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memory fixes
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dgtlmoon
@@ -14,6 +14,7 @@ from changedetectionio import strtobool
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from changedetectionio.processors import difference_detection_processor, SCREENSHOT_FORMAT_PNG
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from changedetectionio.processors.exceptions import ProcessorException
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from . import DEFAULT_COMPARISON_METHOD, DEFAULT_COMPARISON_THRESHOLD_OPENCV, DEFAULT_COMPARISON_THRESHOLD_PIXELMATCH, CROPPED_IMAGE_TEMPLATE_FILENAME
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from .util import find_region_with_template_matching_isolated, regenerate_template_isolated, crop_image_isolated
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name = 'Visual / Image screenshot change detection'
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description = 'Compares screenshots using fast algorithms (OpenCV or pixelmatch), 10-100x faster than SSIM'
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@@ -162,8 +163,20 @@ class perform_site_check(difference_detection_processor):
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if crop_region:
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try:
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cropped_current_img = current_img.crop(crop_region)
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logger.debug(f"Cropped screenshot to {cropped_current_img.size} (region: {crop_region}) for comparison")
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# Use subprocess isolation to crop image - ensures complete memory independence
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import io
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current_img_bytes = io.BytesIO()
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current_img.save(current_img_bytes, format='PNG')
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current_img_bytes_data = current_img_bytes.getvalue()
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cropped_bytes = crop_image_isolated(current_img_bytes_data, crop_region)
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if cropped_bytes:
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cropped_current_img = Image.open(io.BytesIO(cropped_bytes))
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cropped_current_img.load()
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logger.debug(f"Cropped screenshot to {cropped_current_img.size} (region: {crop_region}) via subprocess")
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else:
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logger.error("Subprocess crop failed")
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crop_region = None
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except Exception as e:
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logger.error(f"Failed to crop screenshot: {e}")
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crop_region = None # Disable cropping on error
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@@ -214,13 +227,23 @@ class perform_site_check(difference_detection_processor):
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template_path = os.path.join(watch.watch_data_dir, CROPPED_IMAGE_TEMPLATE_FILENAME)
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if not os.path.isfile(template_path):
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logger.info("Template file missing, regenerating from previous snapshot")
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self._regenerate_template_from_snapshot(
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previous_img, watch, original_crop_region
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# Use subprocess isolation for template regeneration
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import io
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prev_img_bytes = io.BytesIO()
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previous_img.save(prev_img_bytes, format='PNG')
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regenerate_template_isolated(
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prev_img_bytes.getvalue(), original_crop_region, template_path
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)
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logger.debug("Template matching enabled - searching for region movement")
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new_crop_region = self._find_region_with_template_matching(
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current_img, watch, original_crop_region, search_tolerance=0.2
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# Use subprocess isolation for template matching (most memory-intensive operation)
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import io
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curr_img_bytes = io.BytesIO()
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current_img.save(curr_img_bytes, format='PNG')
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with open(template_path, 'rb') as f:
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template_bytes = f.read()
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new_crop_region = find_region_with_template_matching_isolated(
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curr_img_bytes.getvalue(), template_bytes, original_crop_region, search_tolerance=0.2
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)
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if new_crop_region:
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@@ -228,10 +251,17 @@ class perform_site_check(difference_detection_processor):
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crop_region = new_crop_region
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logger.info(f"Template matching: Region moved from {old_region} to {new_crop_region}")
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# Update cropped image with new region
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# Update cropped image with new region using subprocess isolation
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if cropped_current_img:
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cropped_current_img.close()
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cropped_current_img = current_img.crop(crop_region)
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del cropped_current_img
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# Already have curr_img_bytes from template matching above
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cropped_bytes = crop_image_isolated(curr_img_bytes.getvalue(), crop_region)
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if cropped_bytes:
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cropped_current_img = Image.open(io.BytesIO(cropped_bytes))
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cropped_current_img.load()
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else:
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logger.error("Subprocess crop failed after template matching")
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else:
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logger.warning("Template matching: Could not find region, using original position")
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@@ -242,11 +272,17 @@ class perform_site_check(difference_detection_processor):
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cropped_previous_img = None
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if crop_region:
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try:
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cropped_previous_img = previous_img.crop(crop_region)
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# Crop and force independent buffer to break reference to parent image
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cropped_previous_img = previous_img.crop(crop_region).copy()
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cropped_previous_img.load() # Force load into memory
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logger.debug(f"Cropped previous screenshot to {cropped_previous_img.size}")
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except Exception as e:
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logger.warning(f"Failed to crop previous screenshot: {e}")
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# Force garbage collection after cropping to free old image buffers
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import gc
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gc.collect()
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except Exception as e:
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logger.warning(f"Failed to load previous screenshot for comparison: {e}")
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# Clean up current images before returning
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@@ -501,191 +537,3 @@ class perform_site_check(difference_detection_processor):
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gc.collect()
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return changed_detected, change_percentage
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def _regenerate_template_from_snapshot(self, snapshot_img, watch, bbox):
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"""
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Regenerate template file from a snapshot (typically after 'clear data').
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When user clears watch data, the template file is deleted but config remains.
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This extracts the region from the previous/baseline snapshot and saves it
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as the template so tracking can continue.
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Args:
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snapshot_img: PIL Image to extract template from (usually previous_img)
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watch: Watch object (to access data directory)
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bbox: (left, top, right, bottom) bounding box coordinates
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"""
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try:
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left, top, right, bottom = bbox
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width = right - left
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height = bottom - top
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# Ensure watch data directory exists
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watch.ensure_data_dir_exists()
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# Crop the template region
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template = snapshot_img.crop(bbox)
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# Save as PNG (lossless, no compression artifacts)
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template_path = os.path.join(watch.watch_data_dir, CROPPED_IMAGE_TEMPLATE_FILENAME)
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template.save(template_path, format='PNG', optimize=True)
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logger.info(f"Regenerated template: {template_path} ({width}x{height}px)")
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template.close()
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except Exception as e:
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logger.error(f"Failed to regenerate template: {e}")
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def _find_region_with_template_matching(self, current_img, watch, original_bbox, search_tolerance=0.2):
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"""
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Use OpenCV template matching to find where content moved on the page.
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This handles cases where page layout shifts push content to different
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pixel coordinates, but the visual content remains the same.
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Args:
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current_img: PIL Image of current screenshot
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watch: Watch object (to access template file)
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original_bbox: (left, top, right, bottom) tuple of original region
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search_tolerance: How far to search (0.2 = ±20% of region size)
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Returns:
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tuple: New (left, top, right, bottom) region, or None if not found
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"""
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import cv2
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import numpy as np
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import gc
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template_img = None
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current_array = None
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template_array = None
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current_gray = None
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template_gray = None
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search_region = None
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result = None
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try:
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# Load template from watch data directory
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template_path = os.path.join(watch.watch_data_dir, CROPPED_IMAGE_TEMPLATE_FILENAME)
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if not os.path.isfile(template_path):
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logger.warning(f"Template file not found: {template_path}")
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return None
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from PIL import Image
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template_img = Image.open(template_path)
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template_img.load() # Force load image data into memory
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# Calculate search region dimensions first
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left, top, right, bottom = original_bbox
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width = right - left
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height = bottom - top
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margin_x = int(width * search_tolerance)
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margin_y = int(height * search_tolerance)
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# Expand search area
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search_left = max(0, left - margin_x)
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search_top = max(0, top - margin_y)
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search_right = min(current_img.width, right + margin_x)
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search_bottom = min(current_img.height, bottom + margin_y)
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# Convert only the search region of current image to numpy array (not the whole image!)
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current_img_cropped = current_img.crop((search_left, search_top, search_right, search_bottom))
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current_array = np.array(current_img_cropped)
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current_img_cropped.close() # Close immediately after conversion
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del current_img_cropped
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# Convert template to numpy array
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template_array = np.array(template_img)
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# Close template image immediately after conversion
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template_img.close()
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template_img = None
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# Convert to grayscale for matching
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if len(current_array.shape) == 3:
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current_gray = cv2.cvtColor(current_array, cv2.COLOR_RGB2GRAY)
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del current_array # Delete immediately
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current_array = None
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else:
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current_gray = current_array
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current_array = None # Just transfer reference
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if len(template_array.shape) == 3:
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template_gray = cv2.cvtColor(template_array, cv2.COLOR_RGB2GRAY)
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del template_array # Delete immediately
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template_array = None
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else:
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template_gray = template_array
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template_array = None # Just transfer reference
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logger.debug(f"Searching for template in region: ({search_left}, {search_top}) to ({search_right}, {search_bottom})")
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# Perform template matching (search_region is now just current_gray since we pre-cropped)
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result = cv2.matchTemplate(current_gray, template_gray, cv2.TM_CCOEFF_NORMED)
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# Delete arrays immediately after matchTemplate
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del current_gray
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current_gray = None
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del template_gray
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template_gray = None
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min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
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# Delete result array immediately after getting values
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del result
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result = None
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# Force garbage collection now that large arrays are freed
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gc.collect()
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logger.debug(f"Template matching confidence: {max_val:.2%}")
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# Check if match is good enough (80% confidence threshold)
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if max_val >= 0.8:
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# Calculate new bounding box in original image coordinates
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match_x = search_left + max_loc[0]
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match_y = search_top + max_loc[1]
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new_bbox = (match_x, match_y, match_x + width, match_y + height)
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# Calculate movement distance
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move_x = abs(match_x - left)
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move_y = abs(match_y - top)
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logger.info(f"Template found at ({match_x}, {match_y}), "
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f"moved {move_x}px horizontally, {move_y}px vertically, "
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f"confidence: {max_val:.2%}")
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return new_bbox
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else:
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logger.warning(f"Template match confidence too low: {max_val:.2%} (need 80%)")
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return None
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except Exception as e:
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logger.error(f"Template matching error: {e}")
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return None
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finally:
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# Cleanup any remaining objects (in case of early return or exception)
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if template_img is not None:
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try:
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template_img.close()
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except:
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pass
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if result is not None:
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del result
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if search_region is not None:
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del search_region
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if template_gray is not None:
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del template_gray
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if template_array is not None:
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del template_array
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if current_gray is not None:
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del current_gray
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if current_array is not None:
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del current_array
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# Force garbage collection to immediately release memory
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gc.collect()
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