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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import cv2\n",
"import pandas as pd\n",
"from sklearn import svm\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import mean_squared_error\n",
"import os\n",
"import numpy as np\n",
"import cv2\n",
"from sklearn import svm\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import mean_squared_error, precision_score, recall_score\n",
"import os\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def extract_features(image_path):\n",
" # Read the image\n",
" img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
" \n",
" # Resize the image to a fixed size\n",
" img = cv2.resize(img, (200, 200))\n",
" \n",
" # Extract HOG features\n",
" hog = cv2.HOGDescriptor()\n",
" features = hog.compute(img)\n",
" \n",
" return features.flatten()\n",
"\n",
"def load_yolo_annotations(annotation_path, img_width, img_height):\n",
" with open(annotation_path, 'r') as file:\n",
" lines = file.readlines()\n",
" \n",
" for line in lines:\n",
" parts = line.strip().split()\n",
" class_id = int(parts[0])\n",
" if class_id == 3:\n",
" x_center = float(parts[1]) * img_width\n",
" y_center = float(parts[2]) * img_height\n",
" width = float(parts[3]) * img_width\n",
" height = float(parts[4]) * img_height\n",
" \n",
" # Convert from YOLO format (center x, center y, width, height) to (x, y, width, height)\n",
" x = x_center - (width / 2)\n",
" y = y_center - (height / 2)\n",
" return [x, y, width, height]\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Load dataset\n",
"X = [] # Features\n",
"y = [] # Labels (bounding box coordinates)\n",
"\n",
"# Path to your dataset and annotations\n",
"dataset_path = \"C:/Users/keese/term_project/data/processed/training/images\"\n",
"annotations_path = \"C:/Users/keese/term_project/data/processed/training/labels\"\n",
"\n",
"\n",
"for filename in os.listdir(dataset_path):\n",
" if filename.endswith(\".jpg\") or filename.endswith(\".png\"):\n",
" image_path = os.path.join(dataset_path, filename)\n",
" annotation_file = os.path.join(annotations_path, filename.replace('.jpg', '.txt').replace('.png', '.txt'))\n",
" \n",
" if not os.path.exists(annotation_file):\n",
" print(f\"Warning: Annotation file not found for {image_path}\")\n",
" continue\n",
" \n",
" # Read the image to get its dimensions\n",
" img = cv2.imread(image_path)\n",
" img_height, img_width = img.shape[:2]\n",
" \n",
" # Extract features\n",
" features = extract_features(image_path)\n",
" X.append(features)\n",
" \n",
" # Load bounding box coordinates from YOLO annotations\n",
" bbox = load_yolo_annotations(annotation_file, img_width, img_height)\n",
" y.append(bbox)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = np.array(X)\n",
"y = np.array(y)\n",
"y = np.array([bbox if bbox is not None else [np.nan, np.nan, np.nan, np.nan] for bbox in y], dtype=float)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Now create a mask for valid y values\n",
"valid_mask = ~np.isnan(y).any(axis=1) # Create a mask for valid y values\n",
"X = X[valid_mask] # Filter X\n",
"y = y[valid_mask] # Filter y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Split the dataset\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Create and train the SVM model for each coordinate\n",
"models = {\n",
" 'x': svm.SVR(kernel='linear'),\n",
" 'y': svm.SVR(kernel='linear'),\n",
" 'width': svm.SVR(kernel='linear'),\n",
" 'height': svm.SVR(kernel='linear')\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Train each model separately\n",
"for coord in models:\n",
" coord_index = ['x', 'y', 'width', 'height'].index(coord)\n",
" models[coord].fit(X_train, y_train[:, coord_index])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Evaluate the model\n",
"y_pred = np.column_stack([models[coord].predict(X_test) for coord in models])\n",
"y_test = np.array(y_test)\n",
"\n",
"\n",
"mse = mean_squared_error(y_test, y_pred)\n",
"precision = precision_score(y_test, y_pred)\n",
"recall = recall_score(y_test, y_pred)\n",
"print(f\"Mean Squared Error: {mse}\")\n",
"print(f\"Precision: {precision}\")\n",
"print(f\"Recall: {recall}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Function to predict the bounding box of a table in an image\n",
"def predict_table_bbox(image_path):\n",
" features = extract_features(image_path)\n",
" bbox = [models[coord].predict([features])[0] for coord in models]\n",
" return bbox\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from matplotlib.patches import Rectangle\n",
"import cv2\n",
"\n",
"def visualize_predictions(image_path, predictions):\n",
" # Load the image\n",
" img = cv2.imread(image_path)\n",
" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert BGR to RGB\n",
"\n",
" # Create a figure and axis\n",
" fig, ax = plt.subplots(1)\n",
" fig.set_size_inches(20, 20)\n",
"\n",
" # Display the image\n",
" ax.imshow(img)\n",
" ax.axis(\"off\") # Hide the axes\n",
"\n",
" # Loop through the predictions and draw rectangles\n",
" for pred in predictions:\n",
" x, y, w, h, cls = pred # Assuming pred is in the format (x, y, width, height, score, class_id)\n",
" \n",
" # Create a rectangle patch\n",
" rect = Rectangle((x, y), w, h, linewidth=2, edgecolor='r', facecolor='none')\n",
" ax.add_patch(rect)\n",
" \n",
" # Optionally, add a label with the class name and score\n",
" ax.text(x + w / 2, y, f'{cls}', color='r', ha='center', va='bottom')\n",
"\n",
" # Show the plot\n",
" plt.show()\n",
"\n",
"\n",
"\n",
"image_path = \"C:/Users/keese/term_project/Document_layout_Detection_Yolov8/training/images/PMC2987860_00002.jpg\"\n",
"\n",
"preds = predict_table_bbox(image_path)\n",
"\n",
"predictions = [\n",
" [preds[0], preds[1], preds[2], preds[3], 'Table']\n",
"]\n",
"\n",
"visualize_predictions(image_path, predictions)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image_path = \"C:/Users/keese/term_project/Document_layout_Detection_Yolov8/training/images/PMC3033327_00002.jpg\"\n",
"\n",
"preds = predict_table_bbox(image_path)\n",
"# Example predictions: list of [x, y, width, height, score, class_id]\n",
"predictions = [\n",
" [preds[0], preds[1], preds[2], preds[3], 'Table']\n",
"]\n",
"\n",
"visualize_predictions(image_path, predictions)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image_path = \"C:/Users/keese/term_project/Document_layout_Detection_Yolov8/validation/images/PMC2639556_00006.jpg\"\n",
"\n",
"preds = predict_table_bbox(image_path)\n",
"# Example predictions: list of [x, y, width, height, score, class_id]\n",
"predictions = [\n",
" [preds[0], preds[1], preds[2], preds[3], 'Table']\n",
"]\n",
"\n",
"visualize_predictions(image_path, predictions)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image_path = \"C:/Users/keese/term_project/Document_layout_Detection_Yolov8/validation/images/PMC2683799_00002.jpg\"\n",
"\n",
"preds = predict_table_bbox(image_path)\n",
"# Example predictions: list of [x, y, width, height, score, class_id]\n",
"predictions = [\n",
" [preds[0], preds[1], preds[2], preds[3], 'Table']\n",
"]\n",
"\n",
"visualize_predictions(image_path, predictions)"
]
}
],
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"display_name": "Python 3",
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"file_extension": ".py",
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