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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"import os\n",
"import cv2\n",
"import xlsxwriter\n",
"import numpy as np\n",
"\n",
"\n",
"def shear_img(image):\n",
" rows, cols, _ = image.shape\n",
" M = np.float32([[1, -0.22, 0], [0, 1, 0], [0, 0, 1]])\n",
" sheared_img = cv2.warpPerspective(image, M, (int(cols * 1.5), int(rows * 1.5)))\n",
" return cv2.flip(sheared_img, 1)\n",
"\n",
"\n",
"def crop_image(sheared_image, r, target_path):\n",
" cropped = sheared_image[int(r[1]) : int(r[1] + r[3]), int(r[0]) : int(r[0] + r[2])]\n",
" return cropped"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"\n",
"def writeDate(worksheet, row, column, date, format):\n",
" original_format = \"%Y-%m-%d %H-%M-%S-%f\"\n",
" parsed_datetime = datetime.strptime(date, original_format)\n",
"\n",
" worksheet.write_datetime(row, column, parsed_datetime, format)\n",
"\n",
"\n",
"def data2excel(data):\n",
" # save the data in an excel file\n",
" fileName = \"data.xlsx\"\n",
" workbook = xlsxwriter.Workbook(fileName)\n",
" worksheet = workbook.add_worksheet()\n",
"\n",
" dateFormat = workbook.add_format({\"num_format\": \"dd/mm/yy hh:mm:ss\"})\n",
"\n",
" # write the data\n",
" row = 0\n",
" for key, value in data.items():\n",
" date = key.split(\" \", 1)[1][:-4]\n",
" writeDate(worksheet, row, 0, date, dateFormat)\n",
" worksheet.write(row, 1, value)\n",
" try:\n",
" worksheet.write(row, 2, float(value[:6]))\n",
" except Exception as _:\n",
" pass\n",
" row += 1\n",
"\n",
" workbook.close()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"def peaks(data, boxes=2):\n",
" # split data in boxes\n",
" data = np.array_split(data, boxes)\n",
"\n",
" # get the maximum value in each box\n",
" data = [np.max(d) for d in data]\n",
" data = [d > 150 for d in data]\n",
" return np.array(data).astype(int).tolist()\n",
"\n",
"\n",
"digits = {\n",
" 0: [[1, 0, 1], [1, 1], [1, 1]],\n",
" 1: [[0, 0, 0], [0, 1], [0, 1]],\n",
" 2: [[1, 1, 1], [0, 1], [1, 0]],\n",
" 3: [[1, 1, 1], [0, 1], [0, 1]],\n",
" 4: [[0, 1, 0], [1, 1], [0, 1]],\n",
" 5: [[1, 1, 1], [1, 0], [0, 1]],\n",
" 6: [[1, 1, 1], [1, 0], [1, 1]],\n",
" 7: [[1, 0, 0], [0, 1], [0, 1]],\n",
" 8: [[1, 1, 1], [1, 1], [1, 1]],\n",
" 9: [[1, 1, 1], [1, 1], [0, 1]],\n",
"}\n",
"\n",
"\n",
"def ownOCR(image):\n",
" # get vertical pixel line in the middle of the image\n",
" vertical = image[:, image.shape[1] // 2, 0]\n",
"\n",
" # get two horizontal lines at 1/3 and 2/3 of the image\n",
" horizontal1 = image[image.shape[0] // 3, :, 0]\n",
" horizontal2 = image[2 * image.shape[0] // 3, :, 0]\n",
"\n",
" # get times it goes above 150, remove subsequent values\n",
" digit = [peaks(vertical, 3), peaks(horizontal1), peaks(horizontal2)]\n",
" digit = [key for key, value in digits.items() if value == digit]\n",
" return digit[0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"metadata": {}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n",
"index 0 is out of bounds for axis 1 with size 0\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[5], line 24\u001b[0m\n\u001b[0;32m 22\u001b[0m image_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder, image_name)\n\u001b[0;32m 23\u001b[0m image \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(image_path)\n\u001b[1;32m---> 24\u001b[0m sheared_image \u001b[38;5;241m=\u001b[39m \u001b[43mshear_img\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 26\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, region \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(regions):\n\u001b[0;32m 27\u001b[0m target_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(cropped_folder, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mimage_name[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m4\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00midx\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.jpg\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"Cell \u001b[1;32mIn[2], line 11\u001b[0m, in \u001b[0;36mshear_img\u001b[1;34m(image)\u001b[0m\n\u001b[0;32m 9\u001b[0m M \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfloat32([[\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.22\u001b[39m, \u001b[38;5;241m0\u001b[39m], [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m], [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m]])\n\u001b[0;32m 10\u001b[0m sheared_img \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mwarpPerspective(image, M, (\u001b[38;5;28mint\u001b[39m(cols \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1.5\u001b[39m), \u001b[38;5;28mint\u001b[39m(rows \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1.5\u001b[39m)))\n\u001b[1;32m---> 11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflip\u001b[49m\u001b[43m(\u001b[49m\u001b[43msheared_img\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"folder = \"./wetransfer_metingen-16-04-1200-1300_2024-04-16_1102/metingen 16-04 (1200-1300)/\"\n",
"images = [f for f in os.listdir(folder) if f.endswith(\".jpg\")]\n",
"cropped_folder = os.path.join(folder, \"cropped\")\n",
"os.makedirs(cropped_folder, exist_ok=True)\n",
"\n",
"# Initial image for ROI selection\n",
"init_image_path = os.path.join(folder, images[0])\n",
"init_image = cv2.imread(init_image_path)\n",
"sheared_init_image = shear_img(init_image)\n",
"\n",
"regions = []\n",
"for i in range(5): # Assume 5 regions as in your original code\n",
" r = cv2.selectROI(f\"Select the digit {i+1}\", sheared_init_image)\n",
" cv2.destroyAllWindows()\n",
" regions.append(r)\n",
"\n",
"data = {}\n",
"i = 0\n",
"# Process all images\n",
"for image_name in images:\n",
" try:\n",
" image_path = os.path.join(folder, image_name)\n",
" image = cv2.imread(image_path)\n",
" sheared_image = shear_img(image)\n",
"\n",
" for idx, region in enumerate(regions):\n",
" target_path = os.path.join(cropped_folder, f\"{image_name[:-4]}_{idx+1}.jpg\")\n",
" cropped = crop_image(sheared_image, region, target_path)\n",
" digit = ownOCR(cropped)\n",
"\n",
" if image_name not in data:\n",
" data[image_name] = \"\"\n",
"\n",
" data[image_name] += str(digit)\n",
" except Exception as e:\n",
" print(e)\n",
" continue"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"data2excel(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"# clean excel file\n",
"def data2cleanexcel(data):\n",
" # save the data in an excel file\n",
" fileName = \"data_clean.xlsx\"\n",
" workbook = xlsxwriter.Workbook(fileName)\n",
" worksheet = workbook.add_worksheet()\n",
"\n",
" dateFormat = workbook.add_format({\"num_format\": \"dd/mm/yy hh:mm:ss\"})\n",
"\n",
" # write the data\n",
" row = 0\n",
" for key, value in data.items():\n",
" try:\n",
" value = \"6\"+value[1:]\n",
" worksheet.write(row, 2, float(value[:6]))\n",
" date = key.split(\" \", 1)[1][:-4]\n",
" writeDate(worksheet, row, 0, date, dateFormat) \n",
" worksheet.write(row, 1, value)\n",
" except Exception as _:\n",
" continue\n",
"\n",
" row += 1\n",
"\n",
" workbook.close()\n",
"\n",
"\n",
"data2cleanexcel(data)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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