-1, 0, 0],
410 | [0, 1, 0],
411 | [0, 0, 1]
412 | ])
413 |
414 | trans2 = np.dot(trans2r, TreflectY)
415 |
416 | # Figure out if trans1 or trans2 is better
417 | xy1 = self.tformfwd(trans1, uv)
418 | norm1 = norm(xy1 - xy)
419 |
420 | xy2 = self.tformfwd(trans2, uv)
421 | norm2 = norm(xy2 - xy)
422 |
423 | if norm1 <= norm2:
424 | trans = trans1
425 | else:
426 | trans2_inv = inv(trans2)
427 | trans = trans2
428 |
429 | cv2_trans = trans[:, 0:2].T
430 | return cv2_trans
431 |
432 | def findNonreflectiveSimilarity(self, uv, xy, options=None):
433 | options = {'K': 2}
434 |
435 | K = options['K']
436 | M = xy.shape[0]
437 | x = xy[:, 0].reshape((-1, 1))
438 | y = xy[:, 1].reshape((-1, 1))
439 |
440 | tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))
441 | tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))
442 | X = np.vstack((tmp1, tmp2))
443 |
444 | u = uv[:, 0].reshape((-1, 1))
445 | v = uv[:, 1].reshape((-1, 1))
446 | U = np.vstack((u, v))
447 |
448 | if rank(X) >= 2 * K:
449 | r, _, _, _ = lstsq(X, U, rcond=-1)
450 | r = np.squeeze(r)
451 | else:
452 | raise Exception('cp2tform:twoUniquePointsReq')
453 | sc = r[0]
454 | ss = r[1]
455 | tx = r[2]
456 | ty = r[3]
457 |
458 | Tinv = np.array([
459 | [sc, -ss, 0],
460 | [ss, sc, 0],
461 | [tx, ty, 1]
462 | ])
463 |
464 | T = inv(Tinv)
465 | T[:, 2] = np.array([0, 0, 1])
466 | return T, Tinv
467 |
468 | def tformfwd(self, trans, uv):
469 | uv = np.hstack((
470 | uv, np.ones((uv.shape[0], 1))
471 | ))
472 | xy = np.dot(uv, trans)
473 | xy = xy[:, 0:-1]
474 | return xy
475 |
476 | def drawBoxes(self, img, imgname):
477 | write_path = './results/{}/bbox/'.format( time.strftime("%Y%m%d", time.localtime()) )
478 | if not os.path.exists(write_path):
479 | os.makedirs(write_path)
480 | cv2.imwrite('{}{}_aligned.jpg'.format(write_path, imgname), img)
481 |
482 | #### gemfield test phase ####
483 | video_path = 'gemfield.mp4'
484 | if __name__ == '__main__':
485 | if len(sys.argv) == 2:
486 | video_path = sys.argv[1]
487 |
488 | if not os.path.exists(video_path):
489 | print('File not found: {}'.format(video_path))
490 | sys.exit(1)
491 |
492 | videoCapture = cv2.VideoCapture(video_path)
493 | status, frame = videoCapture.read()
494 | mtcnn = Mtcnn()
495 | alignFace = AlignFace()
496 | frame_num = 0
497 | while status:
498 | frame_num += 1
499 | print('process {}'.format(frame_num))
500 | rc = mtcnn.process(frame)
501 | if not rc:
502 | status, frame = videoCapture.read()
503 | continue
504 | mtcnn.drawBoxes(frame, frame_num)
505 | points = mtcnn.d['points']
506 | if points.shape[0] > 0:
507 | for i in range(points.shape[0]):
508 | #gemfield.org: WARNING! THE IMG WILL CONTAIN THE MARK drew earlier!
509 | alignFace.drawBoxes(alignFace(frame, points[i,:]), frame_num)
510 | status, frame = videoCapture.read()
511 | else:
512 | print('End for some reasons...')
513 |
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