50 |
56 |
57 |
94 |
95 |
96 |
97 |
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/classifier/predict.py:
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1 | from datetime import datetime
2 | import logging
3 | import os
4 |
5 | from urllib.request import urlopen
6 | from PIL import Image
7 | import tensorflow as tf
8 | import numpy as np
9 |
10 |
11 | scriptpath = os.path.abspath(__file__)
12 | scriptdir = os.path.dirname(scriptpath)
13 | filename = os.path.join(scriptdir, 'model.pb')
14 | labels_filename = os.path.join(scriptdir, 'labels.txt')
15 |
16 |
17 | output_layer = 'loss:0'
18 | input_node = 'Placeholder:0'
19 |
20 | graph_def = tf.GraphDef()
21 | labels = []
22 | network_input_size = 0
23 |
24 | def _initialize():
25 | global labels, network_input_size
26 | if not labels:
27 | with tf.io.gfile.GFile(filename, 'rb') as f:
28 | graph_def.ParseFromString(f.read())
29 | tf.import_graph_def(graph_def, name='')
30 | with open(labels_filename, 'rt') as lf:
31 | labels = [l.strip() for l in lf.readlines()]
32 | with tf.compat.v1.Session() as sess:
33 | input_tensor_shape = sess.graph.get_tensor_by_name('Placeholder:0').shape.as_list()
34 | network_input_size = input_tensor_shape[1]
35 | logging.info('network_input_size = ' + str(network_input_size))
36 |
37 | def _log_msg(msg):
38 | logging.info("{}: {}".format(datetime.now(),msg))
39 |
40 | def _extract_bilinear_pixel(img, x, y, ratio, xOrigin, yOrigin):
41 | xDelta = (x + 0.5) * ratio - 0.5
42 | x0 = int(xDelta)
43 | xDelta -= x0
44 | x0 += xOrigin
45 | if x0 < 0:
46 | x0 = 0;
47 | x1 = 0;
48 | xDelta = 0.0;
49 | elif x0 >= img.shape[1]-1:
50 | x0 = img.shape[1]-1;
51 | x1 = img.shape[1]-1;
52 | xDelta = 0.0;
53 | else:
54 | x1 = x0 + 1;
55 |
56 | yDelta = (y + 0.5) * ratio - 0.5
57 | y0 = int(yDelta)
58 | yDelta -= y0
59 | y0 += yOrigin
60 | if y0 < 0:
61 | y0 = 0;
62 | y1 = 0;
63 | yDelta = 0.0;
64 | elif y0 >= img.shape[0]-1:
65 | y0 = img.shape[0]-1;
66 | y1 = img.shape[0]-1;
67 | yDelta = 0.0;
68 | else:
69 | y1 = y0 + 1;
70 |
71 | #Get pixels in four corners
72 | bl = img[y0, x0]
73 | br = img[y0, x1]
74 | tl = img[y1, x0]
75 | tr = img[y1, x1]
76 | #Calculate interpolation
77 | b = xDelta * br + (1. - xDelta) * bl
78 | t = xDelta * tr + (1. - xDelta) * tl
79 | pixel = yDelta * t + (1. - yDelta) * b
80 | return pixel.astype(np.uint8)
81 |
82 | def _extract_and_resize(img, targetSize):
83 | determinant = img.shape[1] * targetSize[0] - img.shape[0] * targetSize[1]
84 | if determinant < 0:
85 | ratio = float(img.shape[1]) / float(targetSize[1])
86 | xOrigin = 0
87 | yOrigin = int(0.5 * (img.shape[0] - ratio * targetSize[0]))
88 | elif determinant > 0:
89 | ratio = float(img.shape[0]) / float(targetSize[0])
90 | xOrigin = int(0.5 * (img.shape[1] - ratio * targetSize[1]))
91 | yOrigin = 0
92 | else:
93 | ratio = float(img.shape[0]) / float(targetSize[0])
94 | xOrigin = 0
95 | yOrigin = 0
96 | resize_image = np.empty((targetSize[0], targetSize[1], img.shape[2]), dtype=np.uint8)
97 | for y in range(targetSize[0]):
98 | for x in range(targetSize[1]):
99 | resize_image[y, x] = _extract_bilinear_pixel(img, x, y, ratio, xOrigin, yOrigin)
100 | return resize_image
101 |
102 | def _extract_and_resize_to_256_square(image):
103 | h, w = image.shape[:2]
104 | _log_msg("extract_and_resize_to_256_square: " + str(w) + "x" + str(h) +" and resize to " + str(256) + "x" + str(256))
105 | return _extract_and_resize(image, (256, 256))
106 |
107 | def _crop_center(img,cropx,cropy):
108 | h, w = img.shape[:2]
109 | startx = max(0, w//2-(cropx//2) - 1)
110 | starty = max(0, h//2-(cropy//2) - 1)
111 | _log_msg("crop_center: " + str(w) + "x" + str(h) +" to " + str(cropx) + "x" + str(cropy))
112 | return img[starty:starty+cropy, startx:startx+cropx]
113 |
114 | def _resize_down_to_1600_max_dim(image):
115 | w,h = image.size
116 | if h < 1600 and w < 1600:
117 | return image
118 |
119 | new_size = (1600 * w // h, 1600) if (h > w) else (1600, 1600 * h // w)
120 | _log_msg("resize: " + str(w) + "x" + str(h) + " to " + str(new_size[0]) + "x" + str(new_size[1]))
121 | if max(new_size) / max(image.size) >= 0.5:
122 | method = Image.BILINEAR
123 | else:
124 | method = Image.BICUBIC
125 | return image.resize(new_size, method)
126 |
127 | def _convert_to_nparray(image):
128 | # RGB -> BGR
129 | _log_msg("Convert to numpy array")
130 | image = np.array(image)
131 | return image[:, :, (2,1,0)]
132 |
133 | def _update_orientation(image):
134 | exif_orientation_tag = 0x0112
135 | if hasattr(image, '_getexif'):
136 | exif = image._getexif()
137 | if exif != None and exif_orientation_tag in exif:
138 | orientation = exif.get(exif_orientation_tag, 1)
139 | _log_msg('Image has EXIF Orientation: ' + str(orientation))
140 | # orientation is 1 based, shift to zero based and flip/transpose based on 0-based values
141 | orientation -= 1
142 | if orientation >= 4:
143 | image = image.transpose(Image.TRANSPOSE)
144 | if orientation == 2 or orientation == 3 or orientation == 6 or orientation == 7:
145 | image = image.transpose(Image.FLIP_TOP_BOTTOM)
146 | if orientation == 1 or orientation == 2 or orientation == 5 or orientation == 6:
147 | image = image.transpose(Image.FLIP_LEFT_RIGHT)
148 | return image
149 |
150 | def _predict_image(image):
151 | try:
152 | if image.mode != "RGB":
153 | _log_msg("Converting to RGB")
154 | image.convert("RGB")
155 |
156 | w,h = image.size
157 | _log_msg("Image size: " + str(w) + "x" + str(h))
158 |
159 | # Update orientation based on EXIF tags
160 | image = _update_orientation(image)
161 |
162 | # If the image has either w or h greater than 1600 we resize it down respecting
163 | # aspect ratio such that the largest dimention is 1600
164 | image = _resize_down_to_1600_max_dim(image)
165 |
166 | # Convert image to numpy array
167 | image = _convert_to_nparray(image)
168 |
169 | # Crop the center square and resize that square down to 256x256
170 | resized_image = _extract_and_resize_to_256_square(image)
171 |
172 | # Crop the center for the specified network_input_Size
173 | cropped_image = _crop_center(resized_image, network_input_size, network_input_size)
174 |
175 | tf.compat.v1.reset_default_graph()
176 | tf.import_graph_def(graph_def, name='')
177 |
178 | with tf.compat.v1.Session() as sess:
179 | prob_tensor = sess.graph.get_tensor_by_name(output_layer)
180 | predictions, = sess.run(prob_tensor, {input_node: [cropped_image] })
181 |
182 | result = []
183 | highest_prediction = None
184 | for p, label in zip(predictions, labels):
185 | truncated_probablity = np.float64(round(p,8))
186 | if truncated_probablity > 1e-8:
187 | prediction = {
188 | 'tagName': label,
189 | 'probability': truncated_probablity }
190 | result.append(prediction)
191 | if not highest_prediction or prediction['probability'] > highest_prediction['probability']:
192 | highest_prediction = prediction
193 |
194 | response = {
195 | 'created': datetime.utcnow().isoformat(),
196 | 'predictedTagName': highest_prediction['tagName'],
197 | 'prediction': result
198 | }
199 |
200 | _log_msg("Results: " + str(response))
201 | return response
202 |
203 | except Exception as e:
204 | _log_msg(str(e))
205 | return 'Error: Could not preprocess image for prediction. ' + str(e)
206 |
207 | def predict_image_from_url(image_url):
208 | logging.info("Predicting from url: " + image_url)
209 |
210 | _initialize()
211 |
212 | with urlopen(image_url) as testImage:
213 | image = Image.open(testImage)
214 | return _predict_image(image)
215 |
--------------------------------------------------------------------------------
/frontend/.gitignore:
--------------------------------------------------------------------------------
1 | ## Ignore Visual Studio temporary files, build results, and
2 | ## files generated by popular Visual Studio add-ons.
3 | ##
4 | ## Get latest from https://github.com/github/gitignore/blob/main/VisualStudio.gitignore
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17 | mono_crash.*
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24 | x64/
25 | x86/
26 | [Ww][Ii][Nn]32/
27 | [Aa][Rr][Mm]/
28 | [Aa][Rr][Mm]64/
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33 | [Ll]ogs/
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45 | [Bb]uild[Ll]og.*
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48 | *.VisualState.xml
49 | TestResult.xml
50 | nunit-*.xml
51 |
52 | # Build Results of an ATL Project
53 | [Dd]ebugPS/
54 | [Rr]eleasePS/
55 | dlldata.c
56 |
57 | # Benchmark Results
58 | BenchmarkDotNet.Artifacts/
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62 | project.fragment.lock.json
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243 | # Including strong name files can present a security risk
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314 | _Pvt_Extensions
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399 |
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90 |
83 | ![]()
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87 | 😸 Cat
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