forked from Shinonome/dots-hyprland
script for least busy region of image
This commit is contained in:
end-4
Executable
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#!/usr/bin/env -S\_/bin/sh\_-c\_"source\_\$(eval\_echo\_\$ILLOGICAL_IMPULSE_VIRTUAL_ENV)/bin/activate&&exec\_python\_-E\_"\$0"\_"\$@""
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# Disclaimer: This script is vibe-coded.
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import os
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os.environ["OPENCV_LOG_LEVEL"] = "SILENT"
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import cv2
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import numpy as np
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import argparse
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import json
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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"):
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img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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if img is None:
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raise FileNotFoundError(f"Image not found: {image_path}")
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orig_h, orig_w = img.shape
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scale = 1.0
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if screen_width is not None and screen_height is not None:
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scale_w = screen_width / orig_w
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scale_h = screen_height / orig_h
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if screen_mode == "fill":
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scale = max(scale_w, scale_h)
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else:
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scale = min(scale_w, scale_h)
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new_w = int(orig_w * scale)
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new_h = int(orig_h * scale)
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if verbose:
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print(f"Scaling image from {orig_w}x{orig_h} to {new_w}x{new_h} (scale: {scale:.3f}, mode: {screen_mode})")
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img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
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else:
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if verbose:
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print(f"Using original image size: {orig_w}x{orig_h}")
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arr = img.astype(np.float64)
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h, w = arr.shape
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# Use OpenCV's integral for fast computation
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integral = cv2.integral(arr, sdepth=cv2.CV_64F)[1:,1:]
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integral_sq = cv2.integral(arr**2, sdepth=cv2.CV_64F)[1:,1:]
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def region_sum(ii, x1, y1, x2, y2):
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total = ii[y2, x2]
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if x1 > 0:
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total -= ii[y2, x1-1]
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if y1 > 0:
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total -= ii[y1-1, x2]
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if x1 > 0 and y1 > 0:
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total += ii[y1-1, x1-1]
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return total
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min_var = None
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min_coords = (0, 0)
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area = region_width * region_height
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for y in range(0, h - region_height + 1, stride):
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for x in range(0, w - region_width + 1, stride):
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x1, y1 = x, y
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x2, y2 = x + region_width - 1, y + region_height - 1
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s = region_sum(integral, x1, y1, x2, y2)
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s2 = region_sum(integral_sq, x1, y1, x2, y2)
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mean = s / area
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var = (s2 / area) - (mean ** 2)
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if (min_var is None) or (var < min_var):
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min_var = var
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min_coords = (x, y)
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return min_coords, min_var
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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):
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img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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if img is None:
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raise FileNotFoundError(f"Image not found: {image_path}")
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orig_h, orig_w = img.shape
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scale = 1.0
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if screen_width is not None and screen_height is not None:
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scale_w = screen_width / orig_w
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scale_h = screen_height / orig_h
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if screen_mode == "fill":
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scale = max(scale_w, scale_h)
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else:
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scale = min(scale_w, scale_h)
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new_w = int(orig_w * scale)
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new_h = int(orig_h * scale)
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if verbose:
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print(f"Scaling image from {orig_w}x{orig_h} to {new_w}x{new_h} (scale: {scale:.3f}, mode: {screen_mode})")
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img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
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else:
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if verbose:
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print(f"Using original image size: {orig_w}x{orig_h}")
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arr = img.astype(np.float64)
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h, w = arr.shape
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# Use OpenCV's integral for fast computation
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integral = cv2.integral(arr, sdepth=cv2.CV_64F)[1:,1:]
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integral_sq = cv2.integral(arr**2, sdepth=cv2.CV_64F)[1:,1:]
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def region_sum(ii, x1, y1, x2, y2):
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total = ii[y2, x2]
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if x1 > 0:
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total -= ii[y2, x1-1]
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if y1 > 0:
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total -= ii[y1-1, x2]
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if x1 > 0 and y1 > 0:
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total += ii[y1-1, x1-1]
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return total
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min_size = 10
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max_size = min(h, int(w / aspect_ratio)) if aspect_ratio >= 1.0 else min(int(h * aspect_ratio), w)
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best = None
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best_size = min_size
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while min_size <= max_size:
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mid = (min_size + max_size) // 2
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if aspect_ratio >= 1.0:
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region_h = mid
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region_w = int(mid * aspect_ratio)
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else:
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region_w = mid
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region_h = int(mid / aspect_ratio)
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if region_w > w or region_h > h:
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max_size = mid - 1
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continue
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found = False
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for y in range(0, h - region_h + 1, stride):
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for x in range(0, w - region_w + 1, stride):
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x1, y1 = x, y
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x2, y2 = x + region_w - 1, y + region_h - 1
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s = region_sum(integral, x1, y1, x2, y2)
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s2 = region_sum(integral_sq, x1, y1, x2, y2)
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area = region_w * region_h
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mean = s / area
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var = (s2 / area) - (mean ** 2)
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if var <= threshold:
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found = True
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best = (x, y, region_w, region_h, var)
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break
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if found:
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break
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if found:
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best_size = mid
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min_size = mid + 1
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else:
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max_size = mid - 1
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if best:
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x, y, region_w, region_h, var = best
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center_x = x + region_w // 2
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center_y = y + region_h // 2
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return (center_x, center_y), (region_w, region_h), var
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else:
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return None, (0, 0), None
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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"):
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img = cv2.imread(image_path)
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if img is None:
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raise FileNotFoundError(f"Image not found: {image_path}")
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orig_h, orig_w = img.shape[:2]
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if screen_width is not None and screen_height is not None:
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scale_w = screen_width / orig_w
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scale_h = screen_height / orig_h
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if screen_mode == "fill":
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scale = max(scale_w, scale_h)
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else:
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scale = min(scale_w, scale_h)
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new_w = int(orig_w * scale)
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new_h = int(orig_h * scale)
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img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
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x, y = coords
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cv2.rectangle(img, (x, y), (x+region_width-1, y+region_height-1), (0,0,255), 3)
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cv2.imwrite(output_path, img)
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print(f"Saved output image with rectangle at {output_path}")
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def draw_largest_region(image_path, center, size, output_path='output.png', screen_width=None, screen_height=None, screen_mode="fill"):
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img = cv2.imread(image_path)
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if img is None:
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raise FileNotFoundError(f"Image not found: {image_path}")
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orig_h, orig_w = img.shape[:2]
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if screen_width is not None and screen_height is not None:
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scale_w = screen_width / orig_w
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scale_h = screen_height / orig_h
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if screen_mode == "fill":
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scale = max(scale_w, scale_h)
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else:
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scale = min(scale_w, scale_h)
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new_w = int(orig_w * scale)
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new_h = int(orig_h * scale)
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img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
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cx, cy = center
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region_w, region_h = size
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x1 = cx - region_w // 2
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y1 = cy - region_h // 2
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x2 = cx + region_w // 2 - 1
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y2 = cy + region_h // 2 - 1
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cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,0), 3)
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cv2.imwrite(output_path, img)
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print(f"Saved output image with largest region at {output_path}")
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def main():
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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.")
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parser.add_argument("image_path", help="Path to the input image")
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parser.add_argument("--width", type=int, default=500, help="Region width")
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parser.add_argument("--height", type=int, default=250, help="Region height")
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parser.add_argument("-v", "--visual-output", action="store_true", help="Output image with rectangle")
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parser.add_argument("--screen-width", type=int, default=1920, help="Screen width for wallpaper scaling")
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parser.add_argument("--screen-height", type=int, default=1080, help="Screen height for wallpaper scaling")
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parser.add_argument("--stride", type=int, default=4, help="Step size for sliding window (higher is faster, less precise)")
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parser.add_argument("--screen-mode", choices=["fill", "fit"], default="fill", help="Wallpaper scaling mode: 'fill' (default) or 'fit'")
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parser.add_argument("--verbose", action="store_true", help="Print verbose output")
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parser.add_argument("-l", "--largest-region", action="store_true", help="Find the largest region under the variance threshold and output its center")
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parser.add_argument("-t", "--variance-threshold", type=float, default=1000.0, help="Variance threshold for largest region mode")
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parser.add_argument("--aspect-ratio", type=float, default=1.0, help="Aspect ratio (width/height) for largest region mode")
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args = parser.parse_args()
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if args.largest_region:
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center, size, var = find_largest_region(
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args.image_path,
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screen_width=args.screen_width,
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screen_height=args.screen_height,
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verbose=args.verbose,
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stride=args.stride,
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screen_mode=args.screen_mode,
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threshold=args.variance_threshold,
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aspect_ratio=args.aspect_ratio
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)
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if center:
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if args.visual_output:
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draw_largest_region(args.image_path, center, size, screen_width=args.screen_width, screen_height=args.screen_height, screen_mode=args.screen_mode)
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# Output JSON
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print(json.dumps({
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"center_x": center[0],
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"center_y": center[1],
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"width": size[0],
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"height": size[1],
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"variance": var
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}))
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else:
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print(json.dumps({"error": "No region found under the threshold."}))
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return
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coords, variance = find_least_busy_region(
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args.image_path,
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region_width=args.width,
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region_height=args.height,
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screen_width=args.screen_width,
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screen_height=args.screen_height,
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verbose=args.verbose,
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stride=args.stride,
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screen_mode=args.screen_mode
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)
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if args.visual_output:
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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)
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# Output JSON with center point
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center_x = coords[0] + args.width // 2
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center_y = coords[1] + args.height // 2
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print(json.dumps({
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"center_x": center_x,
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"center_y": center_y,
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"width": args.width,
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"height": args.height,
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"variance": variance
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}))
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if __name__ == "__main__":
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main()
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