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zeeshanalipanhwar/COCO_Image_Viewer.ipynb

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Created December 16, 2020 18:41
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COCO Image Viewer | immersivelimit.com/tutorials/create-coco-annotations-from-scratch
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COCO_Image_Viewer.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# COCO Image Viewer\n",
"This notebook will allow you to view details about a COCO dataset and preview segmentations on annotated images.\n",
"Learn more about it at: http://cocodataset.org/\n",
"\n",
"Note: Gist probably won't show the segmentations, but if you run this code in your own Jupyter Notebook, you'll see them.\n",
"\n",
"The rest of the tutorial can be found at: http://www.immersivelimit.com/tutorials/create-coco-annotations-from-scratch"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import IPython\n",
"import os\n",
"import json\n",
"import random\n",
"import numpy as np\n",
"import requests\n",
"from io import BytesIO\n",
"import base64\n",
"from math import trunc\n",
"from PIL import Image as PILImage\n",
"from PIL import ImageDraw as PILImageDraw"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Load the dataset json\n",
"class CocoDataset():\n",
" def __init__(self, annotation_path, image_dir):\n",
" self.annotation_path = annotation_path\n",
" self.image_dir = image_dir\n",
" self.colors = colors = ['blue', 'purple', 'red', 'green', 'orange', 'salmon', 'pink', 'gold',\n",
" 'orchid', 'slateblue', 'limegreen', 'seagreen', 'darkgreen', 'olive',\n",
" 'teal', 'aquamarine', 'steelblue', 'powderblue', 'dodgerblue', 'navy',\n",
" 'magenta', 'sienna', 'maroon']\n",
" \n",
" json_file = open(self.annotation_path)\n",
" self.coco = json.load(json_file)\n",
" json_file.close()\n",
" \n",
" self.process_info()\n",
" self.process_licenses()\n",
" self.process_categories()\n",
" self.process_images()\n",
" self.process_segmentations()\n",
" \n",
" \n",
" def display_info(self):\n",
" print('Dataset Info:')\n",
" print('=============')\n",
" for key, item in self.info.items():\n",
" print(' {}: {}'.format(key, item))\n",
" \n",
" requirements = [['description', str],\n",
" ['url', str],\n",
" ['version', str],\n",
" ['year', int],\n",
" ['contributor', str],\n",
" ['date_created', str]]\n",
" for req, req_type in requirements:\n",
" if req not in self.info:\n",
" print('ERROR: {} is missing'.format(req))\n",
" elif type(self.info[req]) != req_type:\n",
" print('ERROR: {} should be type {}'.format(req, str(req_type)))\n",
" print('')\n",
"\n",
" \n",
" def display_licenses(self):\n",
" print('Licenses:')\n",
" print('=========')\n",
" \n",
" requirements = [['id', int],\n",
" ['url', str],\n",
" ['name', str]]\n",
" for license in self.licenses:\n",
" for key, item in license.items():\n",
" print(' {}: {}'.format(key, item))\n",
" for req, req_type in requirements:\n",
" if req not in license:\n",
" print('ERROR: {} is missing'.format(req))\n",
" elif type(license[req]) != req_type:\n",
" print('ERROR: {} should be type {}'.format(req, str(req_type)))\n",
" print('')\n",
" print('')\n",
" \n",
" def display_categories(self):\n",
" print('Categories:')\n",
" print('=========')\n",
" for sc_key, sc_val in self.super_categories.items():\n",
" print(' super_category: {}'.format(sc_key))\n",
" for cat_id in sc_val:\n",
" print(' id {}: {}'.format(cat_id, self.categories[cat_id]['name']))\n",
" print('')\n",
" \n",
" def display_image(self, image_id, show_polys=True, show_bbox=True, show_crowds=True, use_url=False):\n",
" print('Image:')\n",
" print('======')\n",
" if image_id == 'random':\n",
" image_id = random.choice(list(self.images.keys()))\n",
" \n",
" # Print the image info\n",
" image = self.images[image_id]\n",
" for key, val in image.items():\n",
" print(' {}: {}'.format(key, val))\n",
" \n",
" # Open the image\n",
" if use_url:\n",
" image_path = image['coco_url']\n",
" response = requests.get(image_path)\n",
" image = PILImage.open(BytesIO(response.content))\n",
" \n",
" else:\n",
" image_path = os.path.join(self.image_dir, image['file_name'])\n",
" image = PILImage.open(image_path)\n",
" \n",
" buffer = BytesIO()\n",
" image.save(buffer, format='PNG')\n",
" buffer.seek(0)\n",
" \n",
" data_uri = base64.b64encode(buffer.read()).decode('ascii')\n",
" image_path = \"data:image/png;base64,{0}\".format(data_uri)\n",
" \n",
" # Calculate the size and adjusted display size\n",
" max_width = 600\n",
" image_width, image_height = image.size\n",
" adjusted_width = min(image_width, max_width)\n",
" adjusted_ratio = adjusted_width / image_width\n",
" adjusted_height = adjusted_ratio * image_height\n",
" \n",
" # Create list of polygons to be drawn\n",
" polygons = {}\n",
" bbox_polygons = {}\n",
" rle_regions = {}\n",
" poly_colors = {}\n",
" print(' segmentations ({}):'.format(len(self.segmentations[image_id])))\n",
" for i, segm in enumerate(self.segmentations[image_id]):\n",
" polygons_list = []\n",
" if segm['iscrowd'] != 0:\n",
" # Gotta decode the RLE\n",
" px = 0\n",
" x, y = 0, 0\n",
" rle_list = []\n",
" for j, counts in enumerate(segm['segmentation']['counts']):\n",
" if j % 2 == 0:\n",
" # Empty pixels\n",
" px += counts\n",
" else:\n",
" # Need to draw on these pixels, since we are drawing in vector form,\n",
" # we need to draw horizontal lines on the image\n",
" x_start = trunc(trunc(px / image_height) * adjusted_ratio)\n",
" y_start = trunc(px % image_height * adjusted_ratio)\n",
" px += counts\n",
" x_end = trunc(trunc(px / image_height) * adjusted_ratio)\n",
" y_end = trunc(px % image_height * adjusted_ratio)\n",
" if x_end == x_start:\n",
" # This is only on one line\n",
" rle_list.append({'x': x_start, 'y': y_start, 'width': 1 , 'height': (y_end - y_start)})\n",
" if x_end > x_start:\n",
" # This spans more than one line\n",
" # Insert top line first\n",
" rle_list.append({'x': x_start, 'y': y_start, 'width': 1, 'height': (image_height - y_start)})\n",
" \n",
" # Insert middle lines if needed\n",
" lines_spanned = x_end - x_start + 1 # total number of lines spanned\n",
" full_lines_to_insert = lines_spanned - 2\n",
" if full_lines_to_insert > 0:\n",
" full_lines_to_insert = trunc(full_lines_to_insert * adjusted_ratio)\n",
" rle_list.append({'x': (x_start + 1), 'y': 0, 'width': full_lines_to_insert, 'height': image_height})\n",
" \n",
" # Insert bottom line\n",
" rle_list.append({'x': x_end, 'y': 0, 'width': 1, 'height': y_end})\n",
" if len(rle_list) > 0:\n",
" rle_regions[segm['id']] = rle_list \n",
" else:\n",
" # Add the polygon segmentation\n",
" for segmentation_points in segm['segmentation']:\n",
" segmentation_points = np.multiply(segmentation_points, adjusted_ratio).astype(int)\n",
" polygons_list.append(str(segmentation_points).lstrip('[').rstrip(']'))\n",
" polygons[segm['id']] = polygons_list\n",
" if i < len(self.colors):\n",
" poly_colors[segm['id']] = self.colors[i]\n",
" else:\n",
" poly_colors[segm['id']] = 'white'\n",
" \n",
" bbox = segm['bbox']\n",
" bbox_points = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1],\n",
" bbox[0] + bbox[2], bbox[1] + bbox[3], bbox[0], bbox[1] + bbox[3],\n",
" bbox[0], bbox[1]]\n",
" bbox_points = np.multiply(bbox_points, adjusted_ratio).astype(int)\n",
" bbox_polygons[segm['id']] = str(bbox_points).lstrip('[').rstrip(']')\n",
" \n",
" # Print details\n",
" print(' {}:{}:{}'.format(segm['id'], poly_colors[segm['id']], self.categories[segm['category_id']]))\n",
" \n",
" \n",
" \n",
" # Draw segmentation polygons on image\n",
" html = '<div class=\"container\" style=\"position:relative;\">'\n",
" html += '<img src=\"{}\" style=\"position:relative;top:0px;left:0px;width:{}px;\">'.format(image_path, adjusted_width)\n",
" html += '<div class=\"svgclass\"><svg width=\"{}\" height=\"{}\">'.format(adjusted_width, adjusted_height)\n",
" \n",
" if show_polys:\n",
" for seg_id, points_list in polygons.items():\n",
" fill_color = poly_colors[seg_id]\n",
" stroke_color = poly_colors[seg_id]\n",
" for points in points_list:\n",
" html += '<polygon points=\"{}\" style=\"fill:{}; stroke:{}; stroke-width:1; fill-opacity:0.5\" />'.format(points, fill_color, stroke_color)\n",
" \n",
" if show_crowds:\n",
" for seg_id, rect_list in rle_regions.items():\n",
" fill_color = poly_colors[seg_id]\n",
" stroke_color = poly_colors[seg_id]\n",
" for rect_def in rect_list:\n",
" x, y = rect_def['x'], rect_def['y']\n",
" w, h = rect_def['width'], rect_def['height']\n",
" html += '<rect x=\"{}\" y=\"{}\" width=\"{}\" height=\"{}\" style=\"fill:{}; stroke:{}; stroke-width:1; fill-opacity:0.5; stroke-opacity:0.5\" />'.format(x, y, w, h, fill_color, stroke_color)\n",
" \n",
" if show_bbox:\n",
" for seg_id, points in bbox_polygons.items():\n",
" fill_color = poly_colors[seg_id]\n",
" stroke_color = poly_colors[seg_id]\n",
" html += '<polygon points=\"{}\" style=\"fill:{}; stroke:{}; stroke-width:1; fill-opacity:0\" />'.format(points, fill_color, stroke_color)\n",
" \n",
" html += '</svg></div>'\n",
" html += '</div>'\n",
" html += '<style>'\n",
" html += '.svgclass { position:absolute; top:0px; left:0px;}'\n",
" html += '</style>'\n",
" return html\n",
" \n",
" def process_info(self):\n",
" self.info = self.coco['info']\n",
" \n",
" def process_licenses(self):\n",
" self.licenses = self.coco['licenses']\n",
" \n",
" def process_categories(self):\n",
" self.categories = {}\n",
" self.super_categories = {}\n",
" for category in self.coco['categories']:\n",
" cat_id = category['id']\n",
" super_category = category['supercategory']\n",
" \n",
" # Add category to the categories dict\n",
" if cat_id not in self.categories:\n",
" self.categories[cat_id] = category\n",
" else:\n",
" print(\"ERROR: Skipping duplicate category id: {}\".format(category))\n",
"\n",
" # Add category to super_categories dict\n",
" if super_category not in self.super_categories:\n",
" self.super_categories[super_category] = {cat_id} # Create a new set with the category id\n",
" else:\n",
" self.super_categories[super_category] |= {cat_id} # Add category id to the set\n",
" \n",
" def process_images(self):\n",
" self.images = {}\n",
" for image in self.coco['images']:\n",
" image_id = image['id']\n",
" if image_id in self.images:\n",
" print(\"ERROR: Skipping duplicate image id: {}\".format(image))\n",
" else:\n",
" self.images[image_id] = image\n",
" \n",
" def process_segmentations(self):\n",
" self.segmentations = {}\n",
" for segmentation in self.coco['annotations']:\n",
" image_id = segmentation['image_id']\n",
" if image_id not in self.segmentations:\n",
" self.segmentations[image_id] = []\n",
" self.segmentations[image_id].append(segmentation)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset Info:\n",
"=============\n",
" url: http://cocodataset.org\n",
" year: 2017\n",
" description: COCO 2017 Dataset\n",
" contributor: COCO Consortium\n",
" date_created: 2017/09/01\n",
" version: 1.0\n",
"\n",
"Licenses:\n",
"=========\n",
" url: http://creativecommons.org/licenses/by/2.0/\n",
" id: 4\n",
" name: Attribution License\n",
"\n",
"\n",
"Categories:\n",
"=========\n",
" super_category: vehicle\n",
" id 2: bicycle\n",
"\n",
" super_category: animal\n",
" id 22: elephant\n",
"\n"
]
}
],
"source": [
"annotation_path = './sample_annotations.json'\n",
"image_dir = './images'\n",
"\n",
"coco_dataset = CocoDataset(annotation_path, image_dir)\n",
"coco_dataset.display_info()\n",
"coco_dataset.display_licenses()\n",
"coco_dataset.display_categories()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image:\n",
"======\n",
" file_name: 000000242287.jpg\n",
" id: 242287\n",
" coco_url: http://images.cocodataset.org/val2017/000000242287.jpg\n",
" width: 426\n",
" date_captured: 2013-11-15 02:41:42\n",
" license: 4\n",
" flickr_url: http://farm3.staticflickr.com/2626/4072194513_edb6acfb2b_z.jpg\n",
" height: 640\n",
" segmentations (1):\n",
" 125686:blue:{'id': 2, 'name': 'bicycle', 'supercategory': 'vehicle'}\n"
]
},
{
"data": {
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"<div class=\"container\" style=\"position:relative;\"><img src=\"http://images.cocodataset.org/val2017/000000242287.jpg\" style=\"position:relative;top:0px;left:0px;width:426px;\"><div class=\"svgclass\"><svg width=\"426\" height=\"640.0\"><polygon points=\"164 417 164 417 164 417 159 409 159 409 155 409 155 410 152 413 144 413\n",
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"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"html = coco_dataset.display_image(242287, use_url=True)\n",
"IPython.display.HTML(html)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image:\n",
"======\n",
" file_name: 000000245915.jpg\n",
" id: 245915\n",
" coco_url: http://images.cocodataset.org/val2017/000000245915.jpg\n",
" width: 500\n",
" date_captured: 2013-11-18 02:53:27\n",
" license: 4\n",
" flickr_url: http://farm1.staticflickr.com/88/211747310_f58a16631e_z.jpg\n",
" height: 333\n",
" segmentations (14):\n",
" 1409619:blue:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 1410165:purple:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
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" 1410622:green:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 1410759:orange:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 1410834:salmon:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 1410880:pink:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 1411090:gold:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 1411108:orchid:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 1411138:slateblue:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 1411160:limegreen:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 1411174:seagreen:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 2210312:darkgreen:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 902200245915:olive:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n"
]
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"html = coco_dataset.display_image(245915, use_url=True)\n",
"IPython.display.HTML(html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sample Annotations JSON\n",
"Below are the contents of 'sample_annotations.json'."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
"\n",
" \"info\": {\n",
"\n",
" \"description\": \"COCO 2017 Dataset\",\"url\": \"http://cocodataset.org\",\"version\": \"1.0\",\"year\": 2017,\"contributor\": \"COCO Consortium\",\"date_created\": \"2017/09/01\"\n",
"\n",
" },\n",
"\n",
" \"licenses\": [\n",
"\n",
" {\"url\": \"http://creativecommons.org/licenses/by/2.0/\",\"id\": 4,\"name\": \"Attribution License\"}\n",
"\n",
" ],\n",
"\n",
" \"images\": [\n",
"\n",
" {\"id\": 242287, \"license\": 4, \"coco_url\": \"http://images.cocodataset.org/val2017/000000242287.jpg\", \"flickr_url\": \"http://farm3.staticflickr.com/2626/4072194513_edb6acfb2b_z.jpg\", \"width\": 426, \"height\": 640, \"file_name\": \"000000242287.jpg\", \"date_captured\": \"2013-11-15 02:41:42\"},\n",
"\n",
" {\"id\": 245915, \"license\": 4, \"coco_url\": \"http://images.cocodataset.org/val2017/000000245915.jpg\", \"flickr_url\": \"http://farm1.staticflickr.com/88/211747310_f58a16631e_z.jpg\", \"width\": 500, \"height\": 333, \"file_name\": \"000000245915.jpg\", \"date_captured\": \"2013-11-18 02:53:27\"}\n",
"\n",
" ],\n",
"\n",
" \"annotations\": [\n",
"\n",
" {\"id\": 125686, \"category_id\": 2, \"iscrowd\": 0, \"segmentation\": [[164.81, 417.51, 164.81, 417.51, 164.81, 417.51, 159.31, 409.27, 159.31, 409.27, 155.19, 409.27, 155.19, 410.64, 152.45, 413.39, 144.21, 413.39, 140.09, 413.39, 137.34, 413.39, 134.59, 414.76, 129.1, 414.76, 122.23, 414.76, 104.38, 405.15, 104.38, 405.15, 93.39, 401.03, 93.39, 401.03, 87.9, 399.66, 86.52, 399.66, 85.15, 399.66, 93.39, 391.42, 78.28, 383.18, 72.79, 383.18, 68.67, 401.03, 82.4, 402.4, 104.38, 409.27, 104.38, 421.63, 85.15, 458.71, 79.66, 469.7, 65.92, 454.59, 61.8, 455.97, 59.06, 455.97, 42.58, 471.07, 39.83, 479.31, 39.83, 482.06, 31.59, 497.17, 19.23, 510.9, 19.23, 519.14, 19.23, 539.74, 20.6, 578.2, 26.09, 590.56, 27.47, 593.3, 38.45, 596.05, 41.2, 593.3, 50.82, 589.18, 53.56, 587.81, 59.06, 585.06, 71.42, 579.57, 79.66, 568.58, 82.4, 560.34, 85.15, 535.62, 87.9, 520.52, 90.64, 513.65, 93.39, 490.3, 93.39, 487.55, 93.39, 480.69, 108.5, 499.91, 108.5, 509.53, 115.36, 521.89, 104.38, 521.89, 103.0, 528.76, 108.5, 534.25, 119.48, 530.13, 124.98, 535.62, 126.35, 535.62, 129.1, 543.86, 166.18, 582.32, 171.67, 578.2, 197.77, 598.8, 208.76, 608.41, 249.96, 627.64, 269.18, 623.52, 278.8, 618.03, 281.55, 609.79, 285.67, 601.55, 287.04, 597.42, 291.16, 591.93, 291.16, 590.56, 318.63, 590.56, 318.63, 591.93, 329.61, 589.18, 332.36, 586.44, 333.73, 583.69, 309.01, 563.09, 313.13, 547.98, 313.13, 541.12, 313.13, 526.01, 313.13, 523.26, 313.13, 516.39, 313.13, 501.29, 307.64, 486.18, 307.64, 486.18, 302.15, 483.43, 291.16, 472.45, 278.8, 454.59, 273.3, 454.59, 262.32, 447.73, 241.72, 438.11, 226.61, 425.75, 226.61, 420.26, 210.13, 413.39, 206.01, 413.39, 197.77, 414.76, 167.55, 410.64]], \"image_id\": 242287, \"area\": 42061.80340000001, \"bbox\": [19.23, 383.18, 314.5, 244.46]},\n",
"\n",
" {\"id\": 1409619, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[376.81, 238.8, 378.19, 228.91, 382.15, 216.06, 383.14, 210.72, 385.9, 207.56, 386.7, 207.16, 387.29, 201.43, 389.27, 197.67, 396.58, 196.09, 404.49, 198.07, 416.16, 190.95, 434.75, 189.76, 458.48, 190.95, 468.96, 196.88, 473.51, 205.58, 473.31, 210.52, 460.65, 225.36, 447.6, 238.41, 437.91, 239.59, 420.91, 233.07, 413.98, 246.71, 406.27, 245.92, 408.45, 232.87, 409.24, 224.17, 407.66, 221.6, 405.28, 221.6, 402.91, 226.54, 395.99, 228.12, 393.02, 229.11, 388.28, 231.88, 383.93, 236.82, 382.74, 241.17]], \"image_id\": 245915, \"area\": 3556.2197000000015, \"bbox\": [376.81, 189.76, 96.7, 56.95]},\n",
"\n",
" {\"id\": 1410165, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[486.34, 239.01, 477.88, 244.78, 468.26, 245.16, 464.41, 244.78, 458.64, 250.16, 451.72, 249.39, 445.56, 243.62, 448.26, 238.62, 476.72, 206.69, 481.34, 205.54, 485.57, 209.0, 500.0, 205.16, 500.0, 267.47, 500.0, 272.86, 498.26, 276.71, 489.03, 275.94, 488.65, 272.47, 494.8, 251.32, 495.95, 244.39]], \"image_id\": 245915, \"area\": 1775.8932499999994, \"bbox\": [445.56, 205.16, 54.44, 71.55]},\n",
"\n",
" {\"id\": 1410330, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[402.59, 196.81, 410.82, 177.35, 416.81, 171.36, 428.78, 171.36, 433.27, 166.87, 451.98, 164.63, 468.44, 165.38, 480.42, 172.11, 486.4, 181.84, 487.15, 188.58, 493.14, 207.28, 486.4, 193.81, 486.4, 208.78, 481.91, 204.29, 474.43, 211.02, 468.44, 196.06, 460.96, 190.82, 446.0, 190.82, 428.04, 188.58, 412.32, 193.81]], \"image_id\": 245915, \"area\": 2049.9325499999995, \"bbox\": [402.59, 164.63, 90.55, 46.39]},\n",
"\n",
" {\"id\": 1410622, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[480.86, 166.6, 481.62, 161.78, 482.63, 158.99, 483.9, 156.2, 485.42, 151.63, 486.19, 147.32, 486.69, 143.0, 486.95, 139.7, 487.45, 135.13, 487.96, 132.6, 489.23, 127.52, 489.74, 125.74, 493.04, 120.92, 495.83, 120.16, 499.89, 119.91, 499.89, 145.03, 495.07, 144.27, 492.78, 148.33, 491.01, 152.65, 489.23, 160.77, 486.69, 163.56, 486.19, 164.57, 484.92, 167.11, 482.63, 167.87, 481.11, 167.87]], \"image_id\": 245915, \"area\": 419.9524499999999, \"bbox\": [480.86, 119.91, 19.03, 47.96]},\n",
"\n",
" {\"id\": 1410759, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[439.19, 122.67, 440.27, 115.81, 441.34, 106.38, 444.98, 100.16, 450.56, 100.38, 456.99, 101.23, 466.85, 103.59, 479.72, 108.09, 488.72, 114.53, 490.65, 119.67, 491.08, 124.82, 488.72, 131.25, 486.15, 142.4, 485.72, 148.19, 481.65, 145.4, 475.21, 139.4, 472.21, 132.97, 463.64, 125.25, 454.42, 122.46, 447.98, 122.03, 442.41, 122.25]], \"image_id\": 245915, \"area\": 1266.1428000000008, \"bbox\": [439.19, 100.16, 51.89, 48.03]},\n",
"\n",
" {\"id\": 1410834, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[331.42, 255.49, 322.64, 250.05, 318.04, 240.02, 318.46, 250.47, 308.01, 246.71, 306.33, 237.93, 303.82, 219.96, 294.63, 220.79, 284.18, 214.94, 279.16, 212.85, 271.22, 212.01, 270.8, 204.07, 273.73, 199.05, 293.37, 192.78, 302.99, 190.69, 309.26, 191.95, 318.04, 196.13, 333.09, 195.71, 358.59, 210.76, 363.19, 221.63, 363.6, 237.93, 361.93, 261.34, 352.32, 261.76, 350.23, 251.31, 341.87, 250.05, 340.19, 255.49, 352.32, 262.18, 356.08, 264.27, 356.08, 265.94, 343.96, 264.69]], \"image_id\": 245915, \"area\": 3901.5125, \"bbox\": [270.8, 190.69, 92.8, 75.25]},\n",
"\n",
" {\"id\": 1410880, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[222.29, 191.74, 233.36, 181.64, 241.18, 167.63, 248.35, 154.93, 257.47, 153.95, 281.57, 149.39, 294.27, 149.39, 300.79, 152.98, 312.84, 160.14, 317.4, 168.29, 318.38, 172.52, 321.96, 178.38, 322.29, 182.62, 321.63, 184.25, 317.07, 188.16, 317.4, 195.97, 306.0, 189.78, 301.11, 188.81, 294.27, 188.81, 281.57, 186.53, 283.2, 195.65, 277.01, 195.65, 274.73, 189.78, 269.19, 195.65, 263.33, 196.62, 263.66, 185.88, 257.47, 195.32, 249.32, 184.25, 244.44, 183.92, 233.69, 189.13, 229.13, 192.39]], \"image_id\": 245915, \"area\": 2987.1327, \"bbox\": [222.29, 149.39, 100.0, 47.23]},\n",
"\n",
" {\"id\": 1411090, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[245.09, 145.01, 245.09, 138.15, 247.12, 132.57, 250.93, 126.73, 258.29, 125.21, 267.68, 125.97, 281.64, 126.23, 290.78, 127.75, 299.91, 135.62, 306.0, 140.95, 307.27, 144.25, 308.79, 149.32, 312.35, 151.86, 312.35, 155.67, 311.84, 158.97, 301.43, 153.64, 294.58, 150.08, 262.86, 150.84, 252.71, 148.05]], \"image_id\": 245915, \"area\": 1372.6493000000003, \"bbox\": [245.09, 125.21, 67.26, 33.76]},\n",
"\n",
" {\"id\": 1411108, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[347.3, 157.93, 341.38, 156.45, 338.91, 159.9, 341.38, 167.31, 336.94, 167.8, 328.54, 155.95, 324.59, 151.02, 321.14, 159.9, 324.1, 162.86, 324.59, 164.84, 319.66, 166.81, 314.72, 161.88, 315.71, 153.98, 315.71, 145.58, 312.25, 136.2, 305.34, 137.69, 299.91, 132.75, 299.42, 127.32, 301.88, 117.44, 311.26, 115.47, 313.73, 121.39, 328.05, 119.42, 340.39, 124.85, 353.72, 134.72, 354.22, 141.14, 363.1, 163.85, 360.63, 165.33, 355.2, 151.02, 353.72, 165.33, 347.8, 163.85, 347.3, 159.41]], \"image_id\": 245915, \"area\": 1794.529700000001, \"bbox\": [299.42, 115.47, 63.68, 52.33]},\n",
"\n",
" {\"id\": 1411138, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[72.59, 177.35, 73.33, 169.12, 77.08, 160.89, 80.07, 156.4, 85.31, 149.66, 88.3, 145.17, 92.04, 139.19, 96.53, 133.95, 100.27, 127.96, 104.02, 125.72, 109.25, 124.97, 118.98, 124.22, 130.21, 122.72, 136.19, 122.72, 149.66, 121.98, 161.64, 123.47, 169.87, 127.21, 170.62, 130.96, 167.62, 133.95, 163.13, 135.44, 158.64, 134.7, 146.67, 140.68, 143.68, 148.91, 143.68, 155.65, 141.43, 160.14, 141.43, 164.63, 141.43, 165.38, 136.19, 165.38, 133.2, 163.13, 131.7, 169.87, 132.45, 176.6, 132.45, 178.1, 132.45, 178.85, 127.96, 178.85, 122.72, 165.38, 122.72, 163.88, 120.48, 168.37, 116.74, 173.61, 114.49, 178.1, 110.75, 177.35, 110.75, 171.36, 115.99, 161.64, 115.99, 153.4, 114.49, 151.16, 111.5, 149.66, 109.25, 151.91, 102.52, 152.66, 98.78, 152.66, 96.53, 151.16, 91.29, 157.15, 83.06, 161.64, 77.82, 167.62, 76.33, 170.62, 75.58, 173.61, 77.08, 176.6, 75.58, 177.35]], \"image_id\": 245915, \"area\": 2469.0984000000003, \"bbox\": [72.59, 121.98, 98.03, 56.87]},\n",
"\n",
" {\"id\": 1411160, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[389.47, 161.42, 391.37, 145.58, 400.25, 136.08, 416.09, 123.41, 438.26, 118.34, 464.88, 122.14, 472.48, 137.35, 480.08, 144.95, 486.42, 151.92, 483.25, 150.65, 475.01, 142.41, 470.58, 153.82, 469.94, 164.59, 461.71, 162.69, 458.54, 158.26, 458.54, 164.59, 453.47, 163.96, 447.77, 155.72, 442.06, 157.62, 440.8, 158.89, 438.9, 162.69, 430.03, 167.13, 423.69, 163.32, 426.86, 156.99, 426.22, 147.48, 421.15, 145.58, 417.35, 146.22, 408.48, 146.85, 401.51, 151.29, 398.98, 156.35, 398.98, 159.52, 398.34, 164.59, 397.08, 164.59]], \"image_id\": 245915, \"area\": 2642.9447999999984, \"bbox\": [389.47, 118.34, 96.95, 48.79]},\n",
"\n",
" {\"id\": 1411174, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[234.05, 180.69, 230.62, 180.38, 228.13, 183.18, 229.07, 185.37, 222.83, 190.04, 219.4, 183.18, 218.78, 186.61, 216.28, 185.37, 217.53, 179.13, 216.91, 172.9, 213.17, 165.41, 204.44, 164.17, 193.53, 163.54, 192.28, 156.06, 190.72, 156.06, 184.48, 154.19, 183.55, 155.75, 188.23, 157.31, 191.65, 157.93, 186.98, 160.42, 179.81, 157.93, 181.68, 149.83, 187.6, 149.2, 194.46, 149.2, 204.12, 143.59, 215.04, 142.97, 221.27, 144.21, 232.49, 146.71, 246.84, 146.08, 263.98, 151.7, 270.53, 150.45, 258.68, 153.88, 247.46, 156.06]], \"image_id\": 245915, \"area\": 1653.885, \"bbox\": [179.81, 142.97, 90.72, 47.07]},\n",
"\n",
" {\"id\": 2210312, \"category_id\": 22, \"iscrowd\": 0, \"segmentation\": [[40.18, 136.53, 43.72, 132.05, 46.31, 112.74, 47.01, 107.08, 53.14, 101.43, 66.09, 101.67, 70.33, 103.32, 79.52, 110.85, 84.23, 116.51, 84.23, 120.98, 80.7, 134.47, 82.82, 147.43, 80.23, 150.26, 75.75, 137.07, 76.93, 134.0, 74.34, 134.71, 76.69, 145.54, 75.51, 150.26, 71.04, 148.61, 69.16, 141.07, 65.15, 132.83, 63.74, 134.47, 65.86, 145.07, 59.5, 145.54, 57.61, 127.41, 56.91, 119.4, 50.78, 119.87, 48.19, 130.23, 43.72, 138.01, 40.89, 140.36, 39.48, 142.72, 38.77, 139.66]], \"image_id\": 245915, \"area\": 1242.647, \"bbox\": [38.77, 101.43, 45.46, 48.83]},\n",
"\n",
" {\"id\": 902200245915, \"category_id\": 22, \"iscrowd\": 1, \"segmentation\": {\"size\": [333, 500], \"counts\": [26454, 2, 651, 3, 13, 1, 313, 12, 6, 3, 312, 21, 310, 23, 310, 22, 310, 21, 12, 4, 296, 20, 12, 6, 294, 19, 13, 8, 293, 18, 14, 9, 292, 16, 15, 10, 292, 14, 17, 10, 292, 13, 17, 11, 293, 11, 17, 12, 293, 9, 18, 13, 294, 7, 19, 13, 295, 5, 19, 14, 297, 2, 19, 15, 318, 15, 318, 15, 318, 14, 319, 14, 319, 13, 320, 12, 321, 12, 321, 11, 322, 11, 322, 10, 323, 8, 326, 5, 10330, 16, 317, 17, 310, 24, 307, 27, 297, 36, 294, 39, 291, 42, 289, 44, 289, 44, 288, 44, 289, 44, 288, 44, 289, 35, 1, 6, 290, 24, 309, 22, 310, 21, 312, 22, 310, 24, 309, 24, 308, 25, 308, 25, 309, 39, 294, 40, 293, 41, 292, 42, 290, 43, 290, 43, 290, 43, 289, 44, 288, 45, 287, 45, 286, 47, 286, 46, 287, 44, 289, 40, 292, 46, 287, 47, 285, 49, 284, 50, 283, 50, 283, 29, 1, 20, 283, 25, 6, 19, 283, 22, 9, 19, 283, 22, 10, 18, 283, 22, 10, 17, 284, 22, 11, 16, 284, 22, 12, 14, 285, 21, 15, 10, 286, 22, 311, 22, 311, 22, 311, 22, 311, 22, 311, 22, 311, 22, 312, 20, 312, 21, 312, 21, 312, 21, 312, 20, 313, 19, 314, 19, 314, 18, 315, 18, 315, 17, 316, 17, 316, 17, 317, 16, 317, 16, 318, 15, 318, 15, 319, 14, 320, 13, 320, 13, 320, 13, 321, 11, 322, 12, 322, 11, 323, 10, 325, 8, 93429]}, \"image_id\": 245915, \"area\": 3188, \"bbox\": [79, 127, 140, 57]}\n",
"\n",
" ],\n",
"\n",
" \"categories\": [\n",
"\n",
" {\"supercategory\": \"vehicle\",\"id\": 2,\"name\": \"bicycle\"},\n",
"\n",
" {\"supercategory\": \"animal\",\"id\": 22,\"name\": \"elephant\"}\n",
"\n",
" ]\n",
"\n",
"}\n"
]
}
],
"source": [
"with open(annotation_path) as json_file:\n",
" lines = json_file.readlines()\n",
"for line in lines:\n",
" print(line)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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