├── requirements.txt ├── images ├── 0.jpg ├── 1.jpg ├── 10.jpg ├── 11.jpg ├── 12.jpg ├── 13.jpg ├── 14.jpg ├── 15.jpg ├── 16.jpg ├── 17.jpg ├── 2.jpg ├── 3.jpg ├── 4.jpg ├── 5.jpg ├── 6.jpg ├── 7.jpg ├── 8.jpg ├── 9.jpg ├── 10.json └── 1.json ├── .gitignore ├── README.md ├── LICENSE ├── labelme2coco.py └── COCO_Image_Viewer.ipynb /requirements.txt: -------------------------------------------------------------------------------- 1 | labelme 2 | numpy 3 | pillow -------------------------------------------------------------------------------- /images/0.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tony607/labelme2coco/HEAD/images/0.jpg -------------------------------------------------------------------------------- /images/1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tony607/labelme2coco/HEAD/images/1.jpg 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tony607/labelme2coco/HEAD/images/9.jpg -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | *.ipynb_checkpoints 3 | __pycache__ 4 | .vscode/ 5 | trainval.json 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # [How to create custom COCO data set for instance segmentation](https://www.dlology.com/blog/how-to-create-custom-coco-data-set-for-instance-segmentation/) | DLology blog 2 | 3 | ## Quick start 4 | 5 | Then you can run the `labelme2coco.py` script to generate a COCO data formatted JSON file for you. 6 | ``` 7 | python labelme2coco.py images 8 | ``` 9 | Then you can run the following Jupyter notebook to visualize the coco annotations. `COCO_Image_Viewer.ipynb` 10 | 11 | 12 | Further instruction on how to create your own datasets, read the [tutorial](https://www.dlology.com/blog/how-to-create-custom-coco-data-set-for-instance-segmentation/). -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | COPYRIGHT 2 | 3 | All contributions by François Chollet: 4 | Copyright (c) 2015 - 2018, François Chollet. 5 | All rights reserved. 6 | 7 | All contributions by Google: 8 | Copyright (c) 2015 - 2018, Google, Inc. 9 | All rights reserved. 10 | 11 | All contributions by Microsoft: 12 | Copyright (c) 2017 - 2018, Microsoft, Inc. 13 | All rights reserved. 14 | 15 | All other contributions: 16 | Copyright (c) 2015 - 2018, the respective contributors. 17 | All rights reserved. 18 | 19 | Each contributor holds copyright over their respective contributions. 20 | The project versioning (Git) records all such contribution source information. 21 | 22 | LICENSE 23 | 24 | The MIT License (MIT) 25 | 26 | Permission is hereby granted, free of charge, to any person obtaining a copy 27 | of this software and associated documentation files (the "Software"), to deal 28 | in the Software without restriction, including without limitation the rights 29 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 30 | copies of the Software, and to permit persons to whom the Software is 31 | furnished to do so, subject to the following conditions: 32 | 33 | The above copyright notice and this permission notice shall be included in all 34 | copies or substantial portions of the Software. 35 | 36 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 37 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 38 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 39 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 40 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 41 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 42 | SOFTWARE. 43 | 44 | -------------------------------------------------------------------------------- /labelme2coco.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | import json 4 | 5 | from labelme import utils 6 | import numpy as np 7 | import glob 8 | import PIL.Image 9 | 10 | 11 | class labelme2coco(object): 12 | def __init__(self, labelme_json=[], save_json_path="./coco.json"): 13 | """ 14 | :param labelme_json: the list of all labelme json file paths 15 | :param save_json_path: the path to save new json 16 | """ 17 | self.labelme_json = labelme_json 18 | self.save_json_path = save_json_path 19 | self.images = [] 20 | self.categories = [] 21 | self.annotations = [] 22 | self.label = [] 23 | self.annID = 1 24 | self.height = 0 25 | self.width = 0 26 | 27 | self.save_json() 28 | 29 | def data_transfer(self): 30 | for num, json_file in enumerate(self.labelme_json): 31 | with open(json_file, "r") as fp: 32 | data = json.load(fp) 33 | self.images.append(self.image(data, num)) 34 | for shapes in data["shapes"]: 35 | label = shapes["label"].split("_") 36 | if label not in self.label: 37 | self.label.append(label) 38 | points = shapes["points"] 39 | self.annotations.append(self.annotation(points, label, num)) 40 | self.annID += 1 41 | 42 | # Sort all text labels so they are in the same order across data splits. 43 | self.label.sort() 44 | for label in self.label: 45 | self.categories.append(self.category(label)) 46 | for annotation in self.annotations: 47 | annotation["category_id"] = self.getcatid(annotation["category_id"]) 48 | 49 | def image(self, data, num): 50 | image = {} 51 | img = utils.img_b64_to_arr(data["imageData"]) 52 | height, width = img.shape[:2] 53 | img = None 54 | image["height"] = height 55 | image["width"] = width 56 | image["id"] = num 57 | image["file_name"] = data["imagePath"].split("/")[-1] 58 | 59 | self.height = height 60 | self.width = width 61 | 62 | return image 63 | 64 | def category(self, label): 65 | category = {} 66 | category["supercategory"] = label[0] 67 | category["id"] = len(self.categories) 68 | category["name"] = label[0] 69 | return category 70 | 71 | def annotation(self, points, label, num): 72 | annotation = {} 73 | contour = np.array(points) 74 | x = contour[:, 0] 75 | y = contour[:, 1] 76 | area = 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1))) 77 | annotation["segmentation"] = [list(np.asarray(points).flatten())] 78 | annotation["iscrowd"] = 0 79 | annotation["area"] = area 80 | annotation["image_id"] = num 81 | 82 | annotation["bbox"] = list(map(float, self.getbbox(points))) 83 | 84 | annotation["category_id"] = label[0] # self.getcatid(label) 85 | annotation["id"] = self.annID 86 | return annotation 87 | 88 | def getcatid(self, label): 89 | for category in self.categories: 90 | if label == category["name"]: 91 | return category["id"] 92 | print("label: {} not in categories: {}.".format(label, self.categories)) 93 | exit() 94 | return -1 95 | 96 | def getbbox(self, points): 97 | polygons = points 98 | mask = self.polygons_to_mask([self.height, self.width], polygons) 99 | return self.mask2box(mask) 100 | 101 | def mask2box(self, mask): 102 | 103 | index = np.argwhere(mask == 1) 104 | rows = index[:, 0] 105 | clos = index[:, 1] 106 | 107 | left_top_r = np.min(rows) # y 108 | left_top_c = np.min(clos) # x 109 | 110 | right_bottom_r = np.max(rows) 111 | right_bottom_c = np.max(clos) 112 | 113 | return [ 114 | left_top_c, 115 | left_top_r, 116 | right_bottom_c - left_top_c, 117 | right_bottom_r - left_top_r, 118 | ] 119 | 120 | def polygons_to_mask(self, img_shape, polygons): 121 | mask = np.zeros(img_shape, dtype=np.uint8) 122 | mask = PIL.Image.fromarray(mask) 123 | xy = list(map(tuple, polygons)) 124 | PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1) 125 | mask = np.array(mask, dtype=bool) 126 | return mask 127 | 128 | def data2coco(self): 129 | data_coco = {} 130 | data_coco["images"] = self.images 131 | data_coco["categories"] = self.categories 132 | data_coco["annotations"] = self.annotations 133 | return data_coco 134 | 135 | def save_json(self): 136 | print("save coco json") 137 | self.data_transfer() 138 | self.data_coco = self.data2coco() 139 | 140 | print(self.save_json_path) 141 | os.makedirs( 142 | os.path.dirname(os.path.abspath(self.save_json_path)), exist_ok=True 143 | ) 144 | json.dump(self.data_coco, open(self.save_json_path, "w"), indent=4) 145 | 146 | 147 | if __name__ == "__main__": 148 | import argparse 149 | 150 | parser = argparse.ArgumentParser( 151 | description="labelme annotation to coco data json file." 152 | ) 153 | parser.add_argument( 154 | "labelme_images", 155 | help="Directory to labelme images and annotation json files.", 156 | type=str, 157 | ) 158 | parser.add_argument( 159 | "--output", help="Output json file path.", default="trainval.json" 160 | ) 161 | args = parser.parse_args() 162 | labelme_json = glob.glob(os.path.join(args.labelme_images, "*.json")) 163 | labelme2coco(labelme_json, args.output) 164 | -------------------------------------------------------------------------------- /COCO_Image_Viewer.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# COCO Image Viewer\n", 8 | "This notebook will allow you to view details about a COCO dataset and preview segmentations on annotated images.\n", 9 | "Learn more about it at: http://cocodataset.org/" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "metadata": { 16 | "ExecuteTime": { 17 | "end_time": "2019-07-27T08:58:05.441736Z", 18 | "start_time": "2019-07-27T08:58:05.075740Z" 19 | } 20 | }, 21 | "outputs": [], 22 | "source": [ 23 | "import IPython\n", 24 | "import os\n", 25 | "import json\n", 26 | "import random\n", 27 | "import numpy as np\n", 28 | "import requests\n", 29 | "from io import BytesIO\n", 30 | "from math import trunc\n", 31 | "from PIL import Image as PILImage\n", 32 | "from PIL import ImageDraw as PILImageDraw" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 2, 38 | "metadata": { 39 | "ExecuteTime": { 40 | "end_time": "2019-07-27T08:58:05.486614Z", 41 | "start_time": "2019-07-27T08:58:05.443733Z" 42 | } 43 | }, 44 | "outputs": [], 45 | "source": [ 46 | "# Load the dataset json\n", 47 | "class CocoDataset():\n", 48 | " def __init__(self, annotation_path, image_dir):\n", 49 | " self.annotation_path = annotation_path\n", 50 | " self.image_dir = image_dir\n", 51 | " self.colors = ['blue', 'purple', 'red', 'green', 'orange', 'salmon', 'pink', 'gold',\n", 52 | " 'orchid', 'slateblue', 'limegreen', 'seagreen', 'darkgreen', 'olive',\n", 53 | " 'teal', 'aquamarine', 'steelblue', 'powderblue', 'dodgerblue', 'navy',\n", 54 | " 'magenta', 'sienna', 'maroon']\n", 55 | "\n", 56 | " json_file = open(self.annotation_path)\n", 57 | " self.coco = json.load(json_file)\n", 58 | " json_file.close()\n", 59 | "\n", 60 | " self.process_info()\n", 61 | " self.process_licenses()\n", 62 | " self.process_categories()\n", 63 | " self.process_images()\n", 64 | " self.process_segmentations()\n", 65 | "\n", 66 | " def display_info(self):\n", 67 | " print('Dataset Info:')\n", 68 | " print('=============')\n", 69 | " if self.info is None:\n", 70 | " return\n", 71 | " for key, item in self.info.items():\n", 72 | " print(' {}: {}'.format(key, item))\n", 73 | "\n", 74 | " requirements = [['description', str],\n", 75 | " ['url', str],\n", 76 | " ['version', str],\n", 77 | " ['year', int],\n", 78 | " ['contributor', str],\n", 79 | " ['date_created', str]]\n", 80 | " for req, req_type in requirements:\n", 81 | " if req not in self.info:\n", 82 | " print('ERROR: {} is missing'.format(req))\n", 83 | " elif type(self.info[req]) != req_type:\n", 84 | " print('ERROR: {} should be type {}'.format(req, str(req_type)))\n", 85 | " print('')\n", 86 | "\n", 87 | " def display_licenses(self):\n", 88 | " print('Licenses:')\n", 89 | " print('=========')\n", 90 | "\n", 91 | " if self.licenses is None:\n", 92 | " return\n", 93 | " requirements = [['id', int],\n", 94 | " ['url', str],\n", 95 | " ['name', str]]\n", 96 | " for license in self.licenses:\n", 97 | " for key, item in license.items():\n", 98 | " print(' {}: {}'.format(key, item))\n", 99 | " for req, req_type in requirements:\n", 100 | " if req not in license:\n", 101 | " print('ERROR: {} is missing'.format(req))\n", 102 | " elif type(license[req]) != req_type:\n", 103 | " print('ERROR: {} should be type {}'.format(\n", 104 | " req, str(req_type)))\n", 105 | " print('')\n", 106 | " print('')\n", 107 | "\n", 108 | " def display_categories(self):\n", 109 | " print('Categories:')\n", 110 | " print('=========')\n", 111 | " for sc_key, sc_val in self.super_categories.items():\n", 112 | " print(' super_category: {}'.format(sc_key))\n", 113 | " for cat_id in sc_val:\n", 114 | " print(' id {}: {}'.format(\n", 115 | " cat_id, self.categories[cat_id]['name']))\n", 116 | " print('')\n", 117 | "\n", 118 | " def display_image(self, image_id, show_polys=True, show_bbox=True, show_crowds=True, use_url=False):\n", 119 | " print('Image:')\n", 120 | " print('======')\n", 121 | " if image_id == 'random':\n", 122 | " image_id = random.choice(list(self.images.keys()))\n", 123 | "\n", 124 | " # Print the image info\n", 125 | " image = self.images[image_id]\n", 126 | " for key, val in image.items():\n", 127 | " print(' {}: {}'.format(key, val))\n", 128 | "\n", 129 | " # Open the image\n", 130 | " if use_url:\n", 131 | " image_path = image['coco_url']\n", 132 | " response = requests.get(image_path)\n", 133 | " image = PILImage.open(BytesIO(response.content))\n", 134 | "\n", 135 | " else:\n", 136 | " # image_path = os.path.join(self.image_dir, image['file_name'])\n", 137 | " image_path = \"{}/{}\".format(self.image_dir, image['file_name'])\n", 138 | " image = PILImage.open(image_path)\n", 139 | "\n", 140 | " # Calculate the size and adjusted display size\n", 141 | " max_width = 600\n", 142 | " image_width, image_height = image.size\n", 143 | " adjusted_width = min(image_width, max_width)\n", 144 | " adjusted_ratio = adjusted_width / image_width\n", 145 | " adjusted_height = adjusted_ratio * image_height\n", 146 | "\n", 147 | " # Create list of polygons to be drawn\n", 148 | " polygons = {}\n", 149 | " bbox_polygons = {}\n", 150 | " rle_regions = {}\n", 151 | " poly_colors = {}\n", 152 | " bbox_categories = {}\n", 153 | " print(' segmentations ({}):'.format(\n", 154 | " len(self.segmentations[image_id])))\n", 155 | " for i, segm in enumerate(self.segmentations[image_id]):\n", 156 | " polygons_list = []\n", 157 | " if segm['iscrowd'] != 0:\n", 158 | " # Gotta decode the RLE\n", 159 | " px = 0\n", 160 | " x, y = 0, 0\n", 161 | " rle_list = []\n", 162 | " for j, counts in enumerate(segm['segmentation']['counts']):\n", 163 | " if j % 2 == 0:\n", 164 | " # Empty pixels\n", 165 | " px += counts\n", 166 | " else:\n", 167 | " # Need to draw on these pixels, since we are drawing in vector form,\n", 168 | " # we need to draw horizontal lines on the image\n", 169 | " x_start = trunc(\n", 170 | " trunc(px / image_height) * adjusted_ratio)\n", 171 | " y_start = trunc(px % image_height * adjusted_ratio)\n", 172 | " px += counts\n", 173 | " x_end = trunc(trunc(px / image_height)\n", 174 | " * adjusted_ratio)\n", 175 | " y_end = trunc(px % image_height * adjusted_ratio)\n", 176 | " if x_end == x_start:\n", 177 | " # This is only on one line\n", 178 | " rle_list.append(\n", 179 | " {'x': x_start, 'y': y_start, 'width': 1, 'height': (y_end - y_start)})\n", 180 | " if x_end > x_start:\n", 181 | " # This spans more than one line\n", 182 | " # Insert top line first\n", 183 | " rle_list.append(\n", 184 | " {'x': x_start, 'y': y_start, 'width': 1, 'height': (image_height - y_start)})\n", 185 | "\n", 186 | " # Insert middle lines if needed\n", 187 | " lines_spanned = x_end - x_start + 1 # total number of lines spanned\n", 188 | " full_lines_to_insert = lines_spanned - 2\n", 189 | " if full_lines_to_insert > 0:\n", 190 | " full_lines_to_insert = trunc(\n", 191 | " full_lines_to_insert * adjusted_ratio)\n", 192 | " rle_list.append(\n", 193 | " {'x': (x_start + 1), 'y': 0, 'width': full_lines_to_insert, 'height': image_height})\n", 194 | "\n", 195 | " # Insert bottom line\n", 196 | " rle_list.append(\n", 197 | " {'x': x_end, 'y': 0, 'width': 1, 'height': y_end})\n", 198 | " if len(rle_list) > 0:\n", 199 | " rle_regions[segm['id']] = rle_list\n", 200 | " else:\n", 201 | " # Add the polygon segmentation\n", 202 | " for segmentation_points in segm['segmentation']:\n", 203 | " segmentation_points = np.multiply(\n", 204 | " segmentation_points, adjusted_ratio).astype(int)\n", 205 | " polygons_list.append(\n", 206 | " str(segmentation_points).lstrip('[').rstrip(']'))\n", 207 | " polygons[segm['id']] = polygons_list\n", 208 | " if i < len(self.colors):\n", 209 | " poly_colors[segm['id']] = self.colors[i]\n", 210 | " else:\n", 211 | " poly_colors[segm['id']] = 'white'\n", 212 | "\n", 213 | " bbox = segm['bbox']\n", 214 | " bbox_points = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1],\n", 215 | " bbox[0] + bbox[2], bbox[1] +\n", 216 | " bbox[3], bbox[0], bbox[1] + bbox[3],\n", 217 | " bbox[0], bbox[1]]\n", 218 | " bbox_points = np.multiply(bbox_points, adjusted_ratio).astype(int)\n", 219 | " bbox_polygons[segm['id']] = str(\n", 220 | " bbox_points).lstrip('[').rstrip(']')\n", 221 | " bbox_categories[segm['id']] = self.categories[segm['category_id']]\n", 222 | " # Print details\n", 223 | " print(' {}:{}:{}'.format(\n", 224 | " segm['id'], poly_colors[segm['id']], self.categories[segm['category_id']]))\n", 225 | "\n", 226 | " # Draw segmentation polygons on image\n", 227 | " html = '
'\n", 228 | " html += ''.format(\n", 229 | " image_path, adjusted_width)\n", 230 | " html += '
'.format(\n", 231 | " adjusted_width, adjusted_height)\n", 232 | "\n", 233 | " if show_polys:\n", 234 | " for seg_id, points_list in polygons.items():\n", 235 | " fill_color = poly_colors[seg_id]\n", 236 | " stroke_color = poly_colors[seg_id]\n", 237 | " for points in points_list:\n", 238 | " html += ''.format(\n", 239 | " points, fill_color, stroke_color)\n", 240 | "\n", 241 | " if show_crowds:\n", 242 | " for seg_id, rect_list in rle_regions.items():\n", 243 | " fill_color = poly_colors[seg_id]\n", 244 | " stroke_color = poly_colors[seg_id]\n", 245 | " for rect_def in rect_list:\n", 246 | " x, y = rect_def['x'], rect_def['y']\n", 247 | " w, h = rect_def['width'], rect_def['height']\n", 248 | " html += ''.format(\n", 249 | " x, y, w, h, fill_color, stroke_color)\n", 250 | "\n", 251 | " if show_bbox:\n", 252 | " for seg_id, points in bbox_polygons.items():\n", 253 | " x, y = [int(i) for i in points.split()[:2]]\n", 254 | " html += '{}'.format(\n", 255 | " x, y, bbox_categories[seg_id][\"name\"])\n", 256 | " fill_color = poly_colors[seg_id]\n", 257 | " stroke_color = poly_colors[seg_id]\n", 258 | " html += ''.format(\n", 259 | " points, fill_color, stroke_color)\n", 260 | "\n", 261 | " html += '
'\n", 262 | " html += '
'\n", 263 | " html += ''\n", 266 | " return html\n", 267 | "\n", 268 | " def process_info(self):\n", 269 | " self.info = self.coco.get('info')\n", 270 | "\n", 271 | " def process_licenses(self):\n", 272 | " self.licenses = self.coco.get('licenses')\n", 273 | "\n", 274 | " def process_categories(self):\n", 275 | " self.categories = {}\n", 276 | " self.super_categories = {}\n", 277 | " for category in self.coco['categories']:\n", 278 | " cat_id = category['id']\n", 279 | " super_category = category['supercategory']\n", 280 | "\n", 281 | " # Add category to the categories dict\n", 282 | " if cat_id not in self.categories:\n", 283 | " self.categories[cat_id] = category\n", 284 | " else:\n", 285 | " print(\"ERROR: Skipping duplicate category id: {}\".format(category))\n", 286 | "\n", 287 | " # Add category to super_categories dict\n", 288 | " if super_category not in self.super_categories:\n", 289 | " # Create a new set with the category id\n", 290 | " self.super_categories[super_category] = {cat_id}\n", 291 | " else:\n", 292 | " self.super_categories[super_category] |= {\n", 293 | " cat_id} # Add category id to the set\n", 294 | "\n", 295 | " def process_images(self):\n", 296 | " self.images = {}\n", 297 | " for image in self.coco['images']:\n", 298 | " image_id = image['id']\n", 299 | " if image_id in self.images:\n", 300 | " print(\"ERROR: Skipping duplicate image id: {}\".format(image))\n", 301 | " else:\n", 302 | " self.images[image_id] = image\n", 303 | "\n", 304 | " def process_segmentations(self):\n", 305 | " self.segmentations = {}\n", 306 | " for segmentation in self.coco['annotations']:\n", 307 | " image_id = segmentation['image_id']\n", 308 | " if image_id not in self.segmentations:\n", 309 | " self.segmentations[image_id] = []\n", 310 | " self.segmentations[image_id].append(segmentation)" 311 | ] 312 | }, 313 | { 314 | "cell_type": "code", 315 | "execution_count": 3, 316 | "metadata": { 317 | "ExecuteTime": { 318 | "end_time": "2019-07-27T08:58:05.510585Z", 319 | "start_time": "2019-07-27T08:58:05.489608Z" 320 | } 321 | }, 322 | "outputs": [ 323 | { 324 | "name": "stdout", 325 | "output_type": "stream", 326 | "text": [ 327 | "Dataset Info:\n", 328 | "=============\n", 329 | "Licenses:\n", 330 | "=========\n", 331 | "Categories:\n", 332 | "=========\n", 333 | " super_category: date\n", 334 | " id 1: date\n", 335 | "\n", 336 | " super_category: fig\n", 337 | " id 2: fig\n", 338 | "\n", 339 | " super_category: hazelnut\n", 340 | " id 3: hazelnut\n", 341 | "\n" 342 | ] 343 | } 344 | ], 345 | "source": [ 346 | "annotation_path = \"trainval.json\"\n", 347 | "image_dir = \"images\"\n", 348 | "\n", 349 | "coco_dataset = CocoDataset(annotation_path, image_dir)\n", 350 | "coco_dataset.display_info()\n", 351 | "coco_dataset.display_licenses()\n", 352 | "coco_dataset.display_categories()" 353 | ] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": 4, 358 | "metadata": { 359 | "ExecuteTime": { 360 | "end_time": "2019-07-27T08:58:05.520552Z", 361 | "start_time": "2019-07-27T08:58:05.512547Z" 362 | } 363 | }, 364 | "outputs": [ 365 | { 366 | "name": "stdout", 367 | "output_type": "stream", 368 | "text": [ 369 | "0 {'height': 600, 'width': 800, 'id': 0, 'file_name': '0.jpg'}\n", 370 | "1 {'height': 600, 'width': 800, 'id': 1, 'file_name': '1.jpg'}\n", 371 | "2 {'height': 600, 'width': 800, 'id': 2, 'file_name': '10.jpg'}\n", 372 | "3 {'height': 600, 'width': 800, 'id': 3, 'file_name': '11.jpg'}\n", 373 | "4 {'height': 600, 'width': 800, 'id': 4, 'file_name': '12.jpg'}\n", 374 | "5 {'height': 600, 'width': 800, 'id': 5, 'file_name': '13.jpg'}\n", 375 | "6 {'height': 600, 'width': 800, 'id': 6, 'file_name': '14.jpg'}\n", 376 | "7 {'height': 600, 'width': 800, 'id': 7, 'file_name': '15.jpg'}\n", 377 | "8 {'height': 600, 'width': 800, 'id': 8, 'file_name': '16.jpg'}\n", 378 | "9 {'height': 600, 'width': 800, 'id': 9, 'file_name': '17.jpg'}\n", 379 | "10 {'height': 600, 'width': 800, 'id': 10, 'file_name': '2.jpg'}\n", 380 | "11 {'height': 600, 'width': 800, 'id': 11, 'file_name': '3.jpg'}\n", 381 | "12 {'height': 600, 'width': 800, 'id': 12, 'file_name': '4.jpg'}\n", 382 | "13 {'height': 600, 'width': 800, 'id': 13, 'file_name': '5.jpg'}\n", 383 | "14 {'height': 600, 'width': 800, 'id': 14, 'file_name': '6.jpg'}\n", 384 | "15 {'height': 600, 'width': 800, 'id': 15, 'file_name': '7.jpg'}\n", 385 | "16 {'height': 600, 'width': 800, 'id': 16, 'file_name': '8.jpg'}\n", 386 | "17 {'height': 600, 'width': 800, 'id': 17, 'file_name': '9.jpg'}\n" 387 | ] 388 | } 389 | ], 390 | "source": [ 391 | "for k, v in coco_dataset.images.items():\n", 392 | " print(k, v)" 393 | ] 394 | }, 395 | { 396 | "cell_type": "code", 397 | "execution_count": 5, 398 | "metadata": { 399 | "ExecuteTime": { 400 | "end_time": "2019-07-27T08:58:05.572387Z", 401 | "start_time": "2019-07-27T08:58:05.523519Z" 402 | }, 403 | "scrolled": false 404 | }, 405 | "outputs": [ 406 | { 407 | "name": "stdout", 408 | "output_type": "stream", 409 | "text": [ 410 | "Image:\n", 411 | "======\n", 412 | " height: 600\n", 413 | " width: 800\n", 414 | " id: 5\n", 415 | " file_name: 13.jpg\n", 416 | " segmentations (8):\n", 417 | " 53:blue:{'supercategory': 'date', 'id': 1, 'name': 'date'}\n", 418 | " 54:purple:{'supercategory': 'date', 'id': 1, 'name': 'date'}\n", 419 | " 55:red:{'supercategory': 'date', 'id': 1, 'name': 'date'}\n", 420 | " 56:green:{'supercategory': 'hazelnut', 'id': 3, 'name': 'hazelnut'}\n", 421 | " 57:orange:{'supercategory': 'hazelnut', 'id': 3, 'name': 'hazelnut'}\n", 422 | " 58:salmon:{'supercategory': 'hazelnut', 'id': 3, 'name': 'hazelnut'}\n", 423 | " 59:pink:{'supercategory': 'hazelnut', 'id': 3, 'name': 'hazelnut'}\n", 424 | " 60:gold:{'supercategory': 'fig', 'id': 2, 'name': 'fig'}\n" 425 | ] 426 | }, 427 | { 428 | "data": { 429 | "text/html": [ 430 | "
datedatedatehazelnuthazelnuthazelnuthazelnutfig
" 439 | ], 440 | "text/plain": [ 441 | "" 442 | ] 443 | }, 444 | "execution_count": 5, 445 | "metadata": {}, 446 | "output_type": "execute_result" 447 | } 448 | ], 449 | "source": [ 450 | "html = coco_dataset.display_image(5, use_url=False)\n", 451 | "IPython.display.HTML(html)" 452 | ] 453 | }, 454 | { 455 | "cell_type": "code", 456 | "execution_count": 6, 457 | "metadata": { 458 | "ExecuteTime": { 459 | "end_time": "2019-07-27T08:58:05.587373Z", 460 | "start_time": "2019-07-27T08:58:05.574383Z" 461 | } 462 | }, 463 | "outputs": [ 464 | { 465 | "name": "stdout", 466 | "output_type": "stream", 467 | "text": [ 468 | "Image:\n", 469 | "======\n", 470 | " height: 600\n", 471 | " width: 800\n", 472 | " id: 1\n", 473 | " file_name: 1.jpg\n", 474 | " segmentations (9):\n", 475 | " 13:blue:{'supercategory': 'hazelnut', 'id': 3, 'name': 'hazelnut'}\n", 476 | " 14:purple:{'supercategory': 'hazelnut', 'id': 3, 'name': 'hazelnut'}\n", 477 | " 15:red:{'supercategory': 'hazelnut', 'id': 3, 'name': 'hazelnut'}\n", 478 | " 16:green:{'supercategory': 'date', 'id': 1, 'name': 'date'}\n", 479 | " 17:orange:{'supercategory': 'date', 'id': 1, 'name': 'date'}\n", 480 | " 18:salmon:{'supercategory': 'date', 'id': 1, 'name': 'date'}\n", 481 | " 19:pink:{'supercategory': 'date', 'id': 1, 'name': 'date'}\n", 482 | " 20:gold:{'supercategory': 'date', 'id': 1, 'name': 'date'}\n", 483 | " 21:orchid:{'supercategory': 'fig', 'id': 2, 'name': 'fig'}\n" 484 | ] 485 | }, 486 | { 487 | "data": { 488 | "text/html": [ 489 | "
hazelnuthazelnuthazelnutdatedatedatedatedatefig
" 498 | ], 499 | "text/plain": [ 500 | "" 501 | ] 502 | }, 503 | "execution_count": 6, 504 | "metadata": {}, 505 | "output_type": "execute_result" 506 | } 507 | ], 508 | "source": [ 509 | "html = coco_dataset.display_image(1, use_url=False)\n", 510 | "IPython.display.HTML(html)" 511 | ] 512 | }, 513 | { 514 | "cell_type": "code", 515 | "execution_count": null, 516 | "metadata": {}, 517 | "outputs": [], 518 | "source": [] 519 | } 520 | ], 521 | "metadata": { 522 | "kernelspec": { 523 | "display_name": "Python 3", 524 | "language": "python", 525 | "name": "python3" 526 | }, 527 | "language_info": { 528 | "codemirror_mode": { 529 | "name": "ipython", 530 | "version": 3 531 | }, 532 | "file_extension": ".py", 533 | "mimetype": "text/x-python", 534 | "name": "python", 535 | "nbconvert_exporter": "python", 536 | "pygments_lexer": "ipython3", 537 | "version": "3.6.8" 538 | }, 539 | "toc": { 540 | "base_numbering": 1, 541 | "nav_menu": {}, 542 | "number_sections": true, 543 | "sideBar": true, 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", 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