260 lines
5.8 MiB
Plaintext
260 lines
5.8 MiB
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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"0: 480x640 1 , 80.1ms\n",
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"Speed: 3.0ms preprocess, 80.1ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n"
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]
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x800 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"0: 480x640 1 , 80.1ms\n",
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"Speed: 2.0ms preprocess, 80.1ms inference, 0.0ms postprocess per image at shape (1, 3, 480, 640)\n"
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]
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x800 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"0: 480x640 1 , 100.1ms\n",
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"Speed: 2.0ms preprocess, 100.1ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n"
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]
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},
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"0: 480x640 1 , 76.1ms\n",
|
|||
|
|
"Speed: 1.0ms preprocess, 76.1ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"0: 480x640 1 , 85.1ms\n",
|
|||
|
|
"Speed: 2.0ms preprocess, 85.1ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"0: 480x640 1 , 74.3ms\n",
|
|||
|
|
"Speed: 2.0ms preprocess, 74.3ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"0: 480x640 1 , 87.6ms\n",
|
|||
|
|
"Speed: 1.0ms preprocess, 87.6ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"0: 480x640 1 , 73.1ms\n",
|
|||
|
|
"Speed: 2.0ms preprocess, 73.1ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import cv2\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"import os\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"\n",
|
|||
|
|
"from config import NUM_CLASSES, CRNN_WEIGHTS_PATH, YOLO_WEIGHTS_PATH\n",
|
|||
|
|
"from license_plate_recognizer import LicensePlateRecognizer\n",
|
|||
|
|
"\n",
|
|||
|
|
"def process_images_in_folder(folder_path, yolo_weights_path, crnn_weights_path):\n",
|
|||
|
|
" lpr = LicensePlateRecognizer(\n",
|
|||
|
|
" yolo_model_path=yolo_weights_path,\n",
|
|||
|
|
" crnn_model_path=crnn_weights_path,\n",
|
|||
|
|
" num_classes=NUM_CLASSES,\n",
|
|||
|
|
" device=\"cpu\"\n",
|
|||
|
|
" )\n",
|
|||
|
|
" \n",
|
|||
|
|
" image_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
|
|||
|
|
" if not image_files:\n",
|
|||
|
|
" print(\"В указанной папке нет изображений.\")\n",
|
|||
|
|
" return\n",
|
|||
|
|
"\n",
|
|||
|
|
" for image_file in image_files:\n",
|
|||
|
|
" image_path = os.path.join(folder_path, image_file)\n",
|
|||
|
|
" image = cv2.imread(image_path)\n",
|
|||
|
|
" if image is None:\n",
|
|||
|
|
" print(f\"Не удалось загрузить изображение: {image_file}\")\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" \n",
|
|||
|
|
" detections = lpr.detect_and_recognize_frame(image, padding=5)\n",
|
|||
|
|
" \n",
|
|||
|
|
" display_image = image.copy()\n",
|
|||
|
|
"\n",
|
|||
|
|
" for det in detections:\n",
|
|||
|
|
" x1, y1, x2, y2 = det[\"bbox\"]\n",
|
|||
|
|
" text = det[\"text\"]\n",
|
|||
|
|
"\n",
|
|||
|
|
" cv2.rectangle(display_image, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
|
|||
|
|
" cv2.putText(\n",
|
|||
|
|
" display_image, text, (x1, y1 - 10),\n",
|
|||
|
|
" cv2.FONT_HERSHEY_SIMPLEX,\n",
|
|||
|
|
" 0.7, (0, 255, 0), 2\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" height, width, _ = display_image.shape\n",
|
|||
|
|
" black_bar_width = 300\n",
|
|||
|
|
" black_bar = np.zeros((height, black_bar_width, 3), dtype=np.uint8)\n",
|
|||
|
|
"\n",
|
|||
|
|
" y_start = 40\n",
|
|||
|
|
" for i, det in enumerate(detections):\n",
|
|||
|
|
" txt = det[\"text\"]\n",
|
|||
|
|
" cv2.putText(\n",
|
|||
|
|
" black_bar,\n",
|
|||
|
|
" f\"Plate #{i+1}: {txt}\",\n",
|
|||
|
|
" (10, y_start),\n",
|
|||
|
|
" cv2.FONT_HERSHEY_SIMPLEX,\n",
|
|||
|
|
" 0.7, (255, 255, 255), 2\n",
|
|||
|
|
" )\n",
|
|||
|
|
" y_start += 40\n",
|
|||
|
|
"\n",
|
|||
|
|
" display_image = np.hstack((display_image, black_bar))\n",
|
|||
|
|
"\n",
|
|||
|
|
" display_image_rgb = cv2.cvtColor(display_image, cv2.COLOR_BGR2RGB)\n",
|
|||
|
|
" plt.figure(figsize=(12, 8))\n",
|
|||
|
|
" plt.imshow(display_image_rgb)\n",
|
|||
|
|
" plt.title(f\"Processed: {image_file}\")\n",
|
|||
|
|
" plt.axis(\"off\")\n",
|
|||
|
|
" plt.show()\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"if __name__ == \"__main__\":\n",
|
|||
|
|
" folder_path = \"img\" # Укажите путь к папке с изображениями\n",
|
|||
|
|
"\n",
|
|||
|
|
" process_images_in_folder(folder_path, YOLO_WEIGHTS_PATH, CRNN_WEIGHTS_PATH)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": ".venv",
|
|||
|
|
"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": "3.9.13"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|