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akTwelve/COCO_Image_Viewer.ipynb

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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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"<IPython.core.display.HTML object>"
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},
"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",
" 1410330:red:{'id': 22, 'name': 'elephant', 'supercategory': 'animal'}\n",
" 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
}
@madhavajay
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Hi @akTwelve awesome notebook.
I noticed the local file branch of this doesn't work.

        # Open the image
        if use_url:
            image_path = image['coco_url']
            response = requests.get(image_path)
            image = PILImage.open(BytesIO(response.content))
            
        else:
            # this branch does not work because it needs to base64 encode the image
            image_path = os.path.join(self.image_dir, image['file_name'])
            image = PILImage.open(image_path)

Here is the fix:

import base64

...
        else:
            image_path = os.path.join(self.image_dir, image['file_name'])
            image = PILImage.open(image_path)

            buffer = BytesIO()
            image.save(buffer, format='PNG')
            buffer.seek(0)
            
            data_uri = base64.b64encode(buffer.read()).decode('ascii')
            image_path = "data:image/png;base64,{0}".format(data_uri)

@akTwelve
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Author

Thanks for sharing this fix! I’ll take a look when I get a chance.

@akTwelve
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akTwelve commented Mar 8, 2020

@madhavajay, I updated the gist with your suggested fix. Thanks again for the fix!

@zeeshanalipanhwar
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zeeshanalipanhwar commented Dec 21, 2020 •
edited
Loading

Hi @akTwelve, upon visualizing my own custom dataset I noticed that the masks of some instances did not get displayed while those of some did. This was due to segmentation_points being a numpy array, so sometimes the array when stringified looked like this '241, 5, 242, ..., 244, 5, 245]'. It can be avoided by adding:
segmentation_points = list (segmentation_points) after the following line of the function display_image in the notebook.
segmentation_points = np.multiply(segmentation_points, adjusted_ratio).astype(int).

@FreshlyBrewedCode
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Thanks for sharing!
I quickly added the option to visualize keypoints. I simply added the parameter in the display_image function:

def display_image(self, image_id, show_polys=True, show_bbox=True, show_crowds=True, show_keypoints=True, use_url=False):

and in the function body below the if show_bbox block I added:

if show_keypoints:
    for i, segm in enumerate(self.segmentations[image_id]):
        keypoints = segm.get('keypoints', None)
        if not keypoints is None:
            for k in range(0, segm.get('num_keypoints', 0)):
                x = keypoints[k * 3 + 0]
                y = keypoints[k * 3 + 1]
                visibility = keypoints[k * 3 + 2]
                html += '<circle cx="{}" cy="{}" r="3" fill="{}" />'.format(x, y, "red")

this will draw all keypoints using red svg circles, regardless of their visibility.

@alexeyev
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Thank you for sharing!

For photos with non-landscape orientation I had to add this to your code: https://stackoverflow.com/a/63798032/1616037

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