Hugging Face's logo

Spaces:
TazinMorshed
/
visual-pollution-recognition
Sleeping

App Files Community
Tazin commited on
Commit
ab6be4c
·
1 Parent(s): 04ff44d

model, interface, example added

Browse files
Files changed (8) hide show
  1. app.ipynb +316 -0
  2. app.py +36 -4
  3. models/vispol-1-recognizer-v0.pkl +3 -0
  4. test_images/unknown_00.jpg +0 -0
  5. test_images/unknown_01.jpg +0 -0
  6. test_images/unknown_02.jpg +0 -0
  7. test_images/unknown_03.jpg +0 -0
  8. test_images/unknown_04.jpg +0 -0
app.ipynb ADDED
@@ -0,0 +1,316 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "nbformat": 4,
3
+ "nbformat_minor": 0,
4
+ "metadata": {
5
+ "colab": {
6
+ "provenance": []
7
+ },
8
+ "kernelspec": {
9
+ "name": "python3",
10
+ "display_name": "Python 3"
11
+ },
12
+ "language_info": {
13
+ "name": "python"
14
+ }
15
+ },
16
+ "cells": [
17
+ {
18
+ "cell_type": "code",
19
+ "execution_count": 1,
20
+ "metadata": {
21
+ "colab": {
22
+ "base_uri": "https://localhost:8080/"
23
+ },
24
+ "id": "SLPnhvWY-foG",
25
+ "outputId": "3a985b54-3d5d-4418-e3b7-46b198b74c3b"
26
+ },
27
+ "outputs": [
28
+ {
29
+ "output_type": "stream",
30
+ "name": "stdout",
31
+ "text": [
32
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.2/14.2 MB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
33
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.1/64.1 KB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
34
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
35
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.9/56.9 KB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
36
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
37
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.8/55.8 KB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
38
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 KB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
39
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 KB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
40
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.0/107.0 KB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
41
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.5/270.5 KB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
42
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
43
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.1/58.1 KB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
44
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.0/79.0 KB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
45
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.3/64.3 KB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
46
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.6/80.6 KB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
47
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.6/69.6 KB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
48
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 KB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
49
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 KB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
50
+ "\u001b[2K \u001b[90m━━━━━���━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m33.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
51
+ "\u001b[?25h Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
52
+ " Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
53
+ ]
54
+ }
55
+ ],
56
+ "source": [
57
+ "!pip install -Uqq fastai gradio nbdev"
58
+ ]
59
+ },
60
+ {
61
+ "cell_type": "code",
62
+ "source": [
63
+ "from fastai.vision.all import *"
64
+ ],
65
+ "metadata": {
66
+ "id": "92Ww4uKl-iaq"
67
+ },
68
+ "execution_count": 2,
69
+ "outputs": []
70
+ },
71
+ {
72
+ "cell_type": "code",
73
+ "source": [
74
+ "#!export\n",
75
+ "from fastai.vision.all import load_learner\n",
76
+ "import gradio as gr"
77
+ ],
78
+ "metadata": {
79
+ "id": "Lqz3L-Zj-jH6"
80
+ },
81
+ "execution_count": 3,
82
+ "outputs": []
83
+ },
84
+ {
85
+ "cell_type": "code",
86
+ "source": [
87
+ "%cd /content/drive/MyDrive/Vispol_classify"
88
+ ],
89
+ "metadata": {
90
+ "colab": {
91
+ "base_uri": "https://localhost:8080/"
92
+ },
93
+ "id": "KTkTrH_O-y8g",
94
+ "outputId": "bf9c1a71-5f9f-44bf-84bd-0e631d152cda"
95
+ },
96
+ "execution_count": 5,
97
+ "outputs": [
98
+ {
99
+ "output_type": "stream",
100
+ "name": "stdout",
101
+ "text": [
102
+ "/content/drive/MyDrive/Vispol_classify\n"
103
+ ]
104
+ }
105
+ ]
106
+ },
107
+ {
108
+ "cell_type": "code",
109
+ "source": [
110
+ "model = load_learner('models/vispol-1-recognizer-v0.pkl')"
111
+ ],
112
+ "metadata": {
113
+ "id": "l21UvfNB-jn8"
114
+ },
115
+ "execution_count": 6,
116
+ "outputs": []
117
+ },
118
+ {
119
+ "cell_type": "code",
120
+ "source": [
121
+ "#!export\n",
122
+ "pollutant_labels = (\n",
123
+ " \"antennas\",\n",
124
+ " \"billboard\",\n",
125
+ " \"broken roads\",\n",
126
+ " \"construction sites\",\n",
127
+ " \"electric pole\",\n",
128
+ " \"garbage can\",\n",
129
+ " \"graffiti\",\n",
130
+ " \"smog\",\n",
131
+ " \"street litter\"\n",
132
+ ")\n",
133
+ "\n",
134
+ "def recognize_image(image):\n",
135
+ " pred, idx, probs = model.predict(image)\n",
136
+ " return dict(zip(pollutant_labels, map(float, probs)))"
137
+ ],
138
+ "metadata": {
139
+ "id": "-vFaZXBG-kdK"
140
+ },
141
+ "execution_count": 15,
142
+ "outputs": []
143
+ },
144
+ {
145
+ "cell_type": "code",
146
+ "source": [
147
+ "img = PILImage.create(f'test_images/poles.jpg')\n",
148
+ "img.thumbnail((256,256))\n",
149
+ "img"
150
+ ],
151
+ "metadata": {
152
+ "colab": {
153
+ "base_uri": "https://localhost:8080/",
154
+ "height": 273
155
+ },
156
+ "id": "J60amehr_BSm",
157
+ "outputId": "3322e464-3c4a-44ef-ed8d-048ecf7de088"
158
+ },
159
+ "execution_count": 18,
160
+ "outputs": [
161
+ {
162
+ "output_type": "execute_result",
163
+ "data": {
164
+ "text/plain": [
165
+ "PILImage mode=RGB size=170x256"
166
+ ],
167
+ "image/png": "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\n"
168
+ },
169
+ "metadata": {},
170
+ "execution_count": 18
171
+ }
172
+ ]
173
+ },
174
+ {
175
+ "cell_type": "code",
176
+ "source": [
177
+ "recognize_image(img)"
178
+ ],
179
+ "metadata": {
180
+ "colab": {
181
+ "base_uri": "https://localhost:8080/",
182
+ "height": 181
183
+ },
184
+ "id": "hwKGWNvF_GYW",
185
+ "outputId": "36ef7765-1cec-430a-d65e-050943149d16"
186
+ },
187
+ "execution_count": 19,
188
+ "outputs": [
189
+ {
190
+ "output_type": "display_data",
191
+ "data": {
192
+ "text/plain": [
193
+ "<IPython.core.display.HTML object>"
194
+ ],
195
+ "text/html": [
196
+ "\n",
197
+ "<style>\n",
198
+ " /* Turns off some styling */\n",
199
+ " progress {\n",
200
+ " /* gets rid of default border in Firefox and Opera. */\n",
201
+ " border: none;\n",
202
+ " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
203
+ " background-size: auto;\n",
204
+ " }\n",
205
+ " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
206
+ " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
207
+ " }\n",
208
+ " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
209
+ " background: #F44336;\n",
210
+ " }\n",
211
+ "</style>\n"
212
+ ]
213
+ },
214
+ "metadata": {}
215
+ },
216
+ {
217
+ "output_type": "display_data",
218
+ "data": {
219
+ "text/plain": [
220
+ "<IPython.core.display.HTML object>"
221
+ ],
222
+ "text/html": []
223
+ },
224
+ "metadata": {}
225
+ },
226
+ {
227
+ "output_type": "execute_result",
228
+ "data": {
229
+ "text/plain": [
230
+ "{'antennas': 0.00038570791366510093,\n",
231
+ " 'billboard': 3.700462184497155e-05,\n",
232
+ " 'broken roads': 4.4147591893306526e-07,\n",
233
+ " 'construction sites': 0.00022819564037490636,\n",
234
+ " 'electric pole': 0.9993365406990051,\n",
235
+ " 'garbage can': 9.962874401026056e-07,\n",
236
+ " 'graffiti': 7.621663939971768e-07,\n",
237
+ " 'smog': 8.581743713875767e-06,\n",
238
+ " 'street litter': 1.71208023402869e-06}"
239
+ ]
240
+ },
241
+ "metadata": {},
242
+ "execution_count": 19
243
+ }
244
+ ]
245
+ },
246
+ {
247
+ "cell_type": "code",
248
+ "source": [
249
+ "#!export\n",
250
+ "image = gr.inputs.Image(shape=(256,256))\n",
251
+ "label = gr.outputs.Label()\n",
252
+ "examples = [\n",
253
+ " 'test_images/unknown_00.jpg',\n",
254
+ " 'test_images/unknown_01.jpg',\n",
255
+ " 'test_images/unknown_02.jpg',\n",
256
+ " 'test_images/unknown_03.jpg',\n",
257
+ " 'test_images/unknown_04.jpg'\n",
258
+ " ]\n",
259
+ "\n",
260
+ "iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples)\n",
261
+ "iface.launch(inline=False, share=True)"
262
+ ],
263
+ "metadata": {
264
+ "colab": {
265
+ "base_uri": "https://localhost:8080/"
266
+ },
267
+ "id": "zjJmvT71_H5V",
268
+ "outputId": "502df66b-5873-4f47-dc0f-d3cd0963dd1c"
269
+ },
270
+ "execution_count": 21,
271
+ "outputs": [
272
+ {
273
+ "output_type": "stream",
274
+ "name": "stderr",
275
+ "text": [
276
+ "/usr/local/lib/python3.8/dist-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
277
+ " warnings.warn(\n",
278
+ "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
279
+ " warnings.warn(value)\n",
280
+ "/usr/local/lib/python3.8/dist-packages/gradio/outputs.py:197: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
281
+ " warnings.warn(\n",
282
+ "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
283
+ " warnings.warn(value)\n"
284
+ ]
285
+ },
286
+ {
287
+ "output_type": "stream",
288
+ "name": "stdout",
289
+ "text": [
290
+ "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
291
+ "Running on public URL: https://354f0c36-f95a-4d9b.gradio.live\n",
292
+ "\n",
293
+ "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
294
+ ]
295
+ },
296
+ {
297
+ "output_type": "execute_result",
298
+ "data": {
299
+ "text/plain": []
300
+ },
301
+ "metadata": {},
302
+ "execution_count": 21
303
+ }
304
+ ]
305
+ },
306
+ {
307
+ "cell_type": "code",
308
+ "source": [],
309
+ "metadata": {
310
+ "id": "TGDuTd7ZBTk2"
311
+ },
312
+ "execution_count": null,
313
+ "outputs": []
314
+ }
315
+ ]
316
+ }
app.py CHANGED
@@ -1,7 +1,39 @@
 
1
  import gradio as gr
2
 
3
- def greet(name):
4
- return "Hello " + name + "!!"
 
5
 
6
- iface = gr.Interface(fn=greet, inputs="text", outputs="text")
7
- iface.launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from fastai.vision.all import *
2
  import gradio as gr
3
 
4
+ # import pathlib
5
+ # temp = pathlib.PosixPath
6
+ # pathlib.PosixPath = pathlib.WindowsPath
7
 
8
+ #!export
9
+ pollutant_labels = (
10
+ "antennas",
11
+ "billboard",
12
+ "broken roads",
13
+ "construction sites",
14
+ "electric pole",
15
+ "garbage can",
16
+ "graffiti",
17
+ "smog",
18
+ "street litter"
19
+ )
20
+
21
+ model = load_learner('models/vispol-1-recognizer-v0.pkl')
22
+
23
+ def recognize_image(image):
24
+ pred, idx, probs = model.predict(image)
25
+ return dict(zip(pollutant_labels, map(float, probs)))
26
+
27
+ #!export
28
+ image = gr.inputs.Image(shape=(256,256))
29
+ label = gr.outputs.Label()
30
+ examples = [
31
+ 'unknown_00.jpg',
32
+ 'unknown_01.jpg',
33
+ 'unknown_02.jpg',
34
+ 'unknown_03.jpg',
35
+ 'unknown_04.jpg'
36
+ ]
37
+
38
+ iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples)
39
+ iface.launch(inline=False, share=True)
models/vispol-1-recognizer-v0.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:33995e3571e4173b2b3684c25e6ff09f6b5651539abcb2ee1114db3823575f36
3
+ size 103099018
test_images/unknown_00.jpg ADDED
test_images/unknown_01.jpg ADDED
test_images/unknown_02.jpg ADDED
test_images/unknown_03.jpg ADDED
test_images/unknown_04.jpg ADDED