Semantic segmentation learning notes (1)

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One 、 difference

1. Semantic segmentation ： Each pixel is labeled （ This pixel is human , Trees , Background, etc ） Semantic segmentation only distinguishes categories , Do not distinguish specific units in the classification
   

2. Instance segmentation ： Not only to distinguish categories , Also distinguish each individual in the category

3. Panoramic segmentation ： It is equivalent to semantic segmentation plus instance segmentation

Two 、 The code demonstrates the original image and mask The fusion

from PIL import Image

import imgviz

import numpy as np

image_file=r'D:\aaa\envs\labelme\Scripts\2_3_json\img.png'

mask_file=r'D:\aaa\envs\labelme\Scripts\2_3_json\label.png'

image=Image.open(image_file)

mask=Image.open(mask_file)

mask_img=Image.blend(image.convert("RGBA"),
                     mask.convert("RGBA"),0.5)

mask_img.save("vis2.png")

3、 ... and 、 Data processing

take labelme Marked json convert to mask Images

import json
import os

import imgviz
import numpy as np
from PIL import Image
import cv2
import glob

def save_colored_mask(mask,image_file):
    lbl_image=Image.fromarray(mask.astype(np.uint8),mode='P')
    colormap=imgviz.label_colormap()
    lbl_image.putpalette(colormap.flatten())
    lbl_image.save(image_file)


json_files=r'E:\ desktop \ Information \ Semantic segmentation '
img_file=r'E:\ desktop \ Information \ Semantic segmentation \ picture '

json_l=glob.glob(os.path.join(json_files,'*.json'))

for json_ in json_l:
    name=os.path.basename(json_)
    img_name=name.replace('json','png')
    fs=open(json_,encoding='utf-8')
    dict_=json.load(fs)
    #  Get images   wide , high 
    height = dict_['imageHeight']
    width = dict_['imageWidth']

    shapes = dict_["shapes"]
    #  Generate an all zero image 
    img = np.zeros((height, width), dtype=np.uint8)

    label_color = {
    "sheep": 1}
    for shape in shapes:
        #  Analyze the coordinates of polygon contour points 
        points = shape['points']
        #  Parse polygon labels 
        label = shape['label']

        points = np.array(points, dtype=np.int32)
        #  Draw the outline 
        cv2.polylines(img, [points], isClosed=True, color=(255), thickness=2)
        #  Fill polygon color 
        cv2.fillPoly(img, [points], color=label_color[label])
    img_path=os.path.join(img_file,img_name)
    print(img_path)

    save_colored_mask(img, img_path)