Make new file for each step of processing

crop_to_screen.py

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+"""
+Crop Concept 2 PM5 rowing machine screens from photos using OpenCV.
+
+Detection strategy:
+  The LCD screen has HIGH internal edge density (text/numbers/lines)
+  compared to other bright regions (windows, walls, lockers).
+  We threshold at multiple brightness levels, filter by edge density,
+  aspect ratio, and size, then pick the best match.
+
+Usage:
+  python crop_screens.py [input_dir] [output_dir]
+"""
+
+import cv2
+import numpy as np
+import os
+import glob
+import sys
+
+
+def find_screen(image):
+    """
+    Detect the Concept 2 PM5 LCD screen region in the image.
+
+    Returns (x, y, w, h) bounding box or None if not found.
+    """
+    h_img, w_img = image.shape[:2]
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Pre-compute edge map for internal-content scoring
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    edges = cv2.Canny(blurred, 50, 150)
+
+    candidates = []
+
+    # Sweep brightness thresholds — screen brightness varies by
+    # lighting conditions (ranges from ~100 in dim gyms to ~200+)
+    for thresh_val in range(120, 200, 10):
+        _, thresh = cv2.threshold(gray, thresh_val, 255, cv2.THRESH_BINARY)
+        kern = cv2.getStructuringElement(cv2.MORPH_RECT, (11, 11))
+        thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kern)
+        thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kern)
+
+        contours, _ = cv2.findContours(
+            thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+        )
+
+        for cnt in contours:
+            x, y, w, h = cv2.boundingRect(cnt)
+            area = cv2.contourArea(cnt)
+            rect_area = w * h
+            if rect_area == 0:
+                continue
+
+            # Size: screen is a small-to-medium portion of the photo
+            area_ratio = rect_area / (h_img * w_img)
+            if area_ratio < 0.005 or area_ratio > 0.12:
+                continue
+
+            # Aspect ratio: LCD is roughly square (0.5 to 1.6)
+            aspect = w / h
+            if aspect < 0.5 or aspect > 1.6:
+                continue
+
+            # Rectangularity
+            rectangularity = area / rect_area
+            if rectangularity < 0.4:
+                continue
+
+            # KEY: edge density — LCD with text > 0.03, plain surfaces < 0.01
+            roi_edges = edges[y : y + h, x : x + w]
+            edge_density = np.sum(roi_edges > 0) / rect_area
+            if edge_density < 0.03:
+                continue
+
+            # Score: edge density * area * rectangularity
+            # This favours text-rich regions that are large and well-shaped
+            score = edge_density * area * rectangularity
+            candidates.append((score, x, y, w, h))
+
+    if not candidates:
+        return None
+
+    candidates.sort(key=lambda c: c[0], reverse=True)
+    return candidates[0][1:]
+
+
+def crop_screen(image_path, output_path, padding=15):
+    """Load an image, find the screen, crop and save it."""
+    image = cv2.imread(image_path)
+    if image is None:
+        print(f"  ERROR: Could not read {image_path}")
+        return False
+
+    h_img, w_img = image.shape[:2]
+    result = find_screen(image)
+
+    if result is None:
+        print(f"  SKIP:  No screen detected in {os.path.basename(image_path)}")
+        return False
+
+    x, y, w, h = result
+
+    # Add padding, clamped to image bounds
+    x1 = max(0, x - padding)
+    y1 = max(0, y - padding)
+    x2 = min(w_img, x + w + padding)
+    y2 = min(h_img, y + h + padding)
+
+    cropped = image[y1:y2, x1:x2]
+    cv2.imwrite(output_path, cropped, [cv2.IMWRITE_JPEG_QUALITY, 95])
+    print(
+        f"  OK:    {os.path.basename(image_path)} -> {os.path.basename(output_path)}  ({w}x{h})"
+    )
+    return True
+
+
+def main():
+    if len(sys.argv) >= 3:
+        input_dir = sys.argv[1]
+        output_dir = sys.argv[2]
+    elif len(sys.argv) == 2:
+        input_dir = sys.argv[1]
+        output_dir = os.path.join(input_dir, "cropped")
+    else:
+        input_dir = "/mnt/user-data/uploads"
+        output_dir = "/mnt/user-data/outputs"
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    images = sorted(
+        glob.glob(os.path.join(input_dir, "*.JPEG"))
+        + glob.glob(os.path.join(input_dir, "*.jpeg"))
+        + glob.glob(os.path.join(input_dir, "*.jpg"))
+        + glob.glob(os.path.join(input_dir, "*.JPG"))
+    )
+
+    if not images:
+        print(f"No images found in {input_dir}")
+        return
+
+    print(f"Found {len(images)} images in {input_dir}\n")
+
+    success = 0
+    for img_path in images:
+        name = os.path.splitext(os.path.basename(img_path))[0]
+        out_path = os.path.join(output_dir, f"{name}_screen.jpg")
+        if crop_screen(img_path, out_path):
+            success += 1
+
+    print(f"\nDone: {success}/{len(images)} screens cropped -> {output_dir}")
+
+
+if __name__ == "__main__":
+    main()