stuff

.config/quickshell/oo/scripts/images/find_regions.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+
+import argparse
+import cv2
+import json
+import numpy as np
+import sys
+
+DEFAULT_IMAGE_PATH = '/tmp/quickshell/media/screenshot/image'
+
+def iou(boxA, boxB):
+    # Compute intersection over union for two boxes
+    xA = max(boxA['x'], boxB['x'])
+    yA = max(boxA['y'], boxB['y'])
+    xB = min(boxA['x'] + boxA['width'], boxB['x'] + boxB['width'])
+    yB = min(boxA['y'] + boxA['height'], boxB['y'] + boxB['height'])
+    interW = max(0, xB - xA)
+    interH = max(0, yB - yA)
+    interArea = interW * interH
+    boxAArea = boxA['width'] * boxA['height']
+    boxBArea = boxB['width'] * boxB['height']
+    iou = interArea / float(boxAArea + boxBArea - interArea) if (boxAArea + boxBArea - interArea) > 0 else 0
+    return iou
+
+def non_max_suppression(regions, iou_threshold=0.7):
+    # Sort by area (largest first)
+    regions = sorted(regions, key=lambda r: r['width'] * r['height'], reverse=True)
+    keep = []
+    while regions:
+        current = regions.pop(0)
+        keep.append(current)
+        regions = [r for r in regions if iou(current, r) < iou_threshold]
+    return keep
+
+def find_regions(image_path, min_width, min_height, max_width=None, max_height=None, quality=False, k=150, min_size=20, sigma=0.8, resize_factor=1.0):
+    image = cv2.imread(image_path)
+    if image is None:
+        print(f'Error: Could not load image {image_path}', file=sys.stderr)
+        sys.exit(1)
+    orig_h, orig_w = image.shape[:2]
+    if resize_factor != 1.0:
+        image = cv2.resize(image, (int(orig_w * resize_factor), int(orig_h * resize_factor)), interpolation=cv2.INTER_AREA)
+    ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()
+    ss.setBaseImage(image)
+    if quality:
+        ss.switchToSelectiveSearchQuality(k, min_size, sigma)
+    else:
+        ss.switchToSelectiveSearchFast(k, min_size, sigma)
+    rects = ss.process()
+    regions = []
+    for (x, y, w, h) in rects:
+        # Scale regions back to original image size if resized
+        if resize_factor != 1.0:
+            x = int(x / resize_factor)
+            y = int(y / resize_factor)
+            w = int(w / resize_factor)
+            h = int(h / resize_factor)
+        # Filter out region that is exactly the same size as the original image
+        if w == orig_w and h == orig_h and x == 0 and y == 0:
+            continue
+        if w > min_width and h > min_height:
+            if (max_width is None or w < max_width) and (max_height is None or h < max_height):
+                regions.append({'x': int(x), 'y': int(y), 'width': int(w), 'height': int(h)})
+    # Remove duplicates/overlaps
+    regions = non_max_suppression(regions, iou_threshold=0.7)
+    return regions, cv2.imread(image_path)  # Return original image for drawing
+
+def draw_regions(image, regions, output_path):
+    for region in regions:
+        if 'x' in region:
+            x, y, w, h = region['x'], region['y'], region['width'], region['height']
+        elif 'at' in region and 'size' in region:
+            x, y = region['at']
+            w, h = region['size']
+        else:
+            continue
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
+    cv2.imwrite(output_path, image)
+
+def main():
+    parser = argparse.ArgumentParser(description='Find regions of interest in an image using selective search.')
+    parser.add_argument('-i', '--image', default=DEFAULT_IMAGE_PATH, help='Path to input image')
+    parser.add_argument('-do', '--debug-output', help='Path to save debug image with rectangles')
+    parser.add_argument('--min-width', type=int, default=200, help='Minimum width of detected region')
+    parser.add_argument('--min-height', type=int, default=100, help='Minimum height of detected region')
+    parser.add_argument('--max-width', type=int, help='Maximum width of detected region')
+    parser.add_argument('--max-height', type=int, help='Maximum height of detected region')
+    parser.add_argument('--single', action='store_true', help='Only output the most likely (largest) region')
+    parser.add_argument('--quality', action='store_true', help='Use quality mode for selective search (slower, less sensitive)')
+    parser.add_argument('--k', type=int, default=3000, help='Segmentation parameter k (default: 150)')
+    parser.add_argument('--min-size', type=int, default=50, help='Segmentation parameter min_size (default: 20)')
+    parser.add_argument('--sigma', type=float, default=0.6, help='Segmentation parameter sigma (default: 0.8)')
+    parser.add_argument('--resize-factor', type=float, default=0.1, help='Resize factor for input image before processing (default: 1.0, e.g. 0.5 for half size)')
+    parser.add_argument('--hyprctl', action='store_true', help='Mimics hyprctl\'s window output, like {"at": [x, y], "size": [w, h]}')
+    args = parser.parse_args()
+
+    regions, image = find_regions(
+        args.image,
+        min_width=args.min_width,
+        min_height=args.min_height,
+        max_width=args.max_width,
+        max_height=args.max_height,
+        quality=args.quality,
+        k=args.k,
+        min_size=args.min_size,
+        sigma=args.sigma,
+        resize_factor=args.resize_factor
+    )
+    if args.single and regions:
+        largest = max(regions, key=lambda r: r['width'] * r['height'])
+        regions = [largest]
+    if args.hyprctl:
+        regions = [{"at": [r['x'], r['y']], "size": [r['width'], r['height']]} for r in regions]
+    print(json.dumps(regions))
+    if args.debug_output:
+        draw_regions(image, regions, args.debug_output)
+
+if __name__ == '__main__':
+    main()
+

.config/quickshell/oo/scripts/images/least_busy_region.py

@@ -0,0 +1,341 @@
+#!/usr/bin/env python3
+# Disclaimer: This script was ai-generated and went through minimal revision.
+
+import os
+os.environ["OPENCV_LOG_LEVEL"] = "SILENT"
+import cv2
+import numpy as np
+import argparse
+import json
+
+def center_crop(img, target_w, target_h):
+    h, w = img.shape[:2]
+    if w == target_w and h == target_h:
+        return img
+    x1 = max(0, (w - target_w) // 2)
+    y1 = max(0, (h - target_h) // 2)
+    x2 = x1 + target_w
+    y2 = y1 + target_h
+    return img[y1:y2, x1:x2]
+
+def find_least_busy_region(image_path, region_width=300, region_height=200, screen_width=None, screen_height=None, verbose=False, stride=2, screen_mode="fill", horizontal_padding=50, vertical_padding=50):
+    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+    if img is None:
+        raise FileNotFoundError(f"Image not found: {image_path}")
+    orig_h, orig_w = img.shape
+    scale = 1.0
+    if screen_width is not None and screen_height is not None:
+        scale_w = screen_width / orig_w
+        scale_h = screen_height / orig_h
+        if screen_mode == "fill":
+            scale = max(scale_w, scale_h)
+        else:
+            scale = min(scale_w, scale_h)
+        new_w = int(orig_w * scale)
+        new_h = int(orig_h * scale)
+        if verbose:
+            print(f"Scaling image from {orig_w}x{orig_h} to {new_w}x{new_h} (scale: {scale:.3f}, mode: {screen_mode})")
+        img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
+        img = center_crop(img, screen_width, screen_height)
+        if verbose:
+            print(f"Cropped image to {screen_width}x{screen_height}")
+    else:
+        if verbose:
+            print(f"Using original image size: {orig_w}x{orig_h}")
+    arr = img.astype(np.float64)
+    h, w = arr.shape
+    # Use OpenCV's integral for fast computation
+    integral = cv2.integral(arr, sdepth=cv2.CV_64F)[1:,1:]
+    integral_sq = cv2.integral(arr**2, sdepth=cv2.CV_64F)[1:,1:]
+    def region_sum(ii, x1, y1, x2, y2):
+        total = ii[y2, x2]
+        if x1 > 0:
+            total -= ii[y2, x1-1]
+        if y1 > 0:
+            total -= ii[y1-1, x2]
+        if x1 > 0 and y1 > 0:
+            total += ii[y1-1, x1-1]
+        return total
+    min_var = None
+    min_coords = (0, 0)
+    area = region_width * region_height
+    x_start = horizontal_padding
+    y_start = vertical_padding
+    x_end = w - region_width - horizontal_padding + 1
+    y_end = h - region_height - vertical_padding + 1
+    for y in range(y_start, max(y_end, y_start+1), stride):
+        for x in range(x_start, max(x_end, x_start+1), stride):
+            x1, y1 = x, y
+            x2, y2 = x + region_width - 1, y + region_height - 1
+            s = region_sum(integral, x1, y1, x2, y2)
+            s2 = region_sum(integral_sq, x1, y1, x2, y2)
+            mean = s / area
+            var = (s2 / area) - (mean ** 2)
+            if (min_var is None) or (var < min_var):
+                min_var = var
+                min_coords = (x, y)
+    return min_coords, min_var
+
+def find_largest_region(image_path, screen_width=None, screen_height=None, verbose=False, stride=2, screen_mode="fill", threshold=100.0, aspect_ratio=1.0, horizontal_padding=50, vertical_padding=50):
+    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+    if img is None:
+        raise FileNotFoundError(f"Image not found: {image_path}")
+    orig_h, orig_w = img.shape
+    scale = 1.0
+    if screen_width is not None and screen_height is not None:
+        scale_w = screen_width / orig_w
+        scale_h = screen_height / orig_h
+        if screen_mode == "fill":
+            scale = max(scale_w, scale_h)
+        else:
+            scale = min(scale_w, scale_h)
+        new_w = int(orig_w * scale)
+        new_h = int(orig_h * scale)
+        if verbose:
+            print(f"Scaling image from {orig_w}x{orig_h} to {new_w}x{new_h} (scale: {scale:.3f}, mode: {screen_mode})")
+        img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
+        img = center_crop(img, screen_width, screen_height)
+        if verbose:
+            print(f"Cropped image to {screen_width}x{screen_height}")
+    else:
+        if verbose:
+            print(f"Using original image size: {orig_w}x{orig_h}")
+    arr = img.astype(np.float64)
+    h, w = arr.shape
+    # Use OpenCV's integral for fast computation
+    integral = cv2.integral(arr, sdepth=cv2.CV_64F)[1:,1:]
+    integral_sq = cv2.integral(arr**2, sdepth=cv2.CV_64F)[1:,1:]
+    def region_sum(ii, x1, y1, x2, y2):
+        total = ii[y2, x2]
+        if x1 > 0:
+            total -= ii[y2, x1-1]
+        if y1 > 0:
+            total -= ii[y1-1, x2]
+        if x1 > 0 and y1 > 0:
+            total += ii[y1-1, x1-1]
+        return total
+    min_size = 10
+    max_size = min(h, int(w / aspect_ratio)) if aspect_ratio >= 1.0 else min(int(h * aspect_ratio), w)
+    best = None
+    best_size = min_size
+    while min_size <= max_size:
+        mid = (min_size + max_size) // 2
+        if aspect_ratio >= 1.0:
+            region_h = mid
+            region_w = int(mid * aspect_ratio)
+        else:
+            region_w = mid
+            region_h = int(mid / aspect_ratio)
+        if region_w > w or region_h > h:
+            max_size = mid - 1
+            continue
+        found = False
+        x_start = horizontal_padding
+        y_start = vertical_padding
+        x_end = w - region_w - horizontal_padding + 1
+        y_end = h - region_h - vertical_padding + 1
+        for y in range(y_start, max(y_end, y_start+1), stride):
+            for x in range(x_start, max(x_end, x_start+1), stride):
+                x1, y1 = x, y
+                x2, y2 = x + region_w - 1, y + region_h - 1
+                s = region_sum(integral, x1, y1, x2, y2)
+                s2 = region_sum(integral_sq, x1, y1, x2, y2)
+                area = region_w * region_h
+                mean = s / area
+                var = (s2 / area) - (mean ** 2)
+                if var <= threshold:
+                    found = True
+                    best = (x, y, region_w, region_h, var)
+                    break
+            if found:
+                break
+        if found:
+            best_size = mid
+            min_size = mid + 1
+        else:
+            max_size = mid - 1
+    if best:
+        x, y, region_w, region_h, var = best
+        center_x = x + region_w // 2
+        center_y = y + region_h // 2
+        return (center_x, center_y), (region_w, region_h), var
+    else:
+        return None, (0, 0), None
+
+def draw_region(image_path, coords, region_width=300, region_height=200, output_path='output.png', screen_width=None, screen_height=None, screen_mode="fill"):
+    img = cv2.imread(image_path)
+    if img is None:
+        raise FileNotFoundError(f"Image not found: {image_path}")
+    orig_h, orig_w = img.shape[:2]
+    if screen_width is not None and screen_height is not None:
+        scale_w = screen_width / orig_w
+        scale_h = screen_height / orig_h
+        if screen_mode == "fill":
+            scale = max(scale_w, scale_h)
+        else:
+            scale = min(scale_w, scale_h)
+        new_w = int(orig_w * scale)
+        new_h = int(orig_h * scale)
+        img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
+        img = center_crop(img, screen_width, screen_height)
+    x, y = coords
+    cv2.rectangle(img, (x, y), (x+region_width-1, y+region_height-1), (0,0,255), 3)
+    cv2.imwrite(output_path, img)
+    print(f"Saved output image with rectangle at {output_path}")
+
+def draw_largest_region(image_path, center, size, output_path='output.png', screen_width=None, screen_height=None, screen_mode="fill"):
+    img = cv2.imread(image_path)
+    if img is None:
+        raise FileNotFoundError(f"Image not found: {image_path}")
+    orig_h, orig_w = img.shape[:2]
+    if screen_width is not None and screen_height is not None:
+        scale_w = screen_width / orig_w
+        scale_h = screen_height / orig_h
+        if screen_mode == "fill":
+            scale = max(scale_w, scale_h)
+        else:
+            scale = min(scale_w, scale_h)
+        new_w = int(orig_w * scale)
+        new_h = int(orig_h * scale)
+        img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
+        img = center_crop(img, screen_width, screen_height)
+    cx, cy = center
+    region_w, region_h = size
+    x1 = cx - region_w // 2
+    y1 = cy - region_h // 2
+    x2 = cx + region_w // 2 - 1
+    y2 = cy + region_h // 2 - 1
+    cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,0), 3)
+    cv2.imwrite(output_path, img)
+    print(f"Saved output image with largest region at {output_path}")
+
+def get_dominant_color(image_path, x, y, w, h, screen_width=None, screen_height=None, screen_mode="fill"):
+    img = cv2.imread(image_path)
+    if img is None:
+        raise FileNotFoundError(f"Image not found: {image_path}")
+    orig_h, orig_w = img.shape[:2]
+    if screen_width is not None and screen_height is not None:
+        scale_w = screen_width / orig_w
+        scale_h = screen_height / orig_h
+        if screen_mode == "fill":
+            scale = max(scale_w, scale_h)
+        else:
+            scale = min(scale_w, scale_h)
+        new_w = int(orig_w * scale)
+        new_h = int(orig_h * scale)
+        img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
+        img = center_crop(img, screen_width, screen_height)
+    # Ensure region is within bounds
+    x = max(0, x)
+    y = max(0, y)
+    w = max(1, min(w, img.shape[1] - x))
+    h = max(1, min(h, img.shape[0] - y))
+    region = img[y:y+h, x:x+w]
+    if region.size == 0 or region.shape[0] == 0 or region.shape[1] == 0:
+        return [0, 0, 0]
+    region = region.reshape((-1, 3))
+    # Filter out black pixels (optional, improves accuracy for some images)
+    non_black = region[np.any(region > 10, axis=1)]
+    if non_black.shape[0] == 0:
+        non_black = region
+    region = np.float32(non_black)
+    if region.shape[0] < 3:
+        return [int(x) for x in np.mean(region, axis=0)]
+    # K-means to find dominant color
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+    K = min(3, region.shape[0])
+    _, labels, centers = cv2.kmeans(region, K, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
+    counts = np.bincount(labels.flatten())
+    dominant = centers[np.argmax(counts)]
+    return [int(x) for x in dominant]
+
+def main():
+    parser = argparse.ArgumentParser(description="Find least busy region in an image and output a JSON. Made for determining a suitable position for a wallpaper widget.")
+    parser.add_argument("image_path", help="Path to the input image")
+    parser.add_argument("--width", type=int, default=300, help="Region width")
+    parser.add_argument("--height", type=int, default=200, help="Region height")
+    parser.add_argument("-v", "--visual-output", action="store_true", help="Output image with rectangle")
+    parser.add_argument("--screen-width", type=int, default=1920, help="Screen width for wallpaper scaling")
+    parser.add_argument("--screen-height", type=int, default=1080, help="Screen height for wallpaper scaling")
+    parser.add_argument("--stride", type=int, default=10, help="Step size for sliding window (higher is faster, less precise)")
+    parser.add_argument("--screen-mode", choices=["fill", "fit"], default="fill", help="Wallpaper scaling mode: 'fill' (default) or 'fit'")
+    parser.add_argument("--verbose", action="store_true", help="Print verbose output")
+    parser.add_argument("-l", "--largest-region", action="store_true", help="Find the largest region under the variance threshold and output its center")
+    parser.add_argument("-t", "--variance-threshold", type=float, default=1000.0, help="Variance threshold for largest region mode")
+    parser.add_argument("--aspect-ratio", type=float, default=1.78, help="Aspect ratio (width/height) for largest region mode")
+    parser.add_argument("--horizontal-padding", "-hp", type=int, default=50, help="Minimum horizontal distance from region to image edge")
+    parser.add_argument("--vertical-padding", "-vp", type=int, default=50, help="Minimum vertical distance from region to image edge")
+    args = parser.parse_args()
+
+    if args.largest_region:
+        center, size, var = find_largest_region(
+            args.image_path,
+            screen_width=args.screen_width,
+            screen_height=args.screen_height,
+            verbose=args.verbose,
+            stride=args.stride,
+            screen_mode=args.screen_mode,
+            threshold=args.variance_threshold,
+            aspect_ratio=args.aspect_ratio,
+            horizontal_padding=args.horizontal_padding,
+            vertical_padding=args.vertical_padding
+        )
+        if center:
+            if args.visual_output:
+                draw_largest_region(args.image_path, center, size, screen_width=args.screen_width, screen_height=args.screen_height, screen_mode=args.screen_mode)
+            # Extract dominant color
+            cx, cy = center
+            region_w, region_h = size
+            x1 = cx - region_w // 2
+            y1 = cy - region_h // 2
+            dominant_color = get_dominant_color(
+                args.image_path, x1, y1, region_w, region_h,
+                screen_width=args.screen_width, screen_height=args.screen_height, screen_mode=args.screen_mode
+            )
+            dominant_color_hex = '#{:02x}{:02x}{:02x}'.format(*dominant_color)
+            print(json.dumps({
+                "center_x": center[0],
+                "center_y": center[1],
+                "width": size[0],
+                "height": size[1],
+                "variance": var,
+                "dominant_color": dominant_color_hex
+            }))
+        else:
+            print(json.dumps({"error": "No region found under the threshold."}))
+        return
+
+    coords, variance = find_least_busy_region(
+        args.image_path,
+        region_width=args.width,
+        region_height=args.height,
+        screen_width=args.screen_width,
+        screen_height=args.screen_height,
+        verbose=args.verbose,
+        stride=args.stride,
+        screen_mode=args.screen_mode,
+        horizontal_padding=args.horizontal_padding,
+        vertical_padding=args.vertical_padding
+    )
+    if args.visual_output:
+        draw_region(args.image_path, coords, region_width=args.width, region_height=args.height, screen_width=args.screen_width, screen_height=args.screen_height, screen_mode=args.screen_mode)
+    # Output JSON with center point
+    center_x = coords[0] + args.width // 2
+    center_y = coords[1] + args.height // 2
+    dominant_color = get_dominant_color(
+        args.image_path, coords[0], coords[1], args.width, args.height,
+        screen_width=args.screen_width, screen_height=args.screen_height, screen_mode=args.screen_mode
+    )
+    dominant_color_hex = '#{:02x}{:02x}{:02x}'.format(*dominant_color)
+    print(json.dumps({
+        "center_x": center_x,
+        "center_y": center_y,
+        "width": args.width,
+        "height": args.height,
+        "variance": variance,
+        "dominant_color": dominant_color_hex
+    }))
+
+if __name__ == "__main__":
+    main()
+