172 lines
4.6 KiB
Plaintext
172 lines
4.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f7589eab-423f-48be-88cc-96348b018bc7",
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"metadata": {},
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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 json\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"from PIL import Image\n",
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"from skimage.feature import peak_local_max\n",
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"from skimage.measure import label\n",
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"from skimage.measure import regionprops"
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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": 2,
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"id": "931d4ec3-4ca7-4b51-a3bd-9affccd46ecf",
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"metadata": {},
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"outputs": [],
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"source": [
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"def hough_center_detection(i, rp, labeled_img, img_size=2048):\n",
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" hs, ws, he, we = rp.bbox\n",
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" hs = np.clip(hs - 5, 0, img_size-1)\n",
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" ws = np.clip(ws - 5, 0, img_size-1)\n",
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" he = np.clip(he + 5, 0, img_size-1)\n",
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" we = np.clip(we + 5, 0, img_size-1)\n",
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"\n",
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" m = np.array(labeled_img == rp.label, np.uint8)[hs:he, ws:we]\n",
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" \n",
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" cricles = cv2.HoughCircles(\n",
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" m,\n",
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" method = cv2.HOUGH_GRADIENT,\n",
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" dp = 1,\n",
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" minDist = 14,\n",
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" minRadius = 5,\n",
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" maxRadius = 12,\n",
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" param1 = 5,\n",
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" param2 = 6,\n",
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" )\n",
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" \n",
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" if cricles is None:\n",
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" return np.array([])\n",
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" \n",
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" if (rp.area > 400) & (cricles.shape[1] != 2):\n",
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" print(i)\n",
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" \n",
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" centers = np.round(cricles[0][:, :2][:, ::-1] + [hs, ws])\n",
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" centers = np.array(centers, np.int32)\n",
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" \n",
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" return centers"
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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": 3,
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"id": "0dc26f52-7985-48c9-a7b7-6d9243dcc5a7",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_mask(probs, min_size=8):\n",
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" binary = np.array(probs * 255., np.uint8)\n",
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" _, binary = cv2.threshold(binary, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
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" \n",
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" centers = []\n",
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" mask = np.zeros(binary.shape)\n",
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" \n",
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" labeled_img = label(binary)\n",
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" rps = regionprops(labeled_img, intensity_image=probs)\n",
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" \n",
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" for rp in rps:\n",
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" if rp.area < min_size:\n",
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" continue\n",
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"\n",
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" h, w = np.array(np.round(rp.centroid), np.int32)\n",
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" mask[h, w] = 1\n",
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" \n",
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" return mask"
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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": 4,
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"id": "3d439e0d-b3a7-45ab-82e7-b34b1b5b182c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_mask_v2(probs, min_size=8):\n",
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" binary = np.array(probs * 255., np.uint8)\n",
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" _, binary = cv2.threshold(binary, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
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" \n",
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" centers = []\n",
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" mask = np.zeros(binary.shape)\n",
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" \n",
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" labeled_img = label(binary)\n",
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" rps = regionprops(labeled_img, intensity_image=probs)\n",
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"\n",
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" for i, rp in enumerate(rps):\n",
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" if rp.area < 32:\n",
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" continue\n",
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"\n",
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" rp_centers = hough_center_detection(i, rp, labeled_img)\n",
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"\n",
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" if len(rp_centers) == 0:\n",
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" h, w = np.array(np.round(rp.centroid), np.int32)\n",
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" mask[h, w] = 1\n",
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" else:\n",
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" for h, w in rp_centers:\n",
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" mask[h, w] = 1\n",
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"\n",
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" return mask"
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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": 5,
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"id": "86ba3768-f686-4995-a8d6-77eb675c6702",
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"metadata": {},
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"outputs": [],
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"source": [
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"colors = ['red', 'yellow', 'blue']\n",
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"\n",
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"with open('./logs/0/version_0/test.json') as f:\n",
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" data = json.load(f)\n",
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" \n",
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"img_path = np.array(data['img_path'])\n",
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"pred = np.array(data['pred'])\n",
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"lb = np.array(data['label'])"
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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": null,
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"id": "7fbc6c01-7380-4f80-9326-15aa48aa1c2c",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "cmae",
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"language": "python",
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"name": "cmae"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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