325 lines
9.8 KiB
Python
325 lines
9.8 KiB
Python
import os
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import cv2
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import glob
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import json
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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from PIL import Image
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from core.data import load_data_v2_plot,load_data_v2_plot_jj,load_data_v2_plot_cz,load_data_v2_plot_kw
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from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score, confusion_matrix
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def plot1(json_path, psize=24, img_size=2048):
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bg = np.zeros((img_size, img_size)) + 255
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bg[0, 0] = 0
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points, edge_index, labels, _ = load_data_v2_plot(json_path,max_edge_length=28)
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mask_pd = np.zeros((img_size, img_size))
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mask_pd[points[:, 0], points[:, 1]] = 1
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mask_pd = np.array(mask_pd, np.uint8)
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plt.figure(figsize=(9, 9))
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plt.imshow(bg, cmap='gray')
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h, w = np.where(mask_pd != 0)
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plt.scatter(points[:, 1], points[:, 0], s=16, c='#8EB9D9', zorder=2)
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#
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for idx, (s, e) in enumerate(edge_index.T):
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s = points[s]
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e = points[e]
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plt.plot([s[1], e[1]], [s[0], e[0]], linewidth=1, c='#C0C0C0', zorder=1)
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plt.axis('off')
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plt.tight_layout()
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def plot_combine(img_path, json_path, json_path2, json_path3, json_path4, psize=40):
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c = ['#dfc8a0', '#814d81', '#debd97']
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##814d81紫色
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points, edge_index, labels, _ = load_data_v2(json_path)
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select_point_index = np.where(labels == 1)[0]
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[points[:, 0], points[:, 1]] = labels + 1
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mask_pd = np.array(mask_pd, np.uint8)
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plt.figure(figsize=(9, 9))
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img = np.array(Image.open(img_path))
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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c = [ '#debd97','#84aad0','#debd97']
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points, edge_index, labels, _ = load_data_v2(json_path2)
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AA = np.where(labels == 0)[0]
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AA = AA[~np.isin(AA, select_point_index)]
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VV = np.where(labels == 1)[0]
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VV = VV[~np.isin(VV, select_point_index)]
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labelsv = labels[VV]
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pointsv = points[VV]
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[pointsv[:, 0], pointsv[:, 1]] = labelsv + 1
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mask_pd = np.array(mask_pd, np.uint8)
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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# VV = np.where(labels == 2)[0]
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labels = labels[AA]
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points = points[AA]
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[points[:, 0], points[:, 1]] = labels + 1
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mask_pd = np.array(mask_pd, np.uint8)
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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c = ['#84aad0', '#ff5b5b', '#ff5b5b']
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# points, edge_index, labels, _ = load_data_v2_plot_jj(json_path2, max_edge_length=28)
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points, edge_index, labels, _ = load_data_v2(json_path3)
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VV = np.where(labels == 1)[0]
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VV = VV[~np.isin(VV, select_point_index)]
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labels = labels[VV]
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points = points[VV]
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[points[:, 0], points[:, 1]] = labels + 1
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mask_pd = np.array(mask_pd, np.uint8)
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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c = ['#84aad0', '#92b964', '#ff5b5b']
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points, edge_index, labels, _ = load_data_v2(json_path4)
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VV = np.where(labels == 1)[0]
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VV = VV[~np.isin(VV, select_point_index)]
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labels = labels[VV]
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points = points[VV]
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[points[:, 0], points[:, 1]] = labels + 1
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mask_pd = np.array(mask_pd, np.uint8)
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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plt.imshow(img, cmap='gray')
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plt.axis('off')
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plt.tight_layout()
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plt.savefig('kw.jpg', dpi=300)
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plt.show()
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def plot2(img_path, json_path, json_path2, json_path3, json_path4, psize=40):
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# c = ['red', 'green', 'yellow']
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# c = [ '#dfc8a0', '#84aad0','#93ba66']
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# c = [ '#dfc8a0','#debd97', '#84aad0']
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# c = ['#9BB6CF', '#9BB6CF', '#9BB6CF']
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# c = ['#83a2c1', '#9BB6CF', '#9BB6CF']
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# points, edge_index, labels, _ = load_data_v2_plot(json_path,max_edge_length=25)
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# c = ['#dfc8a0', '#84aad0', '#93ba66']
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# c = ['#dfc8a0', '#814d81', '#debd97']
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c = ['#dfc8a0', '#814d81', '#debd97']
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##814d81紫色
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points, edge_index, labels, _ = load_data_v2_plot(json_path,max_edge_length=28)
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select_point_index = np.where(labels == 1)[0]
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# points, edge_index, labels, _ = load_data_v2_plot_jj(json_path,max_edge_length=31)
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# mask_pd = np.zeros((2048, 2048))
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[points[:, 0], points[:, 1]] = labels + 1
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mask_pd = np.array(mask_pd, np.uint8)
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plt.figure(figsize=(9, 9))
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img = np.array(Image.open(img_path))
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# bg = np.zeros_like(img) + 255
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# bg[0, 0] = 0
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# plt.imshow(bg, cmap='gray')
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# for idx, (s, e) in enumerate(edge_index.T):
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# s = points[s]
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# e = points[e]
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# plt.plot([s[1], e[1]], [s[0], e[0]], linewidth=1, c='#C0C0C0', zorder=1)
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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# plt.imshow(img, cmap='gray')
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# plt.axis('off')
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# plt.tight_layout()
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# plt.show()
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# #
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# for idx, (s, e) in enumerate(edge_index.T):
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# s = points[s]
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# e = points[e]
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# plt.plot([s[1], e[1]], [s[0], e[0]], linewidth=1, c='#C0C0C0', zorder=1)
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# c = [ '#84aad0','#92b964','#92b964']
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#debd97 黄 92b964 绿 84aad0 蓝
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# c = [ '#92b964','#debd97','#debd97']
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c = [ '#debd97','#84aad0','#debd97']
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points, edge_index, labels, _ = load_data_v2_plot_jj(json_path2, max_edge_length=28)
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AA = np.where(labels == 0)[0]
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AA = AA[~np.isin(AA, select_point_index)]
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VV = np.where(labels == 1)[0]
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VV = VV[~np.isin(VV, select_point_index)]
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labelsv = labels[VV]
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pointsv = points[VV]
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[pointsv[:, 0], pointsv[:, 1]] = labelsv + 1
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mask_pd = np.array(mask_pd, np.uint8)
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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# VV = np.where(labels == 2)[0]
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labels = labels[AA]
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points = points[AA]
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[points[:, 0], points[:, 1]] = labels + 1
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mask_pd = np.array(mask_pd, np.uint8)
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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c = ['#84aad0', '#ff5b5b', '#ff5b5b']
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# points, edge_index, labels, _ = load_data_v2_plot_jj(json_path2, max_edge_length=28)
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points, edge_index, labels, _ = load_data_v2_plot_cz(json_path3, max_edge_length=28)
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VV = np.where(labels == 1)[0]
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VV = VV[~np.isin(VV, select_point_index)]
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labels = labels[VV]
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points = points[VV]
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[points[:, 0], points[:, 1]] = labels + 1
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mask_pd = np.array(mask_pd, np.uint8)
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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# plt.imshow(img, cmap='gray')
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# plt.axis('off')
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# plt.tight_layout()
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#
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# plt.savefig('cz.jpg', dpi=300)
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# plt.show()
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# plt.imshow(img, cmap='gray')
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# plt.axis('off')
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# plt.tight_layout()
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#
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# plt.savefig('JJ.jpg', dpi=300)
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# plt.show()
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# plt.imshow(img, cmap='gray')
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# plt.axis('off')
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# plt.tight_layout()
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# plt.show()
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c = ['#84aad0', '#92b964', '#ff5b5b']
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points, edge_index, labels, _ = load_data_v2_plot_kw(json_path4, max_edge_length=28)
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VV = np.where(labels == 1)[0]
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VV = VV[~np.isin(VV, select_point_index)]
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labels = labels[VV]
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points = points[VV]
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mask_pd = np.zeros((512, 512)) + 256
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mask_pd[points[:, 0], points[:, 1]] = labels + 1
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mask_pd = np.array(mask_pd, np.uint8)
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for i in range(3):
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h, w = np.where(mask_pd == i + 1)
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plt.scatter(w, h, s=psize, c=c[i])
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plt.imshow(img, cmap='gray')
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plt.axis('off')
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plt.tight_layout()
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plt.savefig('kw.jpg', dpi=300)
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plt.show()
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#
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#
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# c = ['#84aad0', '#ff5b5b', '#ff5b5b']
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# # points, edge_index, labels, _ = load_data_v2_plot_jj(json_path2, max_edge_length=28)
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# points, edge_index, labels, _ = load_data_v2_plot_cz(json_path3, max_edge_length=28)
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#
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# # a = []
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# # b = []
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# #
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# # for i in points:
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# # if i not in xj_points:
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# # print(i)
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# VV = np.where(labels == 1)[0]
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# labels = labels[VV]
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# points = points[VV]
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#
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# mask_pd = np.zeros((512, 512)) + 256
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# mask_pd[points[:, 0], points[:, 1]] = labels + 1
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# mask_pd = np.array(mask_pd, np.uint8)
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# for i in range(3):
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# h, w = np.where(mask_pd == i + 1)
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# plt.scatter(w, h, s=psize, c=c[i])
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# plt.imshow(img, cmap='gray')
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# plt.axis('off')
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# plt.tight_layout()
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#
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#
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#
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# plt.imshow(img, cmap='gray')
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# plt.axis('off')
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# plt.tight_layout()
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#
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# plt.savefig('49.jpg', dpi=300)
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# plt.show()
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# json_path4 = '/home/gao/mouclear/detect_40/40/39/jj_v2/after_test_v2/39.json'
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# img_path4 = '/home/gao/mouclear/detect_40/40/39/jj_v2/after_test_v2/39.jpg'
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# json_path1 = '/home/gao/mouclear/detect_40/40/39/cz/v1/39.json'
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# img_path1 = '/home/gao/mouclear/detect_40/40/39/cz/v1/39.jpg'
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# json_path3 = '/home/gao/mouclear/detect_40/40/39/smo/raw/39.json'
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# img_path3 = '/home/gao/mouclear/detect_40/40/39/smo/raw/39.jpg'
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# json_path2 = '/home/gao/mouclear/detect_40/40/39/xj_28/v2/39.json'
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# img_path2 = '/home/gao/mouclear/detect_40/40/39/xj_28/v2/39.jpg'
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img_path1 = '/home/gao/mouclear/cc/final_todo/53/cz/raw/53.jpg'
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json_path1 = '/home/gao/mouclear/cc/final_todo/53/xj_norm/raw/53.json'
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json_path2 = '/home/gao/mouclear/cc/final_todo/53/jj_nonorm_WO3/raw/53.json'
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json_path3 = '/home/gao/mouclear/cc/final_todo/53/cz_mask/raw/mask.json'
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# json_path4 = '/home/gao/mouclear/cc/final_todo/53/kw/53.json'
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json_path4 = '/home/gao/mouclear/cc/final_todo/53/kw/mask.json'
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plot2(img_path1,json_path1, json_path2, json_path3, json_path4) |