291 lines
12 KiB
Plaintext
291 lines
12 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 46,
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"id": "66ee10ca-9a42-4e52-a56c-3c4e8b470afa",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-20T05:57:02.162368Z",
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"start_time": "2024-06-20T05:57:02.157402Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import cv2\n",
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"import glob\n",
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"import copy\n",
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"import json\n",
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"import shutil\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from PIL import Image\n",
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"from labelme import utils\n",
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"from skimage.feature import peak_local_max"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"id": "40711569-6f55-49d2-8a88-152c7b677218",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-20T05:57:02.167292Z",
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"start_time": "2024-06-20T05:57:02.165442Z"
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}
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},
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"outputs": [],
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"source": [
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"class_dict = {\n",
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" 1: 'Norm', \n",
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" 2: 'SV',\n",
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" 3: 'LineSV',\n",
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"}\n",
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"\n",
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"class_dict_rev = {\n",
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" '0': 0, \n",
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" '1': 1,\n",
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" '2': 2,\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"id": "1762751c-7d19-482c-96ff-477b9ea50e5b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-20T05:57:02.173565Z",
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"start_time": "2024-06-20T05:57:02.170089Z"
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}
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},
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"outputs": [],
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"source": [
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"def crop_slide(img_path, save_path, patch_size=256, step=128):\n",
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" #2048*2048,裁减出来的size是256*256,滑动窗口128\n",
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" \n",
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" base_name = img_path.split('/')[-1].split('.')[0]\n",
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" json_path = img_path.replace('.jpg', '.json')\n",
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" img = cv2.imread(img_path, 0)\n",
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" # img = cv2.equalizeHist(img)\n",
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" # img = cv2.GaussianBlur(img, (5, 5), 0)\n",
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" h, w = img.shape\n",
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" \n",
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" with open(json_path) as f:\n",
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" json_data = json.load(f)\n",
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"\n",
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" points = np.array([item['points'][0][::-1] for item in json_data['shapes']], np.int32)\n",
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" labels = np.array([class_dict_rev[item['label']] for item in json_data['shapes']], np.int32)\n",
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" \n",
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" mask = np.zeros_like(img)\n",
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" mask[points[:, 0], points[:, 1]] = labels\n",
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"\n",
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" for i in range(0, h-patch_size+1, step):\n",
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" for j in range(0, w-patch_size+1, step):\n",
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" v_nums = np.sum(mask[i:i+patch_size, j:j+patch_size] > 1)\n",
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" \n",
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" Image.fromarray(img[i:i+patch_size, j:j+patch_size]).save(\n",
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" os.path.join(save_path, 'img', '{}_{}_{}_{}.png'.format(base_name, str(i), str(j), str(v_nums)))\n",
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" )\n",
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" \n",
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" Image.fromarray(mask[i:i+patch_size, j:j+patch_size]).save(\n",
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" os.path.join(save_path, 'lbl', '{}_{}_{}_{}.png'.format(base_name, str(i), str(j), str(v_nums)))\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"id": "1417b046-4e1c-4b9c-9aff-cf5295701601",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-20T05:57:37.902137Z",
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"start_time": "2024-06-20T05:57:37.881766Z"
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}
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},
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"outputs": [],
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"source": [
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"def process_slide(img_path, save_path):\n",
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" base_name = img_path.split('/')[-1].split('.')[0]\n",
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" json_path = img_path.replace('.jpg', '.json')\n",
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" img = cv2.imread(img_path, 0)\n",
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" # img = cv2.equalizeHist(img)\n",
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" # img = cv2.GaussianBlur(img, (5, 5), 0)\n",
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" \n",
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" h, w = img.shape\n",
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" \n",
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" with open(json_path) as f:\n",
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" json_data = json.load(f)\n",
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"\n",
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" points = np.array([item['points'][0][::-1] for item in json_data['shapes']], np.int32)\n",
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" labels = np.array([class_dict_rev[item['label']] for item in json_data['shapes']], np.int32)\n",
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" \n",
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" mask = np.zeros_like(img)\n",
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" for idx, point in enumerate(points):\n",
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" cv2.circle(mask, point[::-1], 8, int(labels[idx]), -1)\n",
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" \n",
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" mask = np.zeros_like(img)\n",
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" mask[points[:, 0], points[:, 1]] = labels\n",
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" \n",
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" Image.fromarray(img).save(\n",
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" os.path.join(save_path, 'img', '{}.png'.format(base_name))\n",
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" )\n",
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" \n",
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" Image.fromarray(mask).save(\n",
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" os.path.join(save_path, 'lbl', '{}.png'.format(base_name))\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"id": "59c532e0-39ec-406c-8d77-e0a17a9181cd",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-20T05:57:02.185924Z",
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"start_time": "2024-06-20T05:57:02.183624Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "10"
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},
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"execution_count": 50,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"img_lst = glob.glob('../../data/linesv/slide/*.jpg') \n",
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"img_lst.sort(); len(img_lst)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"id": "41c0bdb9-f5ca-4744-bc8a-d5d9723716c9",
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"metadata": {
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"tags": [],
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"ExecuteTime": {
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"end_time": "2024-06-20T05:57:02.189160Z",
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"start_time": "2024-06-20T05:57:02.186688Z"
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}
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},
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"outputs": [],
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"source": [
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"# for _type in ['test']: \n",
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"# os.makedirs('/home/gao/下载/process/test/{}/img'.format(_type), exist_ok=True)\n",
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"# os.makedirs('/home/gao/下载/process/test/{}/lbl'.format(_type), exist_ok=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"id": "378409b3-66b2-4325-8822-104c9c2519a1",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-20T05:57:02.192446Z",
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"start_time": "2024-06-20T05:57:02.189668Z"
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}
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},
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"outputs": [],
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"source": [
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"# crop_slide('../../data/linesv/slide/0.jpg', save_path='../../data/linesv/patch_unet/train/', step=64)\n",
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"# crop_slide('../../data/linesv/slide/3.jpg', save_path='../../data/linesv/patch_unet/valid/', step=256)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"id": "14d437d6-a43c-4471-a5ff-5df43e273819",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-20T05:57:02.195706Z",
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"start_time": "2024-06-20T05:57:02.193215Z"
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}
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},
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"outputs": [],
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"source": [
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"# for name in [2, 4, 6, 8, 10, 20, 30, 40]:\n",
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"# process_slide('../../data/linesv/slide/{}.jpg'.format(name), save_path='../../data/linesv/patch_unet/test/')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"id": "ecdb4f79-cfa5-4511-bf67-21bbe26ba403",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-20T05:57:47.740520Z",
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"start_time": "2024-06-20T05:57:40.815902Z"
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}
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},
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"outputs": [
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{
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"ename": "KeyError",
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"evalue": "'Norm'",
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"output_type": "error",
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"traceback": [
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)",
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"Cell \u001B[0;32mIn[57], line 3\u001B[0m\n\u001B[1;32m 1\u001B[0m img_lst \u001B[38;5;241m=\u001B[39m glob\u001B[38;5;241m.\u001B[39mglob(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m/home/gao/mouclear/cc/data/end-to-end-result-xj/end-to-end-gnn/raw/*.jpg\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m 2\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m item \u001B[38;5;129;01min\u001B[39;00m img_lst:\n\u001B[0;32m----> 3\u001B[0m \u001B[43mprocess_slide\u001B[49m\u001B[43m(\u001B[49m\u001B[43mitem\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msave_path\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m/home/gao/mouclear/cc/data/end-to-end-result-xj/end-to-end-gnn/raw\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
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"Cell \u001B[0;32mIn[56], line 14\u001B[0m, in \u001B[0;36mprocess_slide\u001B[0;34m(img_path, save_path)\u001B[0m\n\u001B[1;32m 11\u001B[0m json_data \u001B[38;5;241m=\u001B[39m json\u001B[38;5;241m.\u001B[39mload(f)\n\u001B[1;32m 13\u001B[0m points \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([item[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpoints\u001B[39m\u001B[38;5;124m'\u001B[39m][\u001B[38;5;241m0\u001B[39m][::\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m item \u001B[38;5;129;01min\u001B[39;00m json_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshapes\u001B[39m\u001B[38;5;124m'\u001B[39m]], np\u001B[38;5;241m.\u001B[39mint32)\n\u001B[0;32m---> 14\u001B[0m labels \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([class_dict_rev[item[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel\u001B[39m\u001B[38;5;124m'\u001B[39m]] \u001B[38;5;28;01mfor\u001B[39;00m item \u001B[38;5;129;01min\u001B[39;00m json_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshapes\u001B[39m\u001B[38;5;124m'\u001B[39m]], np\u001B[38;5;241m.\u001B[39mint32)\n\u001B[1;32m 16\u001B[0m mask \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mzeros_like(img)\n\u001B[1;32m 17\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m idx, point \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(points):\n",
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"Cell \u001B[0;32mIn[56], line 14\u001B[0m, in \u001B[0;36m<listcomp>\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m 11\u001B[0m json_data \u001B[38;5;241m=\u001B[39m json\u001B[38;5;241m.\u001B[39mload(f)\n\u001B[1;32m 13\u001B[0m points \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([item[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpoints\u001B[39m\u001B[38;5;124m'\u001B[39m][\u001B[38;5;241m0\u001B[39m][::\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m item \u001B[38;5;129;01min\u001B[39;00m json_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshapes\u001B[39m\u001B[38;5;124m'\u001B[39m]], np\u001B[38;5;241m.\u001B[39mint32)\n\u001B[0;32m---> 14\u001B[0m labels \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([\u001B[43mclass_dict_rev\u001B[49m\u001B[43m[\u001B[49m\u001B[43mitem\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mlabel\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m]\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m item \u001B[38;5;129;01min\u001B[39;00m json_data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshapes\u001B[39m\u001B[38;5;124m'\u001B[39m]], np\u001B[38;5;241m.\u001B[39mint32)\n\u001B[1;32m 16\u001B[0m mask \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mzeros_like(img)\n\u001B[1;32m 17\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m idx, point \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(points):\n",
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"\u001B[0;31mKeyError\u001B[0m: 'Norm'"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"img_lst = glob.glob('/home/gao/mouclear/cc/data/end-to-end-result-xj/end-to-end-gnn/raw/*.jpg')\n",
|
||
|
|
"for item in img_lst:\n",
|
||
|
|
" process_slide(item, save_path='/home/gao/mouclear/cc/data/end-to-end-result-xj/end-to-end-gnn/raw')\n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"outputs": [],
|
||
|
|
"source": [],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"id": "6171b6259ef615c7",
|
||
|
|
"execution_count": null
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "ee0f80af-0c81-43fc-b879-002ad63413e8",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"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": "3.8.18"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|