rework for API, folder changes, easter eggs and fun

scripts/init.sh

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+#!/bin/bash
+
+# Copy the original tesseract-ocr files to the volume directory without overwriting existing files
+echo "Copying original files without overwriting existing files"
+mkdir -p /usr/share/tesseract-ocr
+cp -rn /usr/share/tesseract-ocr-original/* /usr/share/tesseract-ocr
+
+# Run the main command
+exec "$@"

scripts/split_photos.py

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+import sys
+import cv2
+import numpy as np
+import os 
+
+def find_photo_boundaries(image, background_color, tolerance=30, min_area=10000, min_contour_area=500):
+    mask = cv2.inRange(image, background_color - tolerance, background_color + tolerance)
+    mask = cv2.bitwise_not(mask)
+    kernel = np.ones((5,5),np.uint8)
+    mask = cv2.dilate(mask, kernel, iterations=2)
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    photo_boundaries = []
+    for contour in contours:
+        x, y, w, h = cv2.boundingRect(contour)
+        area = w * h
+        contour_area = cv2.contourArea(contour)
+        if area >= min_area and contour_area >= min_contour_area:
+            photo_boundaries.append((x, y, w, h))
+
+    return photo_boundaries
+
+def estimate_background_color(image, sample_points=5):
+    h, w, _ = image.shape
+    points = [
+        (0, 0),
+        (w - 1, 0),
+        (w - 1, h - 1),
+        (0, h - 1),
+        (w // 2, h // 2),
+    ]
+
+    colors = []
+    for x, y in points:
+        colors.append(image[y, x])
+
+    return np.median(colors, axis=0)
+
+def auto_rotate(image, angle_threshold=10):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    if len(contours) == 0:
+        return image
+
+    largest_contour = max(contours, key=cv2.contourArea)
+    mu = cv2.moments(largest_contour)
+    
+    if mu["m00"] == 0:
+        return image
+    
+    x_centroid = int(mu["m10"] / mu["m00"])
+    y_centroid = int(mu["m01"] / mu["m00"])
+
+    coords = np.column_stack(np.where(binary > 0))
+    u, _, vt = np.linalg.svd(coords - np.array([[y_centroid, x_centroid]]), full_matrices=False)
+
+    angle = np.arctan2(u[1, 0], u[0, 0]) * 180 / np.pi
+
+    if angle < -45:
+        angle = -(90 + angle)
+    else:
+        angle = -angle
+
+    if abs(angle) < angle_threshold:
+        return image
+
+    (h, w) = image.shape[:2]
+    center = (w // 2, h // 2)
+    M = cv2.getRotationMatrix2D(center, angle, 1.0)
+    return cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
+
+
+
+def crop_borders(image, border_color, tolerance=30):
+    mask = cv2.inRange(image, border_color - tolerance, border_color + tolerance)
+    
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if len(contours) == 0:
+        return image
+
+    largest_contour = max(contours, key=cv2.contourArea)
+    x, y, w, h = cv2.boundingRect(largest_contour)
+    
+    return image[y:y+h, x:x+w]
+    
+def split_photos(input_file, output_directory, tolerance=30, min_area=10000, min_contour_area=500, angle_threshold=10, border_size=0):
+    image = cv2.imread(input_file)
+    background_color = estimate_background_color(image)
+
+    # Add a constant border around the image
+    image = cv2.copyMakeBorder(image, border_size, border_size, border_size, border_size, cv2.BORDER_CONSTANT, value=background_color)
+
+    photo_boundaries = find_photo_boundaries(image, background_color, tolerance)
+
+    if not os.path.exists(output_directory):
+        os.makedirs(output_directory)
+
+    # Get the input file's base name without the extension
+    input_file_basename = os.path.splitext(os.path.basename(input_file))[0]
+
+    for idx, (x, y, w, h) in enumerate(photo_boundaries):
+        cropped_image = image[y:y+h, x:x+w]
+        cropped_image = auto_rotate(cropped_image, angle_threshold)
+
+        # Remove the added border
+        cropped_image = cropped_image[border_size:-border_size, border_size:-border_size]
+
+        output_path = os.path.join(output_directory, f"{input_file_basename}_{idx+1}.png")
+        cv2.imwrite(output_path, cropped_image)
+        print(f"Saved {output_path}")
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python3 split_photos.py <input_file> <output_directory> [tolerance] [min_area] [min_contour_area] [angle_threshold] [border_size]")
+        print("\nParameters:")
+        print("  <input_file>       - The input scanned image containing multiple photos.")
+        print("  <output_directory> - The directory where the result images should be placed.")
+        print("  [tolerance]        - Optional. Determines the range of color variation around the estimated background color (default: 30).")
+        print("  [min_area]         - Optional. Sets the minimum area threshold for a photo (default: 10000).")
+        print("  [min_contour_area] - Optional. Sets the minimum contour area threshold for a photo (default: 500).")
+        print("  [angle_threshold]  - Optional. Sets the minimum absolute angle required for the image to be rotated (default: 10).")
+        print("  [border_size]      - Optional. Sets the size of the border added and removed to prevent white borders in the output (default: 0).")
+        sys.exit(1)
+
+    input_file = sys.argv[1]
+    output_directory = sys.argv[2]
+    tolerance = int(sys.argv[3]) if len(sys.argv) > 3 else 20
+    min_area = int(sys.argv[4]) if len(sys.argv) > 4 else 8000
+    min_contour_area = int(sys.argv[5]) if len(sys.argv) > 5 else 500
+    angle_threshold = int(sys.argv[6]) if len(sys.argv) > 6 else 60
+    border_size = int(sys.argv[7]) if len(sys.argv) > 7 else 0
+    split_photos(input_file, output_directory, tolerance=tolerance, min_area=min_area, min_contour_area=min_contour_area, angle_threshold=angle_threshold, border_size=border_size)