8f42fc953d
This is a projection, not a development branch. The tree above was constructed from the internal source named below under a manifest that decides which paths may leave, then scanned as a whole tree rather than as a series of patches, and only then published. Public history starts here because the history before it was not admissible, and neither was the tree. What used to stand in this repository included a rescue copy of another machine, a directory of phone handoffs, deployment wired to one house, and a submodule pointing at a forge no stranger can reach. None of that was ever the product. It stays in the private forge, which is allowed to hold the whole working organism, and this is what was deliberately sent out instead. Three mechanisms produced this tree, in decreasing order of trust. A top-level path the manifest does not name never arrives at all, which is the one that catches directories nobody has thought of yet. Named internal files inside admitted roots are dropped. A short, reviewed table replaces deployment defaults that a public build must not carry -- an endpoint aimed at one LAN, a VPN profile belonging to one phone, packaging built from one checkout path. Everything after this commit is an ordinary publication with the same three trailers, so a force push stops being routine and starts meaning that something deliberate happened. The trailers bind the projection to its source without pretending the public SHA is the private one: same lineage, different tree, and the record says so. Source-Sha: 8f27b1e76a8fef560a336aba18e6990713ff1047 Policy-Sha: 6b261d2f3e6e1fb19874846ba4bb1dfe15565d25b8618c1c1afba0419c101d27 Tree-Digest: 18ec3563c5e5ef9a414993a9f6734b251ff9ed3cd56eebdd6cac01e45c6e3067
399 lines
17 KiB
Python
Executable file
399 lines
17 KiB
Python
Executable file
#!/usr/bin/env python3
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# Disclaimer: This script was ai-generated and went through minimal revision.
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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 center_crop(img, target_w, target_h):
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h, w = img.shape[:2]
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if w == target_w and h == target_h:
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return img
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x1 = max(0, (w - target_w) // 2)
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y1 = max(0, (h - target_h) // 2)
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x2 = x1 + target_w
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y2 = y1 + target_h
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return img[y1:y2, x1:x2]
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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", horizontal_padding=50, vertical_padding=50, busiest=False):
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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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img = center_crop(img, screen_width, screen_height)
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if verbose:
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print(f"Cropped image to {screen_width}x{screen_height}")
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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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# Validate & adjust stride
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stride = max(1, int(stride) if stride else 1)
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# Adjust region size if it does not fit given padding
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if horizontal_padding * 2 >= w or vertical_padding * 2 >= h:
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# Reduce padding to fit at least a 1x1 region
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horizontal_padding = max(0, min(horizontal_padding, (w - 1) // 2))
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vertical_padding = max(0, min(vertical_padding, (h - 1) // 2))
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max_region_w = w - 2 * horizontal_padding
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max_region_h = h - 2 * vertical_padding
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if max_region_w <= 0 or max_region_h <= 0:
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raise ValueError("Image too small for the specified padding.")
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if region_width > max_region_w:
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if verbose:
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print(f"Requested region_width {region_width} too large; clamping to {max_region_w}")
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region_width = max_region_w
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if region_height > max_region_h:
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if verbose:
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print(f"Requested region_height {region_height} too large; clamping to {max_region_h}")
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region_height = max_region_h
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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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# Assume bounds have been checked before calling
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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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max_var = None
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min_coords = (horizontal_padding, vertical_padding)
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max_coords = (horizontal_padding, vertical_padding)
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area = region_width * region_height
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x_start = horizontal_padding
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y_start = vertical_padding
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x_end = w - region_width - horizontal_padding + 1
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y_end = h - region_height - vertical_padding + 1
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if x_end < x_start:
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x_end = x_start
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if y_end < y_start:
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y_end = y_start
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for y in range(y_start, y_end + 1, stride):
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for x in range(x_start, x_end + 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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if x2 >= w or y2 >= h:
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continue # Skip out-of-bounds window
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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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if (max_var is None) or (var > max_var):
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max_var = var
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max_coords = (x, y)
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if busiest:
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return max_coords, max_var
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else:
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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, horizontal_padding=50, vertical_padding=50):
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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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# ...existing scaling logic...
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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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img = center_crop(img, screen_width, screen_height)
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if verbose:
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print(f"Cropped image to {screen_width}x{screen_height}")
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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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stride = max(1, int(stride) if stride else 1)
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threshold = max(0.0, float(threshold))
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# Adjust padding if image too small
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if horizontal_padding * 2 >= w or vertical_padding * 2 >= h:
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horizontal_padding = max(0, min(horizontal_padding, (w - 1) // 2))
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vertical_padding = max(0, min(vertical_padding, (h - 1) // 2))
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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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# Determine maximum feasible size respecting padding
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effective_w = w - 2 * horizontal_padding
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effective_h = h - 2 * vertical_padding
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if effective_w <= 0 or effective_h <= 0:
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return None, (0, 0), None
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# Largest square-ish dimension given aspect ratio and effective space
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if aspect_ratio >= 1.0:
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max_size = min(effective_h, int(effective_w / aspect_ratio))
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else:
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max_size = min(int(effective_h * aspect_ratio), effective_w)
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if max_size < min_size:
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min_size = 1
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max_size = max(1, max_size)
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best = None
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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(round(mid * aspect_ratio))
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else:
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region_w = mid
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region_h = int(round(mid / aspect_ratio if aspect_ratio != 0 else mid))
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if region_w <= 0 or region_h <= 0:
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break
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if region_w > effective_w or region_h > effective_h:
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max_size = mid - 1
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continue
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found = False
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x_start = horizontal_padding
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y_start = vertical_padding
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x_end = w - region_w - horizontal_padding
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y_end = h - region_h - vertical_padding
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for y in range(y_start, y_end + 1, stride):
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for x in range(x_start, x_end + 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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if x2 >= w or y2 >= h:
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continue
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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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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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img = center_crop(img, screen_width, screen_height)
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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 removed for quieter operation
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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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img = center_crop(img, screen_width, screen_height)
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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 removed for quieter operation
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def get_dominant_color(image_path, x, y, w, h, 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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img = center_crop(img, screen_width, screen_height)
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# Ensure region is within bounds
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x = max(0, x)
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y = max(0, y)
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w = max(1, min(w, img.shape[1] - x))
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h = max(1, min(h, img.shape[0] - y))
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region = img[y:y+h, x:x+w]
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if region.size == 0 or region.shape[0] == 0 or region.shape[1] == 0:
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return [0, 0, 0]
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region = region.reshape((-1, 3))
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# Filter out black pixels (optional, improves accuracy for some images)
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non_black = region[np.any(region > 10, axis=1)]
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if non_black.shape[0] == 0:
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non_black = region
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region = np.float32(non_black)
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if region.shape[0] < 3:
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return [int(x) for x in np.mean(region, axis=0)]
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# K-means to find dominant color
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
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K = min(3, region.shape[0])
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_, labels, centers = cv2.kmeans(region, K, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
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counts = np.bincount(labels.flatten())
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dominant = centers[np.argmax(counts)]
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# Reverse from BGR to RGB
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return [int(x) for x in reversed(dominant)]
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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=300, help="Region width")
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parser.add_argument("--height", type=int, default=200, 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=10, 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=1.78, help="Aspect ratio (width/height) for largest region mode")
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parser.add_argument("--horizontal-padding", "-hp", type=int, default=50, help="Minimum horizontal distance from region to image edge")
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parser.add_argument("--vertical-padding", "-vp", type=int, default=50, help="Minimum vertical distance from region to image edge")
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parser.add_argument("--busiest", action="store_true", help="Find the busiest region instead of the least busy")
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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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horizontal_padding=args.horizontal_padding,
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vertical_padding=args.vertical_padding
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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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# Extract dominant color
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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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dominant_color = get_dominant_color(
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args.image_path, x1, y1, region_w, region_h,
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screen_width=args.screen_width, screen_height=args.screen_height, screen_mode=args.screen_mode
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)
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dominant_color_hex = '#{:02x}{:02x}{:02x}'.format(*dominant_color)
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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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"dominant_color": dominant_color_hex
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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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horizontal_padding=args.horizontal_padding,
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vertical_padding=args.vertical_padding,
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busiest=args.busiest
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|
)
|
|
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()
|
|
|