File size: 11,088 Bytes
f3507ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Inference_visualization_tool.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Klhdy8pnk5J8"
},
"source": [
"[](https://colab.research.google.com/github/tensorflow/models/blob/master/official/projects/edgetpu/vision/serving/inference_visualization_tool.ipynb)\n",
"\n",
"# Visualizing segmentation outputs using colab.\n",
"\n",
"This file is located in [github](https://github.com/tensorflow/models/blob/master/official/projects/edgetpu/vision/serving/inference_visualization_tool.ipynb) and uses [colab integration](https://colab.research.google.com/github/tensorflow/models/blob/master/official/projects/edgetpu/vision/serving/inference_visualization_tool.ipynb) to seemlessly show [segmentation model](https://github.com/tensorflow/models/blob/master/official/projects/edgetpu/vision/README.md) outputs."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dHWaHWjYJEsE"
},
"source": [
"## Setup sandbox\n",
"\n",
"Imports required libs and get ready to load data."
]
},
{
"cell_type": "code",
"metadata": {
"id": "YWVHsuCiJHjo"
},
"source": [
"from google.colab import auth # access to saved model in tflite format\n",
"auth.authenticate_user()\n",
"from PIL import Image # used to read images as arrays\n",
"import tensorflow as tf # runs tested model\n",
"import numpy as np # postprocessing for render.\n",
"from scipy import ndimage # postprocessing for render.\n",
"import matplotlib.pyplot as plt # render\n",
"\n",
"# Copies reference to colab's sandbox.\n",
"def copy_to_sandbox(web_path):\n",
" sandbox_path = web_path.split('/')[-1]\n",
" !rm -f {sandbox_path}\n",
" if web_path[:2] == \"gs\":\n",
" !gsutil cp {web_path} {sandbox_path}\n",
" else:\n",
" !wget -v {web_path} --no-check-certificate\n",
" return sandbox_path\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "BzsTQjiEI75t"
},
"source": [
"## Prepare sandbox images\n",
"\n",
"Running this notebook will show sample segmentation of 3 pictures from [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/) dataset. You can try it on other pictures by adding you own URLS to `IMAGE_URLS` list."
]
},
{
"cell_type": "code",
"metadata": {
"id": "-vGHZSPWXbyu"
},
"source": [
"# Common image URL pattern.\n",
"_IMAGE_URL_PATTERN = 'https://raw.githubusercontent.com/tensorflow/models/master/official/projects/edgetpu/vision/serving/testdata/ADE_val_{name}.jpg'\n",
"# Coma separated list of image ids.\n",
"_IMAGE_NAMES = ['00001626','00001471','00000557']\n",
"# List\n",
"IMAGE_URLS = [_IMAGE_URL_PATTERN.replace('{name}', image) for image in _IMAGE_NAMES]\n",
"# IMAGE_URLS.append('your URL')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HMgtac7ULZcK"
},
"source": [
"IMAGES = [copy_to_sandbox(image_url) for image_url in IMAGE_URLS]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "YJU3mFebL6oP"
},
"source": [
"## Prepare sandbox model\n",
"\n",
"Default visualize is running M-size model. Model is copiend to sandbox to run.You can use another model from the list."
]
},
{
"cell_type": "code",
"metadata": {
"id": "PSQPTYgLL_VS"
},
"source": [
"MODEL_HOME='gs://tf_model_garden/models/edgetpu/checkpoint_and_tflite/vision/segmentation-edgetpu/tflite/default_argmax'\n",
"!gsutil ls {MODEL_HOME}"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "shzpQoEaGnvp"
},
"source": [
"# Path to tflite file, can use any other from list above.\n",
"MODEL_NAME='deeplabv3plus_mobilenet_edgetpuv2_m_ade20k_32.tflite'#@param\n",
"MODEL = copy_to_sandbox(MODEL_HOME + \"/\" + MODEL_NAME)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6Bd4if7fMY7v"
},
"source": [
"# Image sizes compatible with the model\n",
"MODEL_IMAGE_WIDTH = 512\n",
"MODEL_IMAGE_HEIGHT = 512"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "mtV4GhuXQn2Q"
},
"source": [
"## Image preprocess\n",
"\n",
"Function defines how to preprocess image before running inference"
]
},
{
"cell_type": "code",
"metadata": {
"id": "jASE46vxRHeP"
},
"source": [
"def read_image(image):\n",
" im = Image.open(image).convert('RGB')\n",
" min_dim=min(im.size[0], im.size[1])\n",
" new_y_dim = MODEL_IMAGE_HEIGHT * im.size[0] // min_dim\n",
" new_x_dim = MODEL_IMAGE_WIDTH * im.size[1] // min_dim\n",
" # scale to outer fit.\n",
" im = im.resize((new_y_dim, new_x_dim))\n",
" input_data = np.expand_dims(im, axis=0)\n",
" # crop to size\n",
" return input_data[:, :MODEL_IMAGE_HEIGHT, :MODEL_IMAGE_WIDTH]\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kkxj-SkrNZE2"
},
"source": [
"## Model runner.\n",
"\n",
"Simple wrapper of tflite interpreter invoke."
]
},
{
"cell_type": "code",
"metadata": {
"id": "GdlsbiVqL5JZ"
},
"source": [
"def run_model(input_data, model_data):\n",
" preprocessed_data = (input_data-128).astype(np.int8)\n",
" # Load the tflite model and allocate tensors.\n",
" interpreter_x = tf.lite.Interpreter(model_path=model_data)\n",
" interpreter_x.allocate_tensors()\n",
" # Get input and output tensors.\n",
" input_details = interpreter_x.get_input_details()\n",
" output_details = interpreter_x.get_output_details()\n",
" interpreter_x.set_tensor(input_details[0]['index'], preprocessed_data)\n",
" interpreter_x.invoke()\n",
" output_data = interpreter_x.get_tensor(output_details[0]['index'])\n",
" return output_data.reshape((MODEL_IMAGE_HEIGHT, MODEL_IMAGE_WIDTH))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "KrJYUoRYOShc"
},
"source": [
"## 6px wide edge highlighter.\n",
"\n",
"First function bellow finds edges of classes, and highlights them with 6px edge. Second function blends edge with original image."
]
},
{
"cell_type": "code",
"metadata": {
"id": "KhS1lOrxHp5C"
},
"source": [
"# Creates a 6px wide boolean edge mask to highlight the segmentation.\n",
"def edge(mydata):\n",
" mydata = mydata.reshape((MODEL_IMAGE_HEIGHT, MODEL_IMAGE_WIDTH))\n",
" mydatat = mydata.transpose([1, 0])\n",
" mydata = np.convolve(mydata.reshape(-1), [-1, 0, 1], mode='same').reshape((MODEL_IMAGE_HEIGHT, MODEL_IMAGE_WIDTH))\n",
" mydatat = np.convolve(mydatat.reshape(-1), [-1, 0, 1], mode='same').reshape((MODEL_IMAGE_HEIGHT, MODEL_IMAGE_WIDTH)).transpose([1, 0])\n",
" mydata = np.maximum((mydata != 0).astype(np.int8), (mydatat != 0).astype(np.int8))\n",
" mydata = ndimage.binary_dilation(mydata).astype(np.int8)\n",
" mydata = ndimage.binary_dilation(mydata).astype(np.int8)\n",
" mydata = ndimage.binary_dilation(mydata).astype(np.int8)\n",
" return mydata"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "GRxyl3DkSeIF"
},
"source": [
"def fancy_edge_overlay(input_data, output_data):\n",
" output_data = np.reshape(np.minimum(output_data, 32), (MODEL_IMAGE_HEIGHT, MODEL_IMAGE_WIDTH))\n",
" output_edge = edge(output_data).reshape((MODEL_IMAGE_HEIGHT, MODEL_IMAGE_WIDTH,1))\n",
" output_data = np.stack([output_data%3, (output_data//3)%3, (output_data//9)%3], axis = -1)\n",
" return input_data.reshape((MODEL_IMAGE_HEIGHT, MODEL_IMAGE_WIDTH, 3)).astype(np.float32) * (1-output_edge) + output_data * output_edge * 255\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "1NJY0tKaTjdW"
},
"source": [
"## Visualize!"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1mot5M_nl5P7"
},
"source": [
"# Set visualization wind sizes.\n",
"fig, ax = plt.subplots(max(len(IMAGES),2), 3)\n",
"fig.set_figwidth(30)\n",
"fig.set_figheight(10*max(len(IMAGES),2))\n",
"\n",
"# Read and test image.\n",
"for r, image in enumerate(IMAGES):\n",
" input_data = read_image(image)\n",
" ax[r, 0].imshow(input_data.reshape((MODEL_IMAGE_HEIGHT, MODEL_IMAGE_WIDTH, 3)).astype(np.uint8))\n",
" ax[r, 0].set_title('Original')\n",
" ax[r, 0].grid(False)\n",
"\n",
" # Test the model on input data.\n",
" output_data = run_model(input_data, MODEL)\n",
" ax[r, 1].imshow(output_data, vmin = 0, vmax = 32)\n",
" ax[r, 1].set_title('Segmentation')\n",
" ax[r, 1].grid(False)\n",
"\n",
" fancy_data = fancy_edge_overlay(input_data, output_data)\n",
" ax[r, 2].imshow(fancy_data.astype(np.uint8), vmin = 0, vmax = 32)\n",
" ax[r, 2].set_title('Segmentation & original')\n",
" ax[r, 2].grid(False)\n"
],
"execution_count": null,
"outputs": []
}
]
} |