安阳做网站推广,建站系统wordpress下载,企业管理系统有,wordpress call to un输入#xff1a;单通道的灰度图#xff0c;灰度图内含掩膜mask 目标#xff1a;把灰度图中的语义mask转换为COCO数据集格式的json文件 输出#xff1a;COCO数据集格式的json文件
期间遇到的问题#xff1a; 发现有的掩膜内部存在其他类别的掩膜#xff0c;即mask内部还套…输入单通道的灰度图灰度图内含掩膜mask 目标把灰度图中的语义mask转换为COCO数据集格式的json文件 输出COCO数据集格式的json文件
期间遇到的问题 发现有的掩膜内部存在其他类别的掩膜即mask内部还套了mask这种情况的mask怎么只用一个数组来表示
以下是查找的可用代码
from PIL import Image
import numpy as np
from skimage import measure
from shapely.geometry import Polygon, MultiPolygon
import json
import os
from tqdm import tqdmdef create_sub_masks(mask_image):width, height mask_image.size# Initialize a dictionary of sub-masks indexed by RGB colorssub_masks {}for x in range(width):for y in range(height):# Get the RGB values of the pixelpixel mask_image.getpixel((x, y))[:3]# If the pixel is not black...if pixel ! (0, 0, 0):# Check to see if weve created a sub-mask...pixel_str str(pixel)sub_mask sub_masks.get(pixel_str)if sub_mask is None:# Create a sub-mask (one bit per pixel) and add to the dictionary# Note: we add 1 pixel of padding in each direction# because the contours module doesnt handle cases# where pixels bleed to the edge of the imagesub_masks[pixel_str] Image.new(1, (width2, height2))# Set the pixel value to 1 (default is 0), accounting for paddingsub_masks[pixel_str].putpixel((x1, y1), 1)return sub_masksdef create_sub_mask_annotation(sub_mask, image_id, category_id, annotation_id, is_crowd):# Find contours (boundary lines) around each sub-mask# Note: there could be multiple contours if the object# is partially occluded. (E.g. an elephant behind a tree)contours measure.find_contours(np.array(sub_mask), 0.5, positive_orientationlow)segmentations []polygons []for contour in contours:# Flip from (row, col) representation to (x, y)# and subtract the padding pixel# import ipdb;ipdb.set_trace()for i in range(len(contour)):row, col contour[i]contour[i] (col - 1, row - 1)# Make a polygon and simplify itpoly Polygon(contour)poly poly.simplify(1.0, preserve_topologyFalse)polygons.append(poly)segmentation np.array(poly.exterior.coords)segmentation np.maximum(segmentation, 0).ravel().tolist()# import ipdb;ipdb.set_trace()# print(segmentation)#if segmentation []:# continuesegmentations.append(segmentation)# Combine the polygons to calculate the bounding box and areamulti_poly MultiPolygon(polygons)if multi_poly.bounds ():return skipx, y, max_x, max_y multi_poly.bounds# x max(0, x)# y max(0, y)width max_x - xheight max_y - ybbox (x, y, width, height)area multi_poly.areaannotation {segmentation: segmentations,iscrowd: is_crowd,image_id: image_id,category_id: category_id,id: annotation_id,bbox: bbox,area: area}return annotationdef get_name(root, mode_folderTrue):for root, dirs, file in os.walk(root):if mode_folder:return sorted(dirs)else:return sorted(file)def get_annotation(mask_image_root):dataset {info: {year: 2023, version: 2023, description: , url: ,},license: {},images: [],annotations: [],categories: []}class_index {0: background,1:cate1,2:cate2}for s, k in enumerate(list(class_index.keys())):dataset[categories].append({id: k, name: class_index[k], supercategory: xxx})is_crowd 0# These ids will be automatically increased as we goannotation_id 0image_id 0# Create the annotationsrrr maskdirfor i, root in tqdm(enumerate(mask_image_root)):print(i)mask_image Image.open(rrr root).convert(RGB)print(root)weight, height mask_image.size# file_name rgb_ root.split(/)[-1].split(_)[-1]file_name mask_image_root[i]print(file_name)dataset[images].append({file_name: file_name,id: i,width: weight,height: height})# import ipdb;ipdb.set_trace()sub_masks create_sub_masks(mask_image)# import ipdb;ipdb.set_trace()for color, sub_mask in sub_masks.items():category_id 1annotation create_sub_mask_annotation(sub_mask, image_id, category_id, annotation_id, is_crowd)if annotation skip:continuedataset[annotations].append(annotation)annotation_id 1image_id 1with open(trainmask.json, w) as f:json.dump(dataset, f)# rrr ./InstanceSegmentation/
# all_root get_name(rrr, mode_folderFalse)
# get_annotation(all_root)
if __name____main__:maskdir ./mask/trainmask/maskimglist os.listdir(maskdir)get_annotation(maskimglist)问题 上述代码仍然存在不足有的mask太小segmentation输出是 []这需要检查一下我在里面输出的位置判断是空就不保存可以避免这种问题但是bbox等信息有的会出现Nah的情况需要自己判断处理。
整体上来说这个代码还是挺好用的。
还有一点有些内部mask比较极端的情况代码执行容易出错建议把问题图像删除或者自己查找问题修改代码。
更新实际测试有的格式有问题以下是修改后版本代码
from PIL import Image
import numpy as np
from skimage import measure
from shapely.geometry import Polygon, MultiPolygon
import json
import os
from tqdm import tqdmdef create_sub_masks(mask_image):width, height mask_image.size# Initialize a dictionary of sub-masks indexed by RGB colorssub_masks {}for x in range(width):for y in range(height):# Get the RGB values of the pixelpixel mask_image.getpixel((x, y))[:3]# If the pixel is not black...if pixel ! (0, 0, 0):# Check to see if weve created a sub-mask...pixel_str str(pixel)sub_mask sub_masks.get(pixel_str)if sub_mask is None:# Create a sub-mask (one bit per pixel) and add to the dictionary# Note: we add 1 pixel of padding in each direction# because the contours module doesnt handle cases# where pixels bleed to the edge of the imagesub_masks[pixel_str] Image.new(1, (width2, height2))# Set the pixel value to 1 (default is 0), accounting for paddingsub_masks[pixel_str].putpixel((x1, y1), 1)# import ipdb;ipdb.set_trace()return sub_masksdef create_sub_mask_annotation(sub_mask, image_id, category_id, annotation_id, is_crowd):# Find contours (boundary lines) around each sub-mask# Note: there could be multiple contours if the object# is partially occluded. (E.g. an elephant behind a tree)contours measure.find_contours(np.array(sub_mask), 0.5, positive_orientationlow)segmentations []polygons []annotations []for contour in contours:# Flip from (row, col) representation to (x, y)# and subtract the padding pixelfor i in range(len(contour)):row, col contour[i]contour[i] (col - 1, row - 1)# Make a polygon and simplify itpoly Polygon(contour)poly poly.simplify(1.0, preserve_topologyFalse)segmentation np.array(poly.exterior.coords)segmentation np.maximum(segmentation, 0).ravel().tolist()# import ipdb;ipdb.set_trace()# print(segmentation)if segmentation []:continue# segmentations.append(segmentation)# polygons.append(poly)x, y, max_x, max_y poly.boundswidth max_x - xheight max_y - ybbox (x, y, width, height)area poly.areaannotation {segmentation: [segmentation],iscrowd: is_crowd,image_id: image_id,category_id: category_id,id: annotation_id,bbox: bbox,area: area}annotations.append(annotation)annotation {}annotation_id annotation_id 1return annotations, annotation_iddef get_name(root, mode_folderTrue):for root, dirs, file in os.walk(root):if mode_folder:return sorted(dirs)else:return sorted(file)def get_annotation(mask_image_root):dataset {info: {year: 2023, version: 2023, description: , url: ,},license: {},images: [],annotations: [],categories: []}class_index {0: background,1:junban,2:yachi}for s, k in enumerate(list(class_index.keys())):dataset[categories].append({id: k, name: class_index[k], supercategory: yachi})is_crowd 0# These ids will be automatically increased as we goannotation_id 0image_id 0# Create the annotationsrrr maskdir# maskcolor {}# colorid 1for i, root in tqdm(enumerate(mask_image_root)):print(i)mask_image Image.open(rrr root).convert(RGB)print(root)weight, height mask_image.size# file_name rgb_ root.split(/)[-1].split(_)[-1]file_name mask_image_root[i]print(file_name)dataset[images].append({file_name: file_name,id: i,width: weight,height: height})# import ipdb;ipdb.set_trace()sub_masks create_sub_masks(mask_image)for color, sub_mask in sub_masks.items():if color (1, 1, 1):category_id 1elif color (2, 2, 2):category_id 2annotation, annotation_idout create_sub_mask_annotation(sub_mask, image_id, category_id, annotation_id, is_crowd)if annotation skip:continuefor anno in annotation:dataset[annotations].append(anno)# annotation_id 1annotation_id annotation_idoutimage_id 1with open(post_val.json, w) as f:json.dump(dataset, f)# rrr ./InstanceSegmentation/
# all_root get_name(rrr, mode_folderFalse)
# get_annotation(all_root)
if __name____main__:maskdir ./mask/valmask/# maskdir ./mask/posttest/mask/maskimglist os.listdir(maskdir)get_annotation(maskimglist)mask像素值确定类别部分需要自己手动调整修改
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