311 lines
13 KiB
Python
311 lines
13 KiB
Python
# --- START OF FILE image_pipeline.py ---
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# image_pipeline.py
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"""
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THIS SOFTWARE IS PROVIDED “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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Handles the image processing pipeline for normal mode operation.
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Reads current image data (SAR normalized, MFD indices) and parameters (LUTs,
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palette, rotation, size) from AppState, performs the necessary processing steps
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(LUT application, colorization, rotation, resizing), and queues the resulting
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images for display by the DisplayManager.
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"""
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# Standard library imports
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import logging
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import queue # For type hinting
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import math
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from typing import Optional
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# Third-party imports
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import numpy as np
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import cv2
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# Local application imports
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from app_state import AppState
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from utils import put_queue
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from image_processing import (
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apply_color_palette, # Keep specific imports needed
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resize_image,
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# normalize_image is used by receiver/test manager, not directly here
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)
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# Forward declaration for type hinting App instance
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from app import App
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class ImagePipeline:
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"""Processes SAR and MFD images based on AppState for display."""
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def __init__(
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self,
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app_state: AppState,
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sar_queue: queue.Queue,
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mfd_queue: queue.Queue,
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app: 'App', # Use forward declaration for App type hint
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):
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"""
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Initializes the ImagePipeline.
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Args:
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app_state (AppState): Reference to the shared application state.
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sar_queue (queue.Queue): Queue for sending processed SAR images for display.
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mfd_queue (queue.Queue): Queue for sending processed MFD images for display.
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app (App): Reference to the main application instance (for put_queue context).
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"""
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self._log_prefix = "[ImagePipeline]"
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logging.debug(f"{self._log_prefix} Initializing...")
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self._app_state: AppState = app_state
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self._sar_queue: queue.Queue = sar_queue
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self._mfd_queue: queue.Queue = mfd_queue
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self._app: 'App' = app # Store app reference
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logging.debug(f"{self._log_prefix} Initialization complete.")
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def process_sar_for_display(self):
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"""
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Processes the current SAR image from AppState (normalization, LUT, palette,
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rotation, resize) and queues it for display.
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Checks shutdown and test mode flags from AppState.
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"""
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log_prefix = f"{self._log_prefix} SAR Process"
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# Check flags first
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if self._app_state.shutting_down:
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logging.debug(f"{log_prefix} Skipping processing: Shutdown detected.")
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return
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if self._app_state.test_mode_active:
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logging.debug(f"{log_prefix} Skipping processing: Test Mode active.")
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return
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# Validate Input Image from state
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current_normalized_sar = self._app_state.current_sar_normalized
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if current_normalized_sar is None or current_normalized_sar.size == 0:
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logging.warning(f"{log_prefix} No normalized SAR image in AppState. Cannot process.")
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return
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# Get parameters from state safely
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try:
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bc_lut = self._app_state.brightness_contrast_lut
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palette = self._app_state.sar_palette
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geo_info = self._app_state.current_sar_geo_info
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target_w = self._app_state.sar_display_width
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target_h = self._app_state.sar_display_height
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except AttributeError as ae:
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logging.error(f"{log_prefix} Missing required attribute in AppState: {ae}. Cannot process.")
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return
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# Validate essential parameters
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if bc_lut is None:
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logging.warning(f"{log_prefix} Brightness/Contrast LUT missing in AppState. Cannot process.")
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return
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if target_w <= 0 or target_h <= 0:
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logging.warning(f"{log_prefix} Invalid target display dimensions ({target_w}x{target_h}). Cannot process.")
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return
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logging.debug(f"{log_prefix} Processing SAR image from AppState...")
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# Work on a copy to avoid modifying state during processing
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# Ensure the input image is uint8 as expected by subsequent steps
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if current_normalized_sar.dtype != np.uint8:
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logging.warning(f"{log_prefix} Input SAR normalized image is not uint8 ({current_normalized_sar.dtype}). Attempting conversion.")
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try:
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base_img = current_normalized_sar.astype(np.uint8)
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except Exception as e:
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logging.error(f"{log_prefix} Failed to convert input SAR to uint8: {e}. Aborting.")
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return
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else:
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base_img = current_normalized_sar.copy()
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try:
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# --- Processing Steps ---
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is_geo_valid = geo_info.get("valid", False) if geo_info else False
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orient_rad = geo_info.get("orientation", 0.0) if is_geo_valid else 0.0
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# 1. Apply B/C LUT (from state)
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logging.debug(f"{log_prefix} Applying B/C LUT...")
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img = cv2.LUT(base_img, bc_lut)
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if self._app_state.shutting_down: return # Check after each step
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# 2. Apply Color Palette (from state)
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if palette != "GRAY":
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logging.debug(f"{log_prefix} Applying color palette '{palette}'...")
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img = apply_color_palette(img, palette) # Use imported function
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if self._app_state.shutting_down: return
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else:
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logging.debug(f"{log_prefix} Skipping color palette (GRAY).")
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# 3. Apply Rotation
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if is_geo_valid and abs(orient_rad) > 1e-4:
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logging.debug(f"{log_prefix} Applying rotation (Angle: {math.degrees(orient_rad):.2f} deg)...")
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img = self._rotate_image(img, orient_rad) # Use helper method
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if img is None or self._app_state.shutting_down: return # Check helper result and shutdown
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else:
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logging.debug(f"{log_prefix} Skipping rotation (Geo invalid or angle near zero).")
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# 4. Resize Image
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logging.debug(f"{log_prefix} Resizing image to {target_w}x{target_h}...")
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img = self._resize_sar_image(img, target_w, target_h) # Use helper method
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if img is None or self._app_state.shutting_down: # Check helper result and shutdown
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return
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logging.debug(f"{log_prefix} Resize complete. Final shape: {img.shape}")
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# 5. Queue Final Image
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# Use put_queue utility, passing the app instance for context
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put_queue(self._sar_queue, img.copy(), "sar", self._app)
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logging.debug(f"{log_prefix} Queued processed SAR image for display.")
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except Exception as e:
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# Log error only if not during shutdown
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if not self._app_state.shutting_down:
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logging.exception(f"{log_prefix} Error during SAR processing pipeline:")
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def _rotate_image(self, img: np.ndarray, angle_rad: float) -> Optional[np.ndarray]:
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"""
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Helper method to rotate an image using OpenCV.
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Args:
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img (np.ndarray): Input image.
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angle_rad (float): Rotation angle in radians.
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Returns:
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Optional[np.ndarray]: The rotated image, or None on critical error.
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"""
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log_prefix = f"{self._log_prefix} SAR Rotate Helper"
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try:
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deg = math.degrees(angle_rad)
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h, w = img.shape[:2]
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center = (w // 2, h // 2)
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# Get rotation matrix
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M = cv2.getRotationMatrix2D(center, deg, 1.0)
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# Determine border color (black for BGR, 0 for grayscale)
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border_color = [0,0,0] if img.ndim == 3 else 0
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# Perform affine warp
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rotated_img = cv2.warpAffine(
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img,
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M,
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(w, h), # Output size same as input
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flags=cv2.INTER_LINEAR, # Linear interpolation
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borderMode=cv2.BORDER_CONSTANT,
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borderValue=border_color # Fill borders with black
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)
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logging.debug(f"{log_prefix} Rotation successful.")
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return rotated_img
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except Exception as e:
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# Log error and return None to indicate failure
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logging.exception(f"{log_prefix} Rotation warpAffine error:")
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return None
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def _resize_sar_image(self, img: np.ndarray, target_w: int, target_h: int) -> Optional[np.ndarray]:
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"""
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Helper method to resize SAR image using the utility function.
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Args:
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img (np.ndarray): Input image.
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target_w (int): Target width.
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target_h (int): Target height.
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Returns:
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Optional[np.ndarray]: The resized image, or None on error.
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"""
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log_prefix = f"{self._log_prefix} SAR Resize Helper"
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# Basic validation
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if img is None or target_w <= 0 or target_h <=0:
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logging.error(f"{log_prefix} Invalid input for resize (Image None or invalid dims {target_w}x{target_h}).")
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return None
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# Check if resize is actually needed
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if img.shape[1] == target_w and img.shape[0] == target_h:
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logging.debug(f"{log_prefix} Image already target size. Skipping resize.")
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return img
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# Perform resize using the function from image_processing
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resized_img = resize_image(img, target_w, target_h) # This function handles logging
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if resized_img is None:
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logging.error(f"{log_prefix} SAR Resize failed (resize_image returned None).")
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return None # Propagate None on failure
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else:
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logging.debug(f"{log_prefix} Resize successful.")
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return resized_img
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def process_mfd_for_display(self):
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"""
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Applies the MFD LUT from AppState to the indices in AppState and queues the result.
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Checks shutdown and test mode flags from AppState.
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"""
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log_prefix = f"{self._log_prefix} MFD Process"
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# Check flags first
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if self._app_state.shutting_down:
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logging.debug(f"{log_prefix} Skipping processing: Shutdown detected.")
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return
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if self._app_state.test_mode_active:
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logging.debug(f"{log_prefix} Skipping processing: Test Mode active.")
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return
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# Validate Input Indices from state
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current_mfd_indices = self._app_state.current_mfd_indices
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if current_mfd_indices is None or current_mfd_indices.size == 0:
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logging.warning(f"{log_prefix} No MFD indices in AppState. Cannot process.")
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return
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# Get MFD LUT from state
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mfd_lut = self._app_state.mfd_lut
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if mfd_lut is None:
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logging.warning(f"{log_prefix} MFD LUT missing in AppState. Cannot process.")
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return
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logging.debug(f"{log_prefix} Processing MFD indices from AppState...")
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try:
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# --- Check shutdown before potentially heavy LUT application ---
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if self._app_state.shutting_down:
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logging.debug(f"{log_prefix} Shutdown detected before LUT application.")
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return
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# --- Apply MFD LUT (from state) ---
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logging.debug(f"{log_prefix} Applying MFD LUT (shape {mfd_lut.shape}) to indices (shape {current_mfd_indices.shape})...")
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mfd_bgr = mfd_lut[current_mfd_indices]
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logging.debug(f"{log_prefix} MFD LUT applied. Result shape: {mfd_bgr.shape}.")
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# --- Check shutdown again before queueing ---
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if self._app_state.shutting_down:
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logging.debug(f"{log_prefix} Shutdown detected after LUT application.")
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return
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# --- Queue Result ---
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if mfd_bgr is not None:
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# Use put_queue utility, passing the app instance for context
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put_queue(self._mfd_queue, mfd_bgr.copy(), "mfd", self._app)
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logging.debug(f"{log_prefix} Queued processed MFD image for display.")
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else:
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# This case should be rare if LUT is always valid and indices are valid
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logging.error(f"{log_prefix} MFD BGR image is None after LUT application (unexpected).")
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except IndexError as e:
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# Handle index errors (e.g., index value > LUT size - 1)
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min_idx, max_idx = "-","-"
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try: # Safely try to get min/max for logging
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if current_mfd_indices is not None:
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min_idx = np.min(current_mfd_indices)
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max_idx = np.max(current_mfd_indices)
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except ValueError: pass # Ignore if array is empty etc.
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# Log error only if not shutting down
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if not self._app_state.shutting_down:
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logging.error(
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f"{log_prefix} MFD LUT IndexError: {e}. "
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f"Indices range maybe ({min_idx},{max_idx}). "
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f"LUT shape {mfd_lut.shape}"
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)
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except Exception as e:
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# Log other errors only if not shutting down
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if not self._app_state.shutting_down:
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logging.exception(f"{log_prefix} Error applying LUT to MFD indices:")
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# --- END OF FILE image_pipeline.py --- |