imgs.py

import codecs
import json
import os

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from skimage import io, morphology, feature
from skimage import measure, color


def complex_images():
    bg_name = "data/B0FFF2CO42_1.png"
    fname = "data/B0FFF2CO42_GD0003800210101072.png"
    fname2 = "data/c_B0FFF2CO42_GD0003800210101072.png"

    bg = Image.open(bg_name)
    bg = bg.convert('L').convert('RGBA')
    mask = Image.new("RGBA", bg.size)
    final = Image.new("RGBA", bg.size)

    source = Image.open(fname)
    mask.paste(source, (546, 159))

    final = Image.alpha_composite(final, bg)
    final = Image.alpha_composite(final, mask)

    final.save(fname2)


def split_space_content():
    for root, dirs, files in os.walk('data'):
        for fname in files:
            bg_name = "data/"+fname
            img = io.imread(bg_name, as_grey=True)

            # 检测canny边缘,得到二值图片
            edgs = feature.canny(img, sigma=3)

            chull = morphology.convex_hull_object(edgs)

            #
            # fig, axes = plt.subplots(1, 2, figsize=(8, 8))
            # ax0, ax1 = axes.ravel()
            # ax0.imshow(edgs, plt.cm.gray)
            # ax0.set_title('many objects')
            # ax1.imshow(chull, plt.cm.gray)
            # ax1.set_title('convex_hull image')
            # # plt.show()

            plt.imsave("data2/"+fname, chull)

def to_contours():
    for root, dirs, files in os.walk('data2'):
        for fname in files:
            dst = io.imread('data2/'+fname, as_grey=True)
            contours = measure.find_contours(dst, 0.5)
            cords = np.concatenate(contours)

            new_img = measure.subdivide_polygon(cords, degree=2, preserve_ends=True)
            appr_img = measure.approximate_polygon(new_img, tolerance=1)

            print(fname, len(appr_img.tolist()))

def labels():
    bg_name = "data2/B0FFFF6TJG_1.png"
    data = io.imread(bg_name)
    data_rgb = color.rgba2rgb(data)
    data_gray = color.rgb2gray(data_rgb) # drop transparent layer

    # crop image
    mask = ~(data_gray == 1)
    mask_points = np.argwhere(mask)
    top,left,bottom,right = np.min(mask_points[:,0]), np.min(mask_points[:,1]), \
        np.max(mask_points[:,0]), np.max(mask_points[:,1])

    top = top-10>0 and top-10 or 0
    left = left-10>0 and left-10 or 0
    bottom = bottom+10<data.shape[0] and bottom+10 or data.shape[0]
    right = right+10<data.shape[1] and right+10 or data.shape[1]

    print("top,left,bottom,right", top,left,bottom,right);

    image = data_gray[top:bottom, left:right].copy()
    mask = mask[top:bottom, left:right]
    # print(image[0])

    # (np.isclose(image,0.0)) |
    """
    mask = ((np.isclose(image,0.928392)) | (np.isclose(image,0.844302)) | 
            (np.isclose(image,0.872443)) | (np.isclose(image,0.976991)) |
            (np.isclose(image,0.868027)) | (np.isclose(image,0.868027)) |
            (np.isclose(image,0.901961)) | (np.isclose(image,0.899898)) | 
            (np.isclose(image,0.884697)) |
            (np.isclose(image,0.901674)) | (np.isclose(image,0.8058))
            )
    """
    #mask = ~(image == 1)
    image[mask] = 0
    image[~mask] = 1

    #edgs = feature.canny(image, sigma=3)

    #labels = measure.label(edgs, connectivity=1)  # 8连通区域标记
    #dst = color.label2rgb(labels)  # 根据不同的标记显示不同的颜色
    #print('regions number:', labels.max() + 1)  # 显示连通区域块数(从0开始标记)

    #dst = morphology.convex_hull_object(edgs)

    contours = measure.find_contours(image, 0.5)
    #cords = np.concatenate(contours)

    cordarr = []
    for cords in contours:
        appr_img = measure.subdivide_polygon(cords, degree=2, preserve_ends=True)
        appr_img = measure.approximate_polygon(appr_img, tolerance=1)
        appr_img += np.array([top,left])
        cordarr.append(appr_img.tolist())
        print("appr_img:", len(appr_img.tolist()))

    print("cordarr:", len(cordarr))


    f, (ax0, ax1) = plt.subplots(2, figsize=(15, 10))
    ax0.imshow(data)
    ax0.set_title('Input image')
    ax1.imshow(image)
    ax1.set_title('After mask')
    plt.show()

def trim_data():
    suzu = codecs.open("extsource/poi_suzu_mall_names.txt", encoding="gbk")
    names = codecs.open("extsource/poi_mall_names.txt", encoding="gbk")

    am = set()
    for line in suzu:
        if line:
            s = json.loads(line)
            am.add(s["id"])

    for line in names:
        if line:
            s = json.loads(line)
            if s["id"] in am:
                am.remove(s["id"])

    print("\r\n".join(am))


if __name__ == '__main__':
    # complex_images()
    # split_space_content()
    # to_contours()
    labels()
    # trim_data()