Spaces:
Sleeping
Sleeping
add pace model training notebook
Browse files- .gitignore +2 -0
- app.py +48 -10
- notebooks/PaceModel.ipynb +584 -0
.gitignore
CHANGED
@@ -3,3 +3,5 @@ __pycache__
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*.jpg
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*.png
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*.jpg
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*.png
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*.log
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app.py
CHANGED
@@ -18,9 +18,17 @@ class AudioPalette:
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self.image_captioning = ImageCaptioning()
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def generate(self, input_image: PIL.Image.Image):
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-
generated_text = self.image_captioning.query(input_image)[0].get("generated_text")
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pace = self.pace_model.predict(input_image)
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-
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def main():
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model = AudioPalette()
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@@ -33,14 +41,44 @@ def main():
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show_label=True,
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container=True
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),
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outputs=
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cache_examples=False,
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live=False,
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title="Audio Palette",
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self.image_captioning = ImageCaptioning()
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def generate(self, input_image: PIL.Image.Image):
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pace = self.pace_model.predict(input_image)
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print("Pace Prediction Done")
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generated_text = self.image_captioning.query(input_image)[0].get("generated_text")
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print("Captioning Done")
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generated_text = generated_text if generated_text is not None else ""
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temp = pace + " - " + generated_text
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outputs = [temp, pace, generated_text]
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return outputs
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def main():
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model = AudioPalette()
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show_label=True,
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container=True
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),
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outputs=[
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gr.Textbox(
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lines=1,
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placeholder="Pace of the image and the caption",
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label="Caption and Pace",
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show_label=True,
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container=True,
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type="text",
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visible=True
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),
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gr.Textbox(
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lines=1,
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placeholder="Pace of the image",
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label="Pace",
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show_label=True,
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container=True,
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type="text",
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visible=False
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),
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gr.Textbox(
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lines=1,
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placeholder="Caption for the image",
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label="Caption",
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show_label=True,
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container=True,
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type="text",
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visible=False
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),
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# gr.Audio(
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# label="Generated Audio",
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# show_label=True,
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# container=True,
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# visible=False,
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# format="wav",
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# autoplay=False,
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# show_download_button=True,
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# )
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],
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cache_examples=False,
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live=False,
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title="Audio Palette",
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notebooks/PaceModel.ipynb
ADDED
@@ -0,0 +1,584 @@
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1 |
+
{
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2 |
+
"nbformat": 4,
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3 |
+
"nbformat_minor": 0,
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4 |
+
"metadata": {
|
5 |
+
"colab": {
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6 |
+
"provenance": [],
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7 |
+
"gpuType": "T4"
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8 |
+
},
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9 |
+
"kernelspec": {
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10 |
+
"name": "python3",
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11 |
+
"display_name": "Python 3"
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12 |
+
},
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13 |
+
"language_info": {
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14 |
+
"name": "python"
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15 |
+
},
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16 |
+
"accelerator": "GPU"
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17 |
+
},
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18 |
+
"cells": [
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19 |
+
{
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20 |
+
"cell_type": "code",
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21 |
+
"source": [
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22 |
+
"import json\n",
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23 |
+
"import shutil\n",
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24 |
+
"from pathlib import Path\n",
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25 |
+
"from keras.applications.resnet50 import ResNet50\n",
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26 |
+
"\n",
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27 |
+
"import cv2\n",
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28 |
+
"import matplotlib.pyplot as plt\n",
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29 |
+
"import pandas as pd\n",
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30 |
+
"\n",
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31 |
+
"from google.colab import files\n",
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32 |
+
"from google.colab.patches import cv2_imshow"
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33 |
+
],
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34 |
+
"metadata": {
|
35 |
+
"id": "wzZknIqDEwBg"
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36 |
+
},
|
37 |
+
"execution_count": null,
|
38 |
+
"outputs": []
|
39 |
+
},
|
40 |
+
{
|
41 |
+
"cell_type": "code",
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42 |
+
"source": [
|
43 |
+
"kaggle_token = Path(\"/root/.kaggle/kaggle.json\")\n",
|
44 |
+
"if not kaggle_token.parent.exists():\n",
|
45 |
+
" kaggle_token.parent.mkdir()\n",
|
46 |
+
"if not kaggle_token.exists():\n",
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47 |
+
" print(\"Upload token:\")\n",
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48 |
+
" files.upload()\n",
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49 |
+
" shutil.move((Path.cwd() / \"kaggle.json\").as_posix(), kaggle_token.resolve().as_posix())"
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50 |
+
],
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51 |
+
"metadata": {
|
52 |
+
"id": "c0SGUkuUEy6n"
|
53 |
+
},
|
54 |
+
"execution_count": null,
|
55 |
+
"outputs": []
|
56 |
+
},
|
57 |
+
{
|
58 |
+
"cell_type": "code",
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59 |
+
"source": [
|
60 |
+
"!chmod 600 /root/.kaggle/kaggle.json"
|
61 |
+
],
|
62 |
+
"metadata": {
|
63 |
+
"id": "yufGnL24E0gf"
|
64 |
+
},
|
65 |
+
"execution_count": null,
|
66 |
+
"outputs": []
|
67 |
+
},
|
68 |
+
{
|
69 |
+
"cell_type": "code",
|
70 |
+
"source": [
|
71 |
+
"!kaggle d download srbhshinde/flickr8k-sau"
|
72 |
+
],
|
73 |
+
"metadata": {
|
74 |
+
"id": "84kESXJrE7Zn"
|
75 |
+
},
|
76 |
+
"execution_count": null,
|
77 |
+
"outputs": []
|
78 |
+
},
|
79 |
+
{
|
80 |
+
"cell_type": "code",
|
81 |
+
"source": [
|
82 |
+
"!7z x flickr8k-sau.zip"
|
83 |
+
],
|
84 |
+
"metadata": {
|
85 |
+
"id": "NOyPR6EnE80f"
|
86 |
+
},
|
87 |
+
"execution_count": null,
|
88 |
+
"outputs": []
|
89 |
+
},
|
90 |
+
{
|
91 |
+
"cell_type": "code",
|
92 |
+
"source": [
|
93 |
+
"output = Path.cwd() / 'images'\n",
|
94 |
+
"if not output.exists():\n",
|
95 |
+
" output.mkdir()\n",
|
96 |
+
"\n",
|
97 |
+
"fast = output / 'fast'\n",
|
98 |
+
"med = output / 'medium'\n",
|
99 |
+
"slow = output / 'slow'\n",
|
100 |
+
"\n",
|
101 |
+
"if not fast.exists():\n",
|
102 |
+
" fast.mkdir()\n",
|
103 |
+
"\n",
|
104 |
+
"if not med.exists():\n",
|
105 |
+
" med.mkdir()\n",
|
106 |
+
"\n",
|
107 |
+
"if not slow.exists():\n",
|
108 |
+
" slow.mkdir()\n",
|
109 |
+
"\n",
|
110 |
+
"counter = 0\n",
|
111 |
+
"\n",
|
112 |
+
"with open(\"finalDataset.csv\") as f:\n",
|
113 |
+
" f.readline()\n",
|
114 |
+
" image_path = Path.cwd() / 'Flickr_Data' / 'Images'\n",
|
115 |
+
" for line in f:\n",
|
116 |
+
" idx, image_name, pace = line.strip().split(',')\n",
|
117 |
+
" if pace == 'slow':\n",
|
118 |
+
" shutil.copy2(image_path / image_name, slow)\n",
|
119 |
+
" elif pace == 'fast':\n",
|
120 |
+
" shutil.copy2(image_path / image_name, fast)\n",
|
121 |
+
" else:\n",
|
122 |
+
" shutil.copy2(image_path / image_name, med)\n",
|
123 |
+
" counter += 1\n",
|
124 |
+
"\n",
|
125 |
+
"print(counter)"
|
126 |
+
],
|
127 |
+
"metadata": {
|
128 |
+
"id": "dY034gfPE9wW"
|
129 |
+
},
|
130 |
+
"execution_count": null,
|
131 |
+
"outputs": []
|
132 |
+
},
|
133 |
+
{
|
134 |
+
"cell_type": "code",
|
135 |
+
"source": [
|
136 |
+
"import matplotlib.pyplot as plt\n",
|
137 |
+
"import numpy as np\n",
|
138 |
+
"import os\n",
|
139 |
+
"import PIL\n",
|
140 |
+
"import tensorflow as tf\n",
|
141 |
+
"from tensorflow import keras\n",
|
142 |
+
"from tensorflow.keras import layers\n",
|
143 |
+
"from tensorflow.python.keras.layers import Dense, Flatten\n",
|
144 |
+
"from tensorflow.keras.models import Sequential\n",
|
145 |
+
"from tensorflow.keras.optimizers import Adam"
|
146 |
+
],
|
147 |
+
"metadata": {
|
148 |
+
"id": "G4uVyuXJGKDj"
|
149 |
+
},
|
150 |
+
"execution_count": null,
|
151 |
+
"outputs": []
|
152 |
+
},
|
153 |
+
{
|
154 |
+
"cell_type": "code",
|
155 |
+
"execution_count": null,
|
156 |
+
"metadata": {
|
157 |
+
"id": "a2Zst5ytENY3"
|
158 |
+
},
|
159 |
+
"outputs": [],
|
160 |
+
"source": [
|
161 |
+
"import pathlib\n",
|
162 |
+
"data_dir = 'images/'\n",
|
163 |
+
"data_dir = pathlib.Path(data_dir)\n",
|
164 |
+
"bg = list(data_dir.glob('medium/*'))\n",
|
165 |
+
"print(bg[0])\n",
|
166 |
+
"PIL.Image.open(str(bg[0]))"
|
167 |
+
]
|
168 |
+
},
|
169 |
+
{
|
170 |
+
"cell_type": "code",
|
171 |
+
"source": [
|
172 |
+
"data_dir = 'images/'\n",
|
173 |
+
"img_height, img_width = 224,224\n",
|
174 |
+
"batch_size = 32\n",
|
175 |
+
"train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
|
176 |
+
" data_dir,\n",
|
177 |
+
" validation_split = 0.2,\n",
|
178 |
+
" subset = \"training\",\n",
|
179 |
+
" seed = 345,\n",
|
180 |
+
" label_mode = 'categorical',\n",
|
181 |
+
" image_size = (img_height, img_width),\n",
|
182 |
+
" batch_size = batch_size\n",
|
183 |
+
")"
|
184 |
+
],
|
185 |
+
"metadata": {
|
186 |
+
"id": "xlGw3fN5Eb24"
|
187 |
+
},
|
188 |
+
"execution_count": null,
|
189 |
+
"outputs": []
|
190 |
+
},
|
191 |
+
{
|
192 |
+
"cell_type": "code",
|
193 |
+
"source": [
|
194 |
+
"val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
|
195 |
+
" data_dir,\n",
|
196 |
+
" validation_split = 0.2,\n",
|
197 |
+
" subset = \"validation\",\n",
|
198 |
+
" seed = 345,\n",
|
199 |
+
" label_mode = 'categorical',\n",
|
200 |
+
" image_size = (img_height, img_width),\n",
|
201 |
+
" batch_size = batch_size\n",
|
202 |
+
")"
|
203 |
+
],
|
204 |
+
"metadata": {
|
205 |
+
"id": "IN1O5n9WEd2n"
|
206 |
+
},
|
207 |
+
"execution_count": null,
|
208 |
+
"outputs": []
|
209 |
+
},
|
210 |
+
{
|
211 |
+
"cell_type": "code",
|
212 |
+
"source": [
|
213 |
+
"class_names = train_ds.class_names\n",
|
214 |
+
"print(class_names)"
|
215 |
+
],
|
216 |
+
"metadata": {
|
217 |
+
"id": "zVXI77J4Ehh3"
|
218 |
+
},
|
219 |
+
"execution_count": null,
|
220 |
+
"outputs": []
|
221 |
+
},
|
222 |
+
{
|
223 |
+
"cell_type": "code",
|
224 |
+
"source": [
|
225 |
+
"resnet_model = Sequential()\n",
|
226 |
+
"\n",
|
227 |
+
"pretrained_model= ResNet50(\n",
|
228 |
+
" include_top=False,\n",
|
229 |
+
" input_shape=(224,224,3),\n",
|
230 |
+
" pooling='avg',classes=211,\n",
|
231 |
+
" weights='imagenet')\n",
|
232 |
+
"\n",
|
233 |
+
"for layer in pretrained_model.layers:\n",
|
234 |
+
" layer.trainable=False\n",
|
235 |
+
"\n",
|
236 |
+
"resnet_model.add(pretrained_model)\n",
|
237 |
+
"resnet_model.add(Flatten())\n",
|
238 |
+
"resnet_model.add(Dense(1024, activation = 'relu'))\n",
|
239 |
+
"resnet_model.add(Dense(256, activation = 'relu'))\n",
|
240 |
+
"resnet_model.add(Dense(3, activation = 'softmax'))"
|
241 |
+
],
|
242 |
+
"metadata": {
|
243 |
+
"id": "ZpR1kjgWEi3_"
|
244 |
+
},
|
245 |
+
"execution_count": null,
|
246 |
+
"outputs": []
|
247 |
+
},
|
248 |
+
{
|
249 |
+
"cell_type": "code",
|
250 |
+
"source": [
|
251 |
+
"resnet_model.summary()"
|
252 |
+
],
|
253 |
+
"metadata": {
|
254 |
+
"id": "2WGlO4VLEpSf"
|
255 |
+
},
|
256 |
+
"execution_count": null,
|
257 |
+
"outputs": []
|
258 |
+
},
|
259 |
+
{
|
260 |
+
"cell_type": "code",
|
261 |
+
"source": [
|
262 |
+
"resnet_model.compile(optimizer=Adam(learning_rate=0.001),loss='categorical_crossentropy',metrics=['accuracy'])"
|
263 |
+
],
|
264 |
+
"metadata": {
|
265 |
+
"id": "TPNjjBLqEqwu"
|
266 |
+
},
|
267 |
+
"execution_count": null,
|
268 |
+
"outputs": []
|
269 |
+
},
|
270 |
+
{
|
271 |
+
"cell_type": "code",
|
272 |
+
"source": [
|
273 |
+
"epochs = 15\n",
|
274 |
+
"checkpoint_filepath = '/tmp/checkpoint'\n",
|
275 |
+
"model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
|
276 |
+
" filepath=checkpoint_filepath,\n",
|
277 |
+
" save_weights_only=True,\n",
|
278 |
+
" monitor='val_accuracy',\n",
|
279 |
+
" mode='max',\n",
|
280 |
+
" save_best_only=True)\n",
|
281 |
+
"history = resnet_model.fit(\n",
|
282 |
+
" train_ds,\n",
|
283 |
+
" validation_data = val_ds,\n",
|
284 |
+
" epochs = epochs,\n",
|
285 |
+
" callbacks=[model_checkpoint_callback]\n",
|
286 |
+
")"
|
287 |
+
],
|
288 |
+
"metadata": {
|
289 |
+
"id": "rc8VaaypEsJX"
|
290 |
+
},
|
291 |
+
"execution_count": null,
|
292 |
+
"outputs": []
|
293 |
+
},
|
294 |
+
{
|
295 |
+
"cell_type": "code",
|
296 |
+
"source": [
|
297 |
+
"resnet_model.load_weights(checkpoint_filepath)"
|
298 |
+
],
|
299 |
+
"metadata": {
|
300 |
+
"id": "tUCSqCs3JLBu"
|
301 |
+
},
|
302 |
+
"execution_count": null,
|
303 |
+
"outputs": []
|
304 |
+
},
|
305 |
+
{
|
306 |
+
"cell_type": "code",
|
307 |
+
"source": [
|
308 |
+
"resnet_model.save('pace_model')"
|
309 |
+
],
|
310 |
+
"metadata": {
|
311 |
+
"id": "HIsB7PHxfwM-"
|
312 |
+
},
|
313 |
+
"execution_count": null,
|
314 |
+
"outputs": []
|
315 |
+
},
|
316 |
+
{
|
317 |
+
"cell_type": "code",
|
318 |
+
"source": [
|
319 |
+
"resnet_model.save_weights('pace_model_weights.h5')"
|
320 |
+
],
|
321 |
+
"metadata": {
|
322 |
+
"id": "yhiPNdnkgU7C"
|
323 |
+
},
|
324 |
+
"execution_count": null,
|
325 |
+
"outputs": []
|
326 |
+
},
|
327 |
+
{
|
328 |
+
"cell_type": "code",
|
329 |
+
"source": [
|
330 |
+
"resnet_model.load_weights('pace_model_weights.h5')"
|
331 |
+
],
|
332 |
+
"metadata": {
|
333 |
+
"id": "IuXgP1oJhZz1"
|
334 |
+
},
|
335 |
+
"execution_count": null,
|
336 |
+
"outputs": []
|
337 |
+
},
|
338 |
+
{
|
339 |
+
"cell_type": "code",
|
340 |
+
"source": [
|
341 |
+
"import cv2\n",
|
342 |
+
"image=cv2.imread('danny.png')\n",
|
343 |
+
"image_resized= cv2.resize(image, (img_height,img_width))\n",
|
344 |
+
"image=np.expand_dims(image_resized,axis=0)\n",
|
345 |
+
"print(image.shape)"
|
346 |
+
],
|
347 |
+
"metadata": {
|
348 |
+
"id": "YXGqeYevKlpR"
|
349 |
+
},
|
350 |
+
"execution_count": null,
|
351 |
+
"outputs": []
|
352 |
+
},
|
353 |
+
{
|
354 |
+
"cell_type": "code",
|
355 |
+
"source": [
|
356 |
+
"PIL.Image.open('danny.png')"
|
357 |
+
],
|
358 |
+
"metadata": {
|
359 |
+
"id": "ZP6ATt0CYe1c"
|
360 |
+
},
|
361 |
+
"execution_count": null,
|
362 |
+
"outputs": []
|
363 |
+
},
|
364 |
+
{
|
365 |
+
"cell_type": "code",
|
366 |
+
"source": [
|
367 |
+
"pred=resnet_model.predict(image)\n",
|
368 |
+
"print(pred)"
|
369 |
+
],
|
370 |
+
"metadata": {
|
371 |
+
"id": "i1qeeWVcK3av"
|
372 |
+
},
|
373 |
+
"execution_count": null,
|
374 |
+
"outputs": []
|
375 |
+
},
|
376 |
+
{
|
377 |
+
"cell_type": "code",
|
378 |
+
"source": [
|
379 |
+
"output_class=class_names[np.argmax(pred)]\n",
|
380 |
+
"print(\"The predicted class is\", output_class)"
|
381 |
+
],
|
382 |
+
"metadata": {
|
383 |
+
"id": "-FgwW4zDK47O"
|
384 |
+
},
|
385 |
+
"execution_count": null,
|
386 |
+
"outputs": []
|
387 |
+
},
|
388 |
+
{
|
389 |
+
"cell_type": "code",
|
390 |
+
"source": [
|
391 |
+
"import matplotlib.image as img\n",
|
392 |
+
"import matplotlib.pyplot as plt\n",
|
393 |
+
"from scipy.cluster.vq import whiten\n",
|
394 |
+
"from scipy.cluster.vq import kmeans\n",
|
395 |
+
"import pandas as pd\n",
|
396 |
+
"\n",
|
397 |
+
"batman_image = img.imread('danny.png')\n",
|
398 |
+
"\n",
|
399 |
+
"r = []\n",
|
400 |
+
"g = []\n",
|
401 |
+
"b = []\n",
|
402 |
+
"for row in batman_image:\n",
|
403 |
+
" for temp_r, temp_g, temp_b, temp in row:\n",
|
404 |
+
" r.append(temp_r)\n",
|
405 |
+
" g.append(temp_g)\n",
|
406 |
+
" b.append(temp_b)\n",
|
407 |
+
"\n",
|
408 |
+
"batman_df = pd.DataFrame({'red': r,\n",
|
409 |
+
" 'green': g,\n",
|
410 |
+
" 'blue': b})\n",
|
411 |
+
"\n",
|
412 |
+
"batman_df['scaled_color_red'] = whiten(batman_df['red'])\n",
|
413 |
+
"batman_df['scaled_color_blue'] = whiten(batman_df['blue'])\n",
|
414 |
+
"batman_df['scaled_color_green'] = whiten(batman_df['green'])\n",
|
415 |
+
"\n",
|
416 |
+
"cluster_centers, _ = kmeans(batman_df[['scaled_color_red',\n",
|
417 |
+
" 'scaled_color_blue',\n",
|
418 |
+
" 'scaled_color_green']], 3)\n",
|
419 |
+
"\n",
|
420 |
+
"dominant_colors = []\n",
|
421 |
+
"\n",
|
422 |
+
"red_std, green_std, blue_std = batman_df[['red',\n",
|
423 |
+
" 'green',\n",
|
424 |
+
" 'blue']].std()\n",
|
425 |
+
"\n",
|
426 |
+
"for cluster_center in cluster_centers:\n",
|
427 |
+
" red_scaled, green_scaled, blue_scaled = cluster_center\n",
|
428 |
+
" dominant_colors.append((\n",
|
429 |
+
" red_scaled * red_std / 255,\n",
|
430 |
+
" green_scaled * green_std / 255,\n",
|
431 |
+
" blue_scaled * blue_std / 255\n",
|
432 |
+
" ))\n",
|
433 |
+
"\n",
|
434 |
+
"plt.imshow([dominant_colors])\n",
|
435 |
+
"plt.show()"
|
436 |
+
],
|
437 |
+
"metadata": {
|
438 |
+
"id": "pcUf1oNWpNWt"
|
439 |
+
},
|
440 |
+
"execution_count": null,
|
441 |
+
"outputs": []
|
442 |
+
},
|
443 |
+
{
|
444 |
+
"cell_type": "code",
|
445 |
+
"source": [
|
446 |
+
"import matplotlib.image as img\n",
|
447 |
+
"\n",
|
448 |
+
"# Read batman image and print dimensions\n",
|
449 |
+
"batman_image = img.imread('for.jpg')\n",
|
450 |
+
"print(batman_image.shape)"
|
451 |
+
],
|
452 |
+
"metadata": {
|
453 |
+
"id": "r4eAlhkupdlS"
|
454 |
+
},
|
455 |
+
"execution_count": null,
|
456 |
+
"outputs": []
|
457 |
+
},
|
458 |
+
{
|
459 |
+
"cell_type": "code",
|
460 |
+
"source": [
|
461 |
+
"import pandas as pd\n",
|
462 |
+
"from scipy.cluster.vq import whiten\n",
|
463 |
+
"\n",
|
464 |
+
"# Store RGB values of all pixels in lists r, g and b\n",
|
465 |
+
"r = []\n",
|
466 |
+
"g = []\n",
|
467 |
+
"b = []\n",
|
468 |
+
"for row in batman_image:\n",
|
469 |
+
" for temp_r, temp_g, temp_b in row:\n",
|
470 |
+
" r.append(temp_r)\n",
|
471 |
+
" g.append(temp_g)\n",
|
472 |
+
" b.append(temp_b)\n",
|
473 |
+
"\n",
|
474 |
+
"# only printing the size of these lists\n",
|
475 |
+
"# as the content is too big\n",
|
476 |
+
"print(len(r))\n",
|
477 |
+
"print(len(g))\n",
|
478 |
+
"print(len(b))\n",
|
479 |
+
"\n",
|
480 |
+
"# Saving as DataFrame\n",
|
481 |
+
"batman_df = pd.DataFrame({'red': r,\n",
|
482 |
+
" 'green': g,\n",
|
483 |
+
" 'blue': b})\n",
|
484 |
+
"\n",
|
485 |
+
"# Scaling the values\n",
|
486 |
+
"batman_df['scaled_color_red'] = whiten(batman_df['red'])\n",
|
487 |
+
"batman_df['scaled_color_blue'] = whiten(batman_df['blue'])\n",
|
488 |
+
"batman_df['scaled_color_green'] = whiten(batman_df['green'])"
|
489 |
+
],
|
490 |
+
"metadata": {
|
491 |
+
"id": "unc3TcVop2qn"
|
492 |
+
},
|
493 |
+
"execution_count": null,
|
494 |
+
"outputs": []
|
495 |
+
},
|
496 |
+
{
|
497 |
+
"cell_type": "code",
|
498 |
+
"source": [
|
499 |
+
"import seaborn as sns\n",
|
500 |
+
"distortions = []\n",
|
501 |
+
"num_clusters = range(1, 7) # range of cluster sizes\n",
|
502 |
+
"\n",
|
503 |
+
"# Create a list of distortions from the kmeans function\n",
|
504 |
+
"for i in num_clusters:\n",
|
505 |
+
" cluster_centers, distortion = kmeans(batman_df[['scaled_color_red',\n",
|
506 |
+
" 'scaled_color_blue',\n",
|
507 |
+
" 'scaled_color_green']], i)\n",
|
508 |
+
" distortions.append(distortion)\n",
|
509 |
+
"\n",
|
510 |
+
"# Create a data frame with two lists, num_clusters and distortions\n",
|
511 |
+
"elbow_plot = pd.DataFrame({'num_clusters': num_clusters,\n",
|
512 |
+
" 'distortions': distortions})\n",
|
513 |
+
"\n",
|
514 |
+
"# Create a line plot of num_clusters and distortions\n",
|
515 |
+
"sns.lineplot(x='num_clusters', y='distortions', data=elbow_plot)\n",
|
516 |
+
"plt.xticks(num_clusters)\n",
|
517 |
+
"plt.show()"
|
518 |
+
],
|
519 |
+
"metadata": {
|
520 |
+
"id": "NE7I1771qAPK"
|
521 |
+
},
|
522 |
+
"execution_count": null,
|
523 |
+
"outputs": []
|
524 |
+
},
|
525 |
+
{
|
526 |
+
"cell_type": "code",
|
527 |
+
"source": [
|
528 |
+
"cluster_centers, _ = kmeans(batman_df[['scaled_color_red',\n",
|
529 |
+
" 'scaled_color_blue',\n",
|
530 |
+
" 'scaled_color_green']], 3)\n",
|
531 |
+
"\n",
|
532 |
+
"dominant_colors = []\n",
|
533 |
+
"\n",
|
534 |
+
"# Get standard deviations of each color\n",
|
535 |
+
"red_std, green_std, blue_std = batman_df[['red',\n",
|
536 |
+
" 'green',\n",
|
537 |
+
" 'blue']].std()\n",
|
538 |
+
"\n",
|
539 |
+
"for cluster_center in cluster_centers:\n",
|
540 |
+
" red_scaled, green_scaled, blue_scaled = cluster_center\n",
|
541 |
+
"\n",
|
542 |
+
" # Convert each standardized value to scaled value\n",
|
543 |
+
" dominant_colors.append((\n",
|
544 |
+
" red_scaled * red_std / 255,\n",
|
545 |
+
" green_scaled * green_std / 255,\n",
|
546 |
+
" blue_scaled * blue_std / 255\n",
|
547 |
+
" ))\n",
|
548 |
+
"\n",
|
549 |
+
"# Display colors of cluster centers\n",
|
550 |
+
"plt.imshow([dominant_colors])\n",
|
551 |
+
"plt.show()"
|
552 |
+
],
|
553 |
+
"metadata": {
|
554 |
+
"id": "sfE-qoULqHQM"
|
555 |
+
},
|
556 |
+
"execution_count": null,
|
557 |
+
"outputs": []
|
558 |
+
},
|
559 |
+
{
|
560 |
+
"cell_type": "code",
|
561 |
+
"source": [
|
562 |
+
"from webcolors import rgb_to_name\n",
|
563 |
+
"\n",
|
564 |
+
"for i in dominant_colors:\n",
|
565 |
+
" named_color = rgb_to_name(i, spec='css3')\n",
|
566 |
+
" print(named_color)"
|
567 |
+
],
|
568 |
+
"metadata": {
|
569 |
+
"id": "1U86FPKkqPPV"
|
570 |
+
},
|
571 |
+
"execution_count": null,
|
572 |
+
"outputs": []
|
573 |
+
},
|
574 |
+
{
|
575 |
+
"cell_type": "code",
|
576 |
+
"source": [],
|
577 |
+
"metadata": {
|
578 |
+
"id": "xolPh4bIqsU_"
|
579 |
+
},
|
580 |
+
"execution_count": null,
|
581 |
+
"outputs": []
|
582 |
+
}
|
583 |
+
]
|
584 |
+
}
|