{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "0Nm2YOjeNK6L" }, "source": [ "# Multiclass semantic segmentation using DeepLabV3+\n", "\n", "This is an example notebook for Keras sprint prepared by Hugging Face. Keras Sprint aims to reproduce Keras examples and build interactive demos to them.\n", "The markdown parts beginning with 🤗 and the following code snippets are the parts added by Hugging Face team to give you an example of how to host your model and build a demo.\n", "\n", "\n", "**Original Author of the DeepLabV3 Example:** [Soumik Rakshit](http://github.com/soumik12345)
" ] }, { "cell_type": "markdown", "metadata": { "id": "D8YsmvyyNK6P" }, "source": [ "## Introduction\n", "\n", "Semantic segmentation, with the goal to assign semantic labels to every pixel in an image,\n", "is an essential computer vision task. In this example, we implement\n", "the **DeepLabV3+** model for multi-class semantic segmentation, a fully-convolutional\n", "architecture that performs well on semantic segmentation benchmarks.\n", "\n", "### References:\n", "\n", "- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1802.02611.pdf)\n", "- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)\n", "- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://arxiv.org/abs/1606.00915)" ] }, { "cell_type": "markdown", "metadata": { "id": "y0vaTgH-NK6Q" }, "source": [ "## Downloading the data\n", "\n", "We will use the [Crowd Instance-level Human Parsing Dataset](https://arxiv.org/abs/1811.12596)\n", "for training our model. The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images.\n", "Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification.\n", "This dataset can be used for the \"human part segmentation\" task." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dRBW6fvgNK6Q" }, "outputs": [], "source": [ "import os\n", "import cv2\n", "import numpy as np\n", "from glob import glob\n", "from scipy.io import loadmat\n", "import matplotlib.pyplot as plt\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers" ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lV4WmyoXTLaK", "outputId": "afb73da5-807c-4620-d557-d2fa5d1e8374" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "markdown", "source": [ "Find dataset here: https://drive.google.com/uc?id=1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz" ], "metadata": { "id": "CDeJjizwThUD" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "itOy9ZhnNK6R" }, "outputs": [], "source": [ "!unzip -q /content/drive/MyDrive/instance-level-human-parsing.zip" ] }, { "cell_type": "markdown", "source": [ "# 🤗 Installing packages to host and build a demo to our models" ], "metadata": { "id": "q_A0QgojPTGj" } }, { "cell_type": "code", "source": [ "!pip install huggingface-hub\n", "!pip install gradio" ], "metadata": { "id": "fBFHUsE2PYEw" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ZgFk68VYNK6S" }, "source": [ "## Creating a TensorFlow Dataset\n", "\n", "Training on the entire CIHP dataset with 38,280 images takes a lot of time, hence we will be using\n", "a smaller subset of 200 images for training our model in this example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ov8dbQBqNK6S", "outputId": "7e540f83-c1bf-44e0-f68a-970e1e3dcb45", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train Dataset: \n", "Val Dataset: \n" ] } ], "source": [ "IMAGE_SIZE = 512\n", "BATCH_SIZE = 4\n", "NUM_CLASSES = 20\n", "DATA_DIR = \"./instance-level_human_parsing/instance-level_human_parsing/Training\"\n", "NUM_TRAIN_IMAGES = 1000\n", "NUM_VAL_IMAGES = 50\n", "\n", "train_images = sorted(glob(os.path.join(DATA_DIR, \"Images/*\")))[:NUM_TRAIN_IMAGES]\n", "train_masks = sorted(glob(os.path.join(DATA_DIR, \"Category_ids/*\")))[:NUM_TRAIN_IMAGES]\n", "val_images = sorted(glob(os.path.join(DATA_DIR, \"Images/*\")))[\n", " NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES\n", "]\n", "val_masks = sorted(glob(os.path.join(DATA_DIR, \"Category_ids/*\")))[\n", " NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES\n", "]\n", "\n", "\n", "def read_image(image_path, mask=False):\n", " image = tf.io.read_file(image_path)\n", " if mask:\n", " image = tf.image.decode_png(image, channels=1)\n", " image.set_shape([None, None, 1])\n", " image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])\n", " else:\n", " image = tf.image.decode_png(image, channels=3)\n", " image.set_shape([None, None, 3])\n", " image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])\n", " image = image / 127.5 - 1\n", " return image\n", "\n", "\n", "def load_data(image_list, mask_list):\n", " image = read_image(image_list)\n", " mask = read_image(mask_list, mask=True)\n", " return image, mask\n", "\n", "\n", "def data_generator(image_list, mask_list):\n", " dataset = tf.data.Dataset.from_tensor_slices((image_list, mask_list))\n", " dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)\n", " dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)\n", " return dataset\n", "\n", "\n", "train_dataset = data_generator(train_images, train_masks)\n", "val_dataset = data_generator(val_images, val_masks)\n", "\n", "print(\"Train Dataset:\", train_dataset)\n", "print(\"Val Dataset:\", val_dataset)" ] }, { "cell_type": "markdown", "metadata": { "id": "68WUsYO-NK6T" }, "source": [ "## Building the DeepLabV3+ model\n", "\n", "DeepLabv3+ extends DeepLabv3 by adding an encoder-decoder structure. The encoder module\n", "processes multiscale contextual information by applying dilated convolution at multiple\n", "scales, while the decoder module refines the segmentation results along object boundaries.\n", "\n", "![](https://github.com/lattice-ai/DeepLabV3-Plus/raw/master/assets/deeplabv3_plus_diagram.png)\n", "\n", "**Dilated convolution:** With dilated convolution, as we go deeper in the network, we can keep the\n", "stride constant but with larger field-of-view without increasing the number of parameters\n", "or the amount of computation. Besides, it enables larger output feature maps, which is\n", "useful for semantic segmentation.\n", "\n", "The reason for using **Dilated Spatial Pyramid Pooling** is that it was shown that as the\n", "sampling rate becomes larger, the number of valid filter weights (i.e., weights that\n", "are applied to the valid feature region, instead of padded zeros) becomes smaller." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kh9auWU-NK6U" }, "outputs": [], "source": [ "\n", "def convolution_block(\n", " block_input,\n", " num_filters=256,\n", " kernel_size=3,\n", " dilation_rate=1,\n", " padding=\"same\",\n", " use_bias=False,\n", "):\n", " x = layers.Conv2D(\n", " num_filters,\n", " kernel_size=kernel_size,\n", " dilation_rate=dilation_rate,\n", " padding=\"same\",\n", " use_bias=use_bias,\n", " kernel_initializer=keras.initializers.HeNormal(),\n", " )(block_input)\n", " x = layers.BatchNormalization()(x)\n", " return tf.nn.relu(x)\n", "\n", "\n", "def DilatedSpatialPyramidPooling(dspp_input):\n", " dims = dspp_input.shape\n", " x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)\n", " x = convolution_block(x, kernel_size=1, use_bias=True)\n", " out_pool = layers.UpSampling2D(\n", " size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation=\"bilinear\",\n", " )(x)\n", "\n", " out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)\n", " out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)\n", " out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)\n", " out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)\n", "\n", " x = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])\n", " output = convolution_block(x, kernel_size=1)\n", " return output\n" ] }, { "cell_type": "markdown", "metadata": { "id": "c0I36dMgNK6V" }, "source": [ "The encoder features are first bilinearly upsampled by a factor 4, and then\n", "concatenated with the corresponding low-level features from the network backbone that\n", "have the same spatial resolution. For this example, we\n", "use a ResNet50 pretrained on ImageNet as the backbone model, and we use\n", "the low-level features from the `conv4_block6_2_relu` block of the backbone." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1RhOhvaQNK6V" }, "outputs": [], "source": [ "\n", "def DeeplabV3Plus(image_size, num_classes):\n", " model_input = keras.Input(shape=(image_size, image_size, 3))\n", " resnet50 = keras.applications.ResNet50(\n", " weights=\"imagenet\", include_top=False, input_tensor=model_input\n", " )\n", " x = resnet50.get_layer(\"conv4_block6_2_relu\").output\n", " x = DilatedSpatialPyramidPooling(x)\n", "\n", " input_a = layers.UpSampling2D(\n", " size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),\n", " interpolation=\"bilinear\",\n", " )(x)\n", " input_b = resnet50.get_layer(\"conv2_block3_2_relu\").output\n", " input_b = convolution_block(input_b, num_filters=48, kernel_size=1)\n", "\n", " x = layers.Concatenate(axis=-1)([input_a, input_b])\n", " x = convolution_block(x)\n", " x = convolution_block(x)\n", " x = layers.UpSampling2D(\n", " size=(image_size // x.shape[1], image_size // x.shape[2]),\n", " interpolation=\"bilinear\",\n", " )(x)\n", " model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding=\"same\")(x)\n", " return keras.Model(inputs=model_input, outputs=model_output)\n", "\n", "\n", "model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)" ] }, { "cell_type": "markdown", "source": [ "# 🤗 Before we move on with training, let's create TensorBoard" ], "metadata": { "id": "uFkYSXWSSRRh" } }, { "cell_type": "code", "source": [ "# Load the TensorBoard notebook extension\n", "%load_ext tensorboard" ], "metadata": { "id": "ICOJLuIwSQjz" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "log_dir = \"logs/fit/\"\n", "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)" ], "metadata": { "id": "ViDhtqnvSX_q" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "oTLle3r1NK6W" }, "source": [ "## Training\n", "\n", "We train the model using sparse categorical crossentropy as the loss function, and\n", "Adam as the optimizer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dnjSoXIbNK6W", "outputId": "1af1a02f-5ec4-473a-f6bd-32c7506f158e", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/5\n", "250/250 [==============================] - 40s 85ms/step - loss: 1.2065 - accuracy: 0.6356 - val_loss: 2.5502 - val_accuracy: 0.5545\n", "Epoch 2/5\n", "250/250 [==============================] - 20s 81ms/step - loss: 0.9568 - accuracy: 0.6963 - val_loss: 2.6706 - val_accuracy: 0.5978\n", "Epoch 3/5\n", "250/250 [==============================] - 20s 81ms/step - loss: 0.8468 - accuracy: 0.7285 - val_loss: 1.4310 - val_accuracy: 0.6123\n", "Epoch 4/5\n", "250/250 [==============================] - 20s 81ms/step - loss: 0.7736 - accuracy: 0.7513 - val_loss: 1.0082 - val_accuracy: 0.6894\n", "Epoch 5/5\n", "250/250 [==============================] - 20s 81ms/step - loss: 0.7116 - accuracy: 0.7708 - val_loss: 1.0158 - val_accuracy: 0.7053\n" ] } ], "source": [ "loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n", "model.compile(\n", " optimizer=keras.optimizers.Adam(learning_rate=0.001),\n", " loss=loss,\n", " metrics=[\"accuracy\"],\n", ")\n", "\n", "history = model.fit(train_dataset, validation_data=val_dataset, epochs=5,\n", " callbacks = [tensorboard_callback])\n", "# 🤗 note how we call tensorboard" ] }, { "cell_type": "markdown", "source": [ "# 🤗 Push your model to the Hub\n", "We will push our model to the Hugging Face Hub with tensorboard logs.\n", "\n", "If you already have access to keras-io organization, you can give \"keras-io/deeplab-v3\" as the model ID. If not, you can push model to your own account and then carry it to the keras-io organization later. 🥳\n", "\n", "To push your models to the Hub, you need authentication. To authenticate, you can log using notebook_login. You can get your token from https://huggingface.co/settings/tokens 🙌🏻" ], "metadata": { "id": "QLwNPNyZVmjM" } }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 387, "referenced_widgets": [ "6656427d58584fdc93d205920dd586c0", "bbcfbb2e927d453ca5ab6f58c0096ce8", "5c7a3a49f4d64fe7917c8c489047888a", "c8737dd8a7a44ee08762f4a66f8e62c2", "2b5694f714f5449d880fea1e59bf2496", "65c7d524f74444d8becc6b2249c61dfa", "ea738912885747fb85c1c45902128658", "86853c4a023c4eae98828688480d4ee1", "ef117ade52df431eb27301e058d7e649", "b0d5f1a62eaf42d89639b2d3b77000c2", "351c270c0e9a47e1a2c5755f470ad065", "9a20e3b8c7ce4290b60ce1889a369a8a", "83bb5d69285f43fbb30d2a09f8459f06", "338a72e0d5834c2f9879591e295d2b7c", "e99ed10952c5407b90ac9371f41f1e79", "160e8e1d73a041f1bfab72f0bbc7bcbc", "f49bb981eda24cdb897d95e6a9541cf9" ] }, "id": "sONm33JwcGFc", "outputId": "858ce119-a678-4d25-9393-6dde6824153d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ] }, { "cell_type": "markdown", "source": [ "Now we can push our model to the Hugging Face Hub 🤩🙌🏻 The below function will:\n", "1. create a remote repository on Hugging Face Hub, \n", "2. serialize our model, \n", "3. create a model card including training hyperparameters, model architecture and couple of fields you can fill about model,\n", "4. push the model to the Hub." ], "metadata": { "id": "V23tNN0JcLIa" } }, { "cell_type": "code", "source": [ "from huggingface_hub import push_to_hub_keras\n", "\n", "push_to_hub_keras(model,\n", " \"merve/deeplab-v3\",\n", " log_dir = \"./logs/fit\",\n", " tags = [\"image-segmentation\"]\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 508, "referenced_widgets": [ "79d25547eafe436cb17eacea753d8d6b", "590f529278d94fc3b5a8d7bafca4522b", "d935575e705f4be3a7bd741ec52ad3ee", "c92ae6434a7a48b9847047075724dc07", "4a5c0fbb5b7f42b2b72c4ad6dae84bba", "479b8ba4e01f41d593b0712ef13c7ab4", "012c339e2a8a4792b58b2283f81a10a6", "4d741d8aea5d488687b44d2c5251aafa", "3fc5074bdf8e4d819a7e658e025fd726", "3285a709e0534f25930344074ddab274", "f58618ad4990454bbbdb4046144c3be5", "fde09f707d83420e8b7f7991bc611b6d", "7b24fa1794fe413395f3f692aeb0b288", "c952dfa1aa9b4cafb599f8c25c473699", "b04c00cf578047c48a57c5091525e38d", "17f0eb2433b24235b4d56108cd4e2ecb", "ff5b3f4e42794cfd801bff046d07e7ff", "ffaa836cbd2c4743bcda186f2f7f5de3", "15f2504476894ff19275af4a75c0a2b2", "c13a2e4c2cd74d739473c311ff33192a", "dde17f4f2ade443bb1e388fb887662ce", "09cefd16a4444816aa8f66b7af6aba75", "4c65df413dbc4923a76ebdc4fc25ac48", "e065fdde08394bd68846c5641f8ab0e0", "0da75866105c4b1898d3693da8edc0b7", "7ba6afd7404e4d7ab7448f84c29afb08", "6a9fa5d14eee432686642ddf63677028", "7861bfce6c6e432faa15d788aa9392ad", "fa182fa9b39e40d39e9d25af46671620", "5c193eab3d6944819e72525e25880c99", "1a089068a9f44b03b59c3eea9d6d9487", "13c09471f8434875991bb5986c0fd276", "1fdc13a9f6f241c186b56dbcf4c62ecb", "7e01da20a1234719b275f54b8d3c9c37", "f00793caba854b54a345c10f443d9607", "4399b8a5d99c47f3b7f873350ac3bc54", "57febc5abbdb46d9b2fcfc782ca6a6c3", "bf964693f0954cfdba1e57109f2207f8", "e55a6f2d9c8146fc9289a11abd9c5d23", "74d8bc6267b34df282673a95688fb5c2", "537285fa93ad4005a634ba9b20f7fdc9", "696899bd01c240589d89734ec733be99", "dcb9bf7430e44ea9b25d7cfab7b49e3b", "098603a5b92e465a855d30ece229c298", "37b168972ebe44eb918ffa852b141520", "2d23a4207c144e949e783361da188e43", "e4d17b31d93b40b389fd41edf31a4f78", "f7ed1919db754291b52a56b998d0b13b", "cb4eef2d3905403bb8c9430d3c2a8a24", "a8f811fdfbd54a9188fb1a833f60f9a6", "8c7a34132c6343189336f047e3d33d24", "a37de9c39d9c45edba8d593578ff1f08", "4e5358a9eb044636a43f9f5cc67e2ec3", "2393f85e33bd4140b9eca6144f930c3d", "4c7e3c8e4eca4471aa0f760aebde4795", "52ab02bf10ea4d01bf3d3bff9d0ad13f", "c86173f060ed48b1a200f925cef1ef48", "7e347745b55441daa6d37a6890f783fe", "acdbf3200f024bf7bc2d492065f7430f", "9345debcdde446efa852934a8ed9127c", "7abebdb21c29435c9b05926dc5f765ae", "1dc72b732a1d4838a006e66f6786a6eb", "f2a6a5d448514ab799d863a685d4e9a7", "73290bb00d5e46de9efa3b0055a9a671", "8e7c2d758a2e4d55bd915d0e1e174364", "d53d843a31544e428285c0962d40e366", "bcd94f5cdbd84c589b369cf4778ee4f5", "7ad604cec9434d9a99fc7d3d4a96cd25", "8b49e25be8154f8787b76cffd10c6383", "3bf847b8c30844a5bd1f34fe192d99f7", "20277969b49843ce98b4a97c6a798f6f", "6f782e62489540269454a2a590d7a3e2", "6f633413b76a4302bad7b4d20214d5fc", "3c16b5c800a5406497acb059b88deede", "83a1317aa8c74286afde0d789f8cf1a5", "561bc92ca9e1418e889c600ee5a8c270", "ca04e7a0664a429392f62ea53a1f4522" ] }, "id": "84D5xQ3PT7kj", "outputId": "33db88da-168b-44c8-d229-77ad96ba443c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Cloning https://huggingface.co/merve/deeplab-v3 into local empty directory.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "INFO:tensorflow:Assets written to: merve/deeplab-v3/assets\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Adding files tracked by Git LFS: ['variables/variables.data-00000-of-00001', 'model.png', 'variables/variables.index']. This may take a bit of time if the files are large.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Upload file variables/variables.data-00000-of-00001: 0%| | 3.34k/45.4M [00:00 main\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'https://huggingface.co/merve/deeplab-v3/commit/4c83b323a69683a7e3607fd07d7840309bf4b096'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 25 } ] }, { "cell_type": "markdown", "source": [ "## 🤗 Build a UI using Gradio" ], "metadata": { "id": "OQ-Yrw40UGa3" } }, { "cell_type": "markdown", "source": [ "We will now build a UI with Gradio. To do this, we just need to write the inference function (which is usually given in notebooks) and pass it to a gradio `Interface`." ], "metadata": { "id": "3okP5jFMVc9M" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "import tensorflow as tf\n", "import gradio as gr\n", "from huggingface_hub import from_pretrained_keras\n", "import cv2\n", "\n", "# note how I download the already pushed model\n", "model = from_pretrained_keras(\"merve/deeplab-v3\")\n", "\n", "\n", "# we will now create our inference function\n", "colormap = np.array([[0,0,0], [31,119,180], [44,160,44], [44, 127, 125], [52, 225, 143],\n", " [217, 222, 163], [254, 128, 37], [130, 162, 128], [121, 7, 166], [136, 183, 248],\n", " [85, 1, 76], [22, 23, 62], [159, 50, 15], [101, 93, 152], [252, 229, 92],\n", " [167, 173, 17], [218, 252, 252], [238, 126, 197], [116, 157, 140], [214, 220, 252]], dtype=np.uint8)\n", " \n", "img_size = 512\n", " \n", "def read_image(image):\n", " image = tf.convert_to_tensor(image)\n", " image.set_shape([None, None, 3])\n", " image = tf.image.resize(images=image, size=[img_size, img_size])\n", " image = image / 127.5 - 1\n", " return image\n", "\n", "def infer(model, image_tensor):\n", " predictions = model.predict(np.expand_dims((image_tensor), axis=0))\n", " predictions = np.squeeze(predictions)\n", " predictions = np.argmax(predictions, axis=2)\n", " return predictions\n", "\n", "def decode_segmentation_masks(mask, colormap, n_classes):\n", " r = np.zeros_like(mask).astype(np.uint8)\n", " g = np.zeros_like(mask).astype(np.uint8)\n", " b = np.zeros_like(mask).astype(np.uint8)\n", " for l in range(0, n_classes):\n", " idx = mask == l\n", " r[idx] = colormap[l, 0]\n", " g[idx] = colormap[l, 1]\n", " b[idx] = colormap[l, 2]\n", " rgb = np.stack([r, g, b], axis=2)\n", " return rgb\n", "\n", "def get_overlay(image, colored_mask):\n", " image = tf.keras.preprocessing.image.array_to_img(image)\n", " image = np.array(image).astype(np.uint8)\n", " overlay = cv2.addWeighted(image, 0.35, colored_mask, 0.65, 0)\n", " return overlay\n", "\n", "def segmentation(input_image):\n", " image_tensor = read_image(input_image)\n", " prediction_mask = infer(image_tensor=image_tensor, model=model)\n", " prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)\n", " overlay = get_overlay(image_tensor, prediction_colormap)\n", " return (overlay, prediction_colormap)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XsjCxx3uUJIa", "outputId": "a3b06994-0e67-4af9-e075-c36713ee4507" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "config.json not found in HuggingFace Hub\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n" ] } ] }, { "cell_type": "code", "source": [ "# the app takes one image to be segmented\n", "input = gr.inputs.Image()\n", "# the app outputs two segmented images\n", "output = [gr.outputs.Image(), gr.outputs.Image()]\n", "# it's good practice to pass examples, description and a title to guide users\n", "examples = [[\"/content/example_image_2.jpeg\"], [\"/content/example_image_3.jpeg\"]] \n", "title = \"Human Part Segmentation\"\n", "description = \"Upload an image or select from examples to segment out different human parts.\"\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "b8gY7vvDWamC", "outputId": "44409207-5a0f-434a-be4b-e5e8b29a203a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n", " warnings.warn(value)\n" ] } ] }, { "cell_type": "markdown", "source": [ "Let's launch the interface!" ], "metadata": { "id": "j_Y53qbnWf2d" } }, { "cell_type": "code", "source": [ "gr.Interface(segmentation, input, output, examples=examples, allow_flagging=False, analytics_enabled=False,\n", " title=title, description=description).launch(enable_queue=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 704 }, "id": "R8CpugHGWfDQ", "outputId": "4cb4e0f2-d75b-41f3-a78b-969537f91ee9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/gradio/interface.py:356: UserWarning: The `allow_flagging` parameter in `Interface` nowtakes a string value ('auto', 'manual', or 'never'), not a boolean. Setting parameter to: 'never'.\n", " \"The `allow_flagging` parameter in `Interface` now\"\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n", "Running on public URL: https://38608.gradio.app\n", "\n", "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "(,\n", " 'http://127.0.0.1:7862/',\n", " 'https://38608.gradio.app')" ] }, "metadata": {}, "execution_count": 33 } ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "deeplabv3_plus", "provenance": [], "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "6656427d58584fdc93d205920dd586c0": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_bbcfbb2e927d453ca5ab6f58c0096ce8", "IPY_MODEL_5c7a3a49f4d64fe7917c8c489047888a", "IPY_MODEL_c8737dd8a7a44ee08762f4a66f8e62c2", "IPY_MODEL_2b5694f714f5449d880fea1e59bf2496", "IPY_MODEL_65c7d524f74444d8becc6b2249c61dfa" ], "layout": "IPY_MODEL_ea738912885747fb85c1c45902128658" } }, "bbcfbb2e927d453ca5ab6f58c0096ce8": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_86853c4a023c4eae98828688480d4ee1", "placeholder": "​", "style": "IPY_MODEL_ef117ade52df431eb27301e058d7e649", "value": "

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