2021-1-capstone-design1 / HCS_Project2

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정우진
ef6fbb8b 1 parent 67a1c968

covert annotation

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import cv2
import os
import sys
import re
import glob
path = sys.argv[1]
save_path = sys.argv[2]
def cropFolerlevel(path, save_path):
img_list = [file for file in glob.glob(path+'\\*.png')]
txt_list = [file for file in glob.glob(path+'\\*.txt')]
for i in range(img_list.__len__()):
txt = open(txt_list[i])
img = cv2.imread(img_list[i])
name = img_list[i].split('\\')[-1]
p = list(map(int, re.split(' |\n', txt.readlines()[7])[1:5]))
crop_img = img[p[1]:p[1]+p[3], p[0]:p[0]+p[2]]
cv2.imwrite(save_path+'\\'+name , crop_img)
def recursive(path, save_path):
files = os.listdir(path)
for file in files:
fullname = os.path.join(path, file)
savename = os.path.join(save_path, file)
if os.path.isdir(fullname):
recursive(fullname, savename)
if not os.path.isdir(save_path):
os.makedirs(save_path)
cropFolerlevel(path, save_path)
recursive(path, save_path)
\ No newline at end of file
import cv2
import os
import sys
import glob
import re
path = sys.argv[1]
save_path = sys.argv[2]
img_list = [cv2.imread(file) for file in glob.glob(path + '\\*.png')]
txt_list = [file for file in glob.glob(path + '\\*.txt')]
print(cv2.__version__)
print(txt_list)
print(img_list[1])
txt = open(txt_list[1])
img = img_list[1]
p = list(map(int, re.split(' |\n', txt.readlines()[7])[1:5]))
print(p)
cv2.imshow('hello', img)
cv2.waitKey(0)
corp_img = img[p[1]:p[1]+p[3], p[0]:p[0]+p[2]]
cv2.imshow('hello', corp_img)
cv2.imwrite(save_path+'\\result.png', corp_img)
import os
import csv
import xlsxwriter
from PIL import Image
import re
# make empty csv file
workbook = xlsxwriter.Workbook("dataset.xlsx")
worksheet = workbook.add_worksheet("sheet1")
# resize col
worksheet.set_column("A:A", 60)
# get current directory path
directory = os.getcwd()
img_file_name = []
text_file_name = []
for filename in os.listdir(directory):
if filename.endswith(".png"):
path = os.path.join(directory, filename)
img_file_name.append(path)
elif filename.endswith(".txt"):
path = os.path.join(directory, filename)
text_file_name.append(path)
print(img_file_name) # for check
print(text_file_name) # for check
# text annotation
text = []
for t in text_file_name:
plate = []
with open(t, "r") as f:
for line in f:
if line[:5] == "plate":
plate.append(line.strip("plate: ").strip())
elif line[:14] == "position_plate":
numbers = list(map(int, line.strip("position_plate: ").strip().split()))
plate.append(numbers)
elif "char" in line:
numbers = re.findall("\d+", line[8:])
plate.append(list(map(int, numbers)))
text.append(plate)
print(text) # for check
#################################################################################
#################### error : 마지막 image, text에 대해서만 됨 ####################
#################################################################################
# insert to csv
idx = 0
k = 0
for filename in img_file_name:
# col A, B
for i in range(k, k + len(text[idx][0]) - 1):
A = "A" + str(i + 1)
path = os.path.join(directory, filename)
worksheet.write(A, path)
B = "B" + str(i + 1)
worksheet.write(B, text[idx][0])
k += len(text[idx][0]) - 1
idx += 1
idx = 0
k = 0
for filename in text_file_name:
# col C, D, E, F : x1, y1, x2, y2
j = 0
for i in range(k, k + len(text[0]) - 2):
C = "C" + str(i + 1)
worksheet.write(C, text[idx][j + 2][0])
D = "D" + str(i + 1)
worksheet.write(D, text[idx][j + 2][1])
E = "E" + str(i + 1)
worksheet.write(E, text[idx][j + 2][0] + text[idx][j + 2][2])
F = "F" + str(i + 1)
worksheet.write(F, text[idx][j + 2][1] + text[idx][j + 2][3])
j += 1
k += len(text[0]) - 2
idx += 1
workbook.close()
import os
import csv
import xlsxwriter
from PIL import Image
import re
# make empty csv file
workbook = xlsxwriter.Workbook("dataset.xlsx")
worksheet = workbook.add_worksheet("sheet1")
# resize col
worksheet.set_column("A:A", 60)
# get current directory path
directory = os.getcwd()
img_file_name = []
text_file_name = []
for filename in os.listdir(directory):
if filename.endswith(".png"):
path = os.path.join(directory, filename)
img_file_name.append(path)
elif filename.endswith(".txt"):
path = os.path.join(directory, filename)
text_file_name.append(path)
print(img_file_name) # for check
print(text_file_name) # for check
# text annotation
text = []
for t in text_file_name:
plate = []
with open(t, "r") as f:
for line in f:
if line[:5] == "plate":
plate.append(line.strip("plate: ").strip())
elif line[:14] == "position_plate":
numbers = list(map(int, line.strip("position_plate: ").strip().split()))
plate.append(numbers)
elif "char" in line:
numbers = re.findall("\d+", line[8:])
plate.append(list(map(int, numbers)))
text.append(plate)
print(text) # for check
#################################################################################
#################### error : 마지막 image, text에 대해서만 됨 ####################
#################################################################################
# insert to csv
idx = 0
# for filename in os.listdir(directory):
for filename in img_file_name:
# if filename.endswith(".png"):
# col A, B
for i in range(len(text[idx][0]) - 1):
A = "A" + str(i + 1)
path = os.path.join(directory, filename)
worksheet.write(A, path)
B = "B" + str(i + 1)
worksheet.write(B, text[idx][0])
idx += 1
for filename in text_file_name:
# if filename.endswith(".txt"):
# col C, D, E, F : x1, y1, x2, y2
for i in range(len(text[0]) - 2):
C = "C" + str(i + 1)
worksheet.write(C, text[idx][i + 2][0])
D = "D" + str(i + 1)
worksheet.write(D, text[idx][i + 2][1])
E = "E" + str(i + 1)
worksheet.write(E, text[idx][i + 2][0] + text[idx][i + 2][2])
F = "F" + str(i + 1)
worksheet.write(F, text[idx][i + 2][1] + text[idx][i + 2][3])
idx += 1
workbook.close()
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skimage.morphology import skeletonize, skeletonize_3d\n","# import helper as hp\n","# import geopandas as gpd\n","# import geojson as gjs\n","# import shapely, random\n","import pandas as pd\n","# from shapely.geometry import *\n","from PIL import Image\n","import PIL\n","\n","# from helper import *"],"id":"acute-invasion","execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"promotional-motel","executionInfo":{"status":"ok","timestamp":1622118121046,"user_tz":-540,"elapsed":257,"user":{"displayName":"정우진","photoUrl":"","userId":"01776175892273033474"}}},"source":["def addCol(df):\n"," df.insert(0,['filename', 'xmin', 'ymin', 'xmax', 'ymax', 'label'])\n"," return df"],"id":"promotional-motel","execution_count":42,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"embedded-finger","executionInfo":{"status":"ok","timestamp":1622118518265,"user_tz":-540,"elapsed":532,"user":{"displayName":"정우진","photoUrl":"","userId":"01776175892273033474"}},"outputId":"99f5e915-1895-4666-ca76-87dec1396e72"},"source":["path_e = '/content/drive/Shareddrives/capstone/dataset/test1/'\n","all_lanes = path_e + 'annotations.csv'\n","df = pd.read_csv(all_lanes, names=['filename', 'xmin', 'ymin', 'xmax', 'ymax', 'label'])\n","#df.columns = ['filename', 'xmin', 'ymin', 'xmax', 'ymax', 'label']\n","#df = addCol(df)\n","df.head()"],"id":"embedded-finger","execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," 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<td>1_P3_img.jpg</td>\n"," <td>80</td>\n"," <td>70</td>\n"," <td>130</td>\n"," <td>160</td>\n"," <td>0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>"],"text/plain":[" filename xmin ymin xmax ymax label\n","0 1_P3_img.jpg 80 10 160 60 C\n","1 1_P3_img.jpg 180 10 210 60 6\n","2 1_P3_img.jpg 215 10 245 60 4\n","3 1_P3_img.jpg 15 70 75 140 sl\n","4 1_P3_img.jpg 80 70 130 160 0"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"disabled-arabic","executionInfo":{"status":"ok","timestamp":1622118566081,"user_tz":-540,"elapsed":891,"user":{"displayName":"정우진","photoUrl":"","userId":"01776175892273033474"}},"outputId":"7712dfe0-07e1-44fe-f846-ead0199e1cd7"},"source":["path_e = 'D:\\capstone\\dataset/'\n","train_e = path_e + 'train/train1/annotations.csv'\n","val_e = path_e + 'val/val1/annotations.csv'\n","\n","train_files = pd.read_csv(train_e, names=['filename', 'xmin', 'ymin', 'xmax', 'ymax', 'label'])\n","val_files = pd.read_csv(val_e, names=['filename', 'xmin', 'ymin', 'xmax', 'ymax', 'label'])\n","#train_files = addCol(train_files)\n","#val_v2 = addCol(val_files)\n","len(train_files), len(val_files)\n"],"id":"disabled-arabic","execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(75060, 7508)"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"streaming-sperm","executionInfo":{"status":"ok","timestamp":1622118572726,"user_tz":-540,"elapsed":746,"user":{"displayName":"정우진","photoUrl":"","userId":"01776175892273033474"}},"outputId":"e16a8f8d-e935-4210-82ac-cffdb531277e"},"source":["train_files.head()"],"id":"streaming-sperm","execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" filename xmin ymin xmax ymax label\n","0 1_P3_img.jpg 80 10 160 60 L\n","1 1_P3_img.jpg 180 10 210 60 2\n","2 1_P3_img.jpg 215 10 245 60 2\n","3 1_P3_img.jpg 15 70 75 140 fj\n","4 1_P3_img.jpg 80 70 130 160 8"],"text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>xmin</th>\n <th>ymin</th>\n <th>xmax</th>\n <th>ymax</th>\n <th>label</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1_P3_img.jpg</td>\n <td>80</td>\n <td>10</td>\n <td>160</td>\n <td>60</td>\n <td>L</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1_P3_img.jpg</td>\n <td>180</td>\n <td>10</td>\n <td>210</td>\n <td>60</td>\n <td>2</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1_P3_img.jpg</td>\n <td>215</td>\n <td>10</td>\n <td>245</td>\n <td>60</td>\n <td>2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1_P3_img.jpg</td>\n <td>15</td>\n <td>70</td>\n <td>75</td>\n <td>140</td>\n <td>fj</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1_P3_img.jpg</td>\n <td>80</td>\n <td>70</td>\n <td>130</td>\n <td>160</td>\n <td>8</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":3}]},{"cell_type":"raw","metadata":{"id":"checked-equivalent"},"source":[],"id":"checked-equivalent"},{"cell_type":"markdown","metadata":{"id":"IVTT-Yfmytt_"},"source":[],"id":"IVTT-Yfmytt_"},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EAC_gBIfkRXO","executionInfo":{"status":"ok","timestamp":1622118683685,"user_tz":-540,"elapsed":265,"user":{"displayName":"정우진","photoUrl":"","userId":"01776175892273033474"}},"outputId":"fc651662-578c-4d0e-ecb9-7e95962863aa"},"source":["all_lanes = path_e + 'e_label2.csv'\n","df2 = pd.read_csv(all_lanes, names=['name', 'index'])\n","#df2.colums = ['name', 'index']\n","#df2 = addCol2(df2)\n","df2.head()\n","dic_marks = df2.to_dict()['name']\n","print(dic_marks)\n","cat_ids = {label:str(key) for key, label in dic_marks.items()}\n","print(cat_ids)"],"id":"EAC_gBIfkRXO","execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["{0: 'ah', 1: 'aj', 2: 'ak', 3: 'al', 4: 'an', 5: 'ck', 6: 'dh', 7: 'dj', 8: 'dk', 9: 'dl', 10: 'dn', 11: 'eh', 12: 'ej', 13: 'ek', 14: 'el', 15: 'en', 16: 'fh', 17: 'fj', 18: 'fk', 19: 'fn', 20: 'gj', 21: 'gl', 22: 'gn', 23: 'qh', 24: 'qj', 25: 'qk', 26: 'qn', 27: 'qo', 28: 'rh', 29: 'rj', 30: 'rk', 31: 'rn', 32: 'sh', 33: 'sj', 34: 'sk', 35: 'sl', 36: 'sm', 37: 'sn', 38: 'th', 39: 'tj', 40: 'tk', 41: 'tl', 42: 'tn', 43: 'vk', 44: 'wh', 45: 'wj', 46: 'wk', 47: 'wl', 48: 'wn', 49: 'xn', 50: 'zj', 51: 'A', 52: 'B', 53: 'C', 54: 'D', 55: 'E', 56: 'F', 57: 'G', 58: 'H', 59: 'I', 60: 'J', 61: 'K', 62: 'L', 63: 'M', 64: 'N', 65: 'O', 66: 'P', 67: '0', 68: '1', 69: '2', 70: '3', 71: '4', 72: '5', 73: '6', 74: '7', 75: '8', 76: '9'}\n{'ah': '0', 'aj': '1', 'ak': '2', 'al': '3', 'an': '4', 'ck': '5', 'dh': '6', 'dj': '7', 'dk': '8', 'dl': '9', 'dn': '10', 'eh': '11', 'ej': '12', 'ek': '13', 'el': '14', 'en': '15', 'fh': '16', 'fj': '17', 'fk': '18', 'fn': '19', 'gj': '20', 'gl': '21', 'gn': '22', 'qh': '23', 'qj': '24', 'qk': '25', 'qn': '26', 'qo': '27', 'rh': '28', 'rj': '29', 'rk': '30', 'rn': '31', 'sh': '32', 'sj': '33', 'sk': '34', 'sl': '35', 'sm': '36', 'sn': '37', 'th': '38', 'tj': '39', 'tk': '40', 'tl': '41', 'tn': '42', 'vk': '43', 'wh': '44', 'wj': '45', 'wk': '46', 'wl': '47', 'wn': '48', 'xn': '49', 'zj': '50', 'A': '51', 'B': '52', 'C': '53', 'D': '54', 'E': '55', 'F': '56', 'G': '57', 'H': '58', 'I': '59', 'J': '60', 'K': '61', 'L': '62', 'M': '63', 'N': '64', 'O': '65', 'P': '66', '0': '67', '1': '68', '2': '69', '3': '70', '4': '71', '5': '72', '6': '73', '7': '74', '8': '75', '9': '76'}\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dense-jackson","executionInfo":{"status":"ok","timestamp":1622120241189,"user_tz":-540,"elapsed":1224,"user":{"displayName":"정우진","photoUrl":"","userId":"01776175892273033474"}},"outputId":"2f90cc2f-fb78-4f44-f2ba-354cce4aec7a","tags":["outputPrepend"]},"source":["all_names = {}\n","val_names, train_names = list(val_files.filename.unique()) , list(train_files.filename.unique())\n","names = val_names + train_names\n","len(names)\n","for i, name in enumerate(names):\n"," all_names[name] = i\n","print(names)"],"id":"dense-jackson","execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["_img.jpg', '8949_P2_img.jpg', '8950_P5_img.jpg', '8951_P4_img.jpg', '8952_P2_img.jpg', '8953_P2_img.jpg', '8954_P6_img.jpg', '8955_P5_img.jpg', '8956_P1_img.jpg', '8957_P4_img.jpg', '8958_P2_img.jpg', '8959_P2_img.jpg', '8960_P1_img.jpg', '8961_P5_img.jpg', '8962_P5_img.jpg', 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base = '/keras-retinanet-master/keras-retinanet-master/road_mark_detection_dataset/' \n","#base = 'keras-retinanet-master/keras-retinanet-master/road_mark_detection_dataset/BASEMAP_'\n","\n","def generate_coco_style(curr_file, name_file):\n"," json_file = []\n"," json_dict = {}\n"," instance = {\"image_id\":'', \"filename\":\"\", \"height\":\"\", \"width\":\"\", \"annotations\":[]}\n"," anno = {\"bbox\": [], \"bbox_mode\": 1, \"category_id\":\"\"}\n","\n"," for name in name_file:\n"," id_ = all_names[name]\n"," frames = curr_file[curr_file.filename == name]\n","\n"," #new_name = name.replace('jpg', 'png')\n"," new_name = name\n"," plate_type = name.split('_')[1]\n"," if plate_type == 'P1':\n"," width, height = 520, 110\n"," elif plate_type == 'P2':\n"," width, height = 335, 335\n"," elif plate_type == 'P3':\n"," width, height = 335, 170\n"," elif plate_type == 'P4':\n"," width, height = 520, 110\n"," elif plate_type == 'P5':\n"," width, height = 440, 220\n"," elif plate_type == 'P6':\n"," width, height = 335, 170\n"," else:\n"," print(\"no valid plate\")\n"," continue\n"," \n","# new_name \n"," # if not os.path.isfile(new_name):\n"," # print(new_name)\n"," # continue\n"," # im = cv2.imread(new_name)\n"," # height = im.shape[0]\n"," # width = im.shape[1]\n"," instance = {\"image_id\":id_, \"file_name\": (new_name),\"height\":height, \"width\":width, \"annotations\":[]} \n"," for i,frame in frames.iterrows():\n"," xmin, ymin, xmax, ymax, label = frame[1:]\n"," if xmin != ',':\n"," xmin, ymin, xmax, ymax = map(float, (xmin, ymin, xmax, ymax))\n"," w, h = (xmax-xmin), (ymax-ymin)\n"," bbox = [xmin, ymin, w, h]\n"," _label = cat_ids[label]\n"," else:\n"," continue\n"," anno = {\"bbox\": bbox, \"bbox_mode\": 1, \"category_id\": _label}\n"," instance[\"annotations\"].append(anno)\n"," json_file.append(instance)\n"," return json_file\n"],"id":"spectacular-union","execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"greek-recall","executionInfo":{"status":"ok","timestamp":1622120360319,"user_tz":-540,"elapsed":53020,"user":{"displayName":"정우진","photoUrl":"","userId":"01776175892273033474"}}},"source":["train_json_file = generate_coco_style(train_files, train_names)\n","val_json_file = generate_coco_style(val_files, val_names)"],"id":"greek-recall","execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"relevant-brain","executionInfo":{"status":"ok","timestamp":1622120366559,"user_tz":-540,"elapsed":820,"user":{"displayName":"정우진","photoUrl":"","userId":"01776175892273033474"}}},"source":["\n","train_custom = './dataset/train.json'\n","val_custom = './dataset/val.json'\n","\n","def save_custom(json_file, path_to_save):\n"," with open(path_to_save, 'w') as fp:\n"," json.dump(json_file, fp)\n"," \n"," "],"id":"relevant-brain","execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"distinguished-father","executionInfo":{"status":"ok","timestamp":1622120373042,"user_tz":-540,"elapsed":1944,"user":{"displayName":"정우진","photoUrl":"","userId":"01776175892273033474"}}},"source":["save_custom(train_json_file, train_custom)\n","save_custom(val_json_file, val_custom)"],"id":"distinguished-father","execution_count":11,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}]}
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