From b88716350fbd3bf12f3d70812674a3e257c7bdae Mon Sep 17 00:00:00 2001 From: Srihari Humbarwadi Date: Sat, 13 Oct 2018 03:51:13 +0530 Subject: [PATCH] updated color map --- README.md | 14 +- inference.ipynb | 344 ++++++++++++++++++++++++++++++++++++++++++++++++ outputs2/1.png | Bin 0 -> 2158386 bytes outputs2/2.png | Bin 0 -> 1907131 bytes outputs2/3.png | Bin 0 -> 2346310 bytes outputs2/4.png | Bin 0 -> 2255317 bytes 6 files changed, 352 insertions(+), 6 deletions(-) create mode 100644 inference.ipynb create mode 100644 outputs2/1.png create mode 100644 outputs2/2.png create mode 100644 outputs2/3.png create mode 100644 outputs2/4.png diff --git a/README.md b/README.md index 98a167d..06def41 100644 --- a/README.md +++ b/README.md @@ -21,14 +21,16 @@ The results have only 3 classes marked on them. - vehicle (Blue) ## Video results -[Part 1](https://youtu.be/D68uL3aEu5o) -[Part 2](https://youtu.be/YwalCv13E0Q) +[Part 1](https://youtu.be/ehy4yoVLuvM) +[Part 2](https://youtu.be/Q7Z_4USTNuU) +[Part 3](https://youtu.be/x5JNB3NrNfY) +[Part 4](https://youtu.be/YwalCv13E0Q) ## Image results -![image 1](outputs/results/1264.png) -![image 1](outputs/results/1356.png) -![image 1](outputs/results/1351.png) -![image 1](outputs/results/1743.png) +![image 1](outputs2/1.png) +![image 1](outputs2/3.png) +![image 1](outputs2/2.png) +![image 1](outputs2/4.png) ## To-do - [ ] Hyperparameter tuning diff --git a/inference.ipynb b/inference.ipynb new file mode 100644 index 0000000..1ed7b54 --- /dev/null +++ b/inference.ipynb @@ -0,0 +1,344 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import random\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.python.keras import backend as K\n", + "from tensorflow.python.keras.layers import Input, Conv2D, MaxPooling2D, concatenate, UpSampling2D\n", + "from tensorflow.python.keras.optimizers import Adadelta, Nadam\n", + "from tensorflow.python.keras.models import Model, load_model\n", + "from tensorflow.python.keras.utils import multi_gpu_model, plot_model\n", + "from tensorflow.python.keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.python.keras.preprocessing import image\n", + "import tensorflow as tf\n", + "from tensorflow.python.keras.losses import binary_crossentropy\n", + "import cv2\n", + "from multiclassunet import Unet\n", + "from dilatednet import DilatedNet\n", + "import tqdm\n", + "import time\n", + "from tensorflow.python.keras.utils import Sequence\n", + "from moviepy.editor import VideoFileClip, ImageSequenceClip" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/mia/tf/lib/python3.6/site-packages/tensorflow/python/keras/initializers.py:104: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with distribution=normal is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "`normal` is a deprecated alias for `truncated_normal`\n" + ] + } + ], + "source": [ + "unet = Unet(256, 256, 8, 64)\n", + "# unet = DilatedNet(256, 256, 8, True, True)\n", + "# unet.load_weights('dilated.h5')\n", + "p_unet = multi_gpu_model(unet, 4)\n", + "# unet.load_weights('unet1m.h5')\n", + "p_unet.load_weights('models-dr/unet50k.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# s = time.time()\n", + "# for i in tqdm.tqdm(range(len(image_list))):\n", + "# img = image.load_img(f'{test_dir}/{image_list[i]}')\n", + "# x = image.img_to_array(img)/255.\n", + "# img = image.img_to_array(img)\n", + "# _img = img.copy()\n", + "# dims = x.shape\n", + "# x = cv2.resize(x, (256, 256))\n", + " \n", + "# z = unet.predict(np.expand_dims(x, axis=0))\n", + "# z = np.squeeze(z)\n", + "# z = z.reshape(256, 256, 8)\n", + "# z = cv2.resize(z, (dims[1], dims[0]))\n", + "# y = np.argmax(z, axis=2)\n", + "# y = np.argmax(z, axis=2)\n", + " \n", + "# construction = np.zeros_like(y)\n", + "# human = np.zeros_like(y)\n", + "# vehicle = np.zeros_like(y)\n", + " \n", + "# construction[y==2] = 255.\n", + "# human[y==6] = 255.\n", + "# vehicle[y==7] = 255.\n", + "\n", + "# # fig = plt.figure(figsize=(40, 10))\n", + "# # ax = fig.add_subplot(1,2,1)\n", + "# # ax.set_title(f'Image')\n", + "# # ax.imshow(img/255.)\n", + " \n", + "# result = img.copy()\n", + "# alpha = 0.3\n", + "# img[:,:,1] = construction\n", + "# img[:,:,2] = vehicle \n", + "# img[:,:,0] = human\n", + "\n", + "\n", + "# result = cv2.addWeighted(img, alpha, result, 1-alpha, 0, result)\n", + "# # ax3 = fig.add_subplot(1,2,2)\n", + "# # ax3.set_title(f'Generated_Mask {i}')\n", + "# # ax3.imshow(result/255.)\n", + " \n", + "# cv2.imwrite(f'outputs/stuggart_02/{image_list[i]}', cv2.cvtColor(result, cv2.COLOR_RGB2BGR))\n", + "# # cv2.imwrite(f'outputs/images/{i}.png', cv2.cvtColor(_img, cv2.COLOR_RGB2BGR))\n", + "\n", + "# # plt.close(fig)\n", + " \n", + "# e = time.time()\n", + "# t = str(e-s)\n", + "# o = str((e-s)/len(image_list))\n", + "# print(f'total time taken = {t}')\n", + "# print(f'time taken per image = {o}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clip = VideoFileClip('test4k.mp4',target_resolution=(288, 512))\n", + "# res = clip.fl_image(pipeline)\n", + "res.write_videofile('0.mp4', audio=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%HTML\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def pp(image):\n", + " alpha = 0.6\n", + " dims = image.shape\n", + " x = cv2.resize(image, (256, 256))\n", + " x = np.float32(x)/255.\n", + " z = unet.predict(np.expand_dims(x, axis=0))\n", + " z = np.squeeze(z)\n", + " z = z.reshape(256, 256, 8)\n", + " z = cv2.resize(z, (dims[1], dims[0]))\n", + " y = np.argmax(z, axis=2)\n", + " \n", + " img_color = image.copy() \n", + " for i in range(dims[0]):\n", + " for j in range(dims[1]):\n", + " img_color[i, j] = color_map[str(y[i, j])]\n", + " cv2.addWeighted(image, alpha, img_color, 1-alpha, 0, img_color)\n", + " return img_color" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 9\n", + "test = image.load_img(f'{test_dir}/{image_list[i]}')\n", + "test = image.img_to_array(test)\n", + "result = pp(test)\n", + "plt.imshow(result/255.)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1200 frames found\n" + ] + } + ], + "source": [ + "test_dir = 'validate/stuttgart_02/'\n", + "image_list = os.listdir(test_dir)\n", + "print(f'{len(image_list)} frames found')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "color_map = {\n", + "'0': [0, 0, 0],\n", + " '1': [153, 153, 0],\n", + " '2': [255, 204, 204],\n", + " '3': [255, 0, 127],\n", + " '4': [0, 255, 0],\n", + " '5': [0, 204, 204],\n", + " '6': [255, 0, 0],\n", + " '7': [0, 0, 255]\n", + "}\n", + "\n", + "s = time.time()\n", + "for t in tqdm.tqdm(range(len(image_list))):\n", + " img = image.load_img(f'{test_dir}/{image_list[t]}')\n", + " img = image.img_to_array(img)\n", + " alpha = 0.6\n", + " dims = img.shape\n", + " x = cv2.resize(img, (256, 256))\n", + " x = np.float32(x)/255.\n", + " z = unet.predict(np.expand_dims(x, axis=0))\n", + " z = np.squeeze(z)\n", + " z = z.reshape(256, 256, 8)\n", + " z = cv2.resize(z, (dims[1], dims[0]))\n", + " y = np.argmax(z, axis=2)\n", + " \n", + " img_color = img.copy() \n", + " for i in range(dims[0]):\n", + " for j in range(dims[1]):\n", + " img_color[i, j] = color_map[str(y[i, j])]\n", + " cv2.addWeighted(img, alpha, img_color, 1-alpha, 0, img_color)\n", + " cv2.imwrite(f'outputs/stuggart02/{image_list[t]}', cv2.cvtColor(img_color, cv2.COLOR_RGB2BGR)) \n", + "e = time.time()\n", + "t = str(e-s)\n", + "o = str((e-s)/len(image_list))\n", + "print(f'total time taken = {t}')\n", + "print(f'time taken per image = {o}')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading video stuggart00\n", + "Successfully loaded video stuggart00\n", + "[MoviePy] >>>> Building video outputs/videos/stuggart00.mp4\n", + "[MoviePy] Writing video outputs/videos/stuggart00.mp4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 600/600 [00:26<00:00, 22.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[MoviePy] Done.\n", + "[MoviePy] >>>> Video ready: outputs/videos/stuggart00.mp4 \n", + "\n", + "Successfully created video stuggart00\n", + "loading video stuggart01\n", + "Successfully loaded video stuggart01\n", + "[MoviePy] >>>> Building video outputs/videos/stuggart01.mp4\n", + "[MoviePy] Writing video outputs/videos/stuggart01.mp4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1101/1101 [00:50<00:00, 21.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[MoviePy] Done.\n", + "[MoviePy] >>>> Video ready: outputs/videos/stuggart01.mp4 \n", + "\n", + "Successfully created video stuggart01\n", + "loading video stuggart02\n", + "Successfully loaded video stuggart02\n", + "[MoviePy] >>>> Building video outputs/videos/stuggart02.mp4\n", + "[MoviePy] Writing video outputs/videos/stuggart02.mp4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1201/1201 [00:52<00:00, 22.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[MoviePy] Done.\n", + "[MoviePy] >>>> Video ready: outputs/videos/stuggart02.mp4 \n", + "\n", + "Successfully created video stuggart02\n" + ] + } + ], + "source": [ + "for folder in ['stuggart00', 'stuggart01', 'stuggart02']:\n", + " print(f'loading video {folder}')\n", + " clip = ImageSequenceClip(f'outputs/{folder}/', fps=18, load_images=True)\n", + " print(f'Successfully loaded video {folder}')\n", + " clip.write_videofile(f'outputs/videos/{folder}.mp4')\n", + " print(f'Successfully created video {folder}')" + ] + } + ], + "metadata": { + "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": 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