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| #!/usr/bin/env python3 | |
| """ | |
| Developed by Nikhil Nageshwar Inturi | |
| This module provides PlotGenerator to process all masks in a directory: | |
| - For each mask, find its image by matching name stem, then output: | |
| 1) a binary mask PNG, | |
| 2) an overlay PNG with colored mask + boundaries. | |
| """ | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from PIL import Image | |
| from skimage.segmentation import find_boundaries | |
| from pathlib import Path | |
| import logging | |
| class PlotGenerator: | |
| """ | |
| Process every .npy mask in mask_dir and generate | |
| corresponding plots using images from image_dir. | |
| """ | |
| def __init__( | |
| self, | |
| image_dir: Path, | |
| mask_dir: Path, | |
| output_dir: Path, | |
| overlay_color: tuple[int,int,int] = (238,144,144), | |
| boundary_color: tuple[int,int,int] = (100,100,255), | |
| alpha: float = 0.5 | |
| ) -> None: | |
| self.image_dir = Path(image_dir) | |
| self.mask_dir = Path(mask_dir) | |
| self.output_dir = Path(output_dir) | |
| self.overlay_color = np.array(overlay_color, dtype=np.uint8) | |
| self.boundary_color = np.array(boundary_color, dtype=np.uint8) | |
| self.alpha = alpha | |
| self.logger = logging.getLogger(self.__class__.__name__) | |
| self.output_dir.mkdir(parents=True, exist_ok=True) | |
| def run(self) -> None: | |
| mask_paths = list(self.mask_dir.glob("*.npy")) | |
| if not mask_paths: | |
| self.logger.warning(f"No .npy masks found in {self.mask_dir}") | |
| return | |
| for mask_path in mask_paths: | |
| stem = mask_path.stem | |
| img_candidates = list(self.image_dir.glob(f"{stem}*.png")) | |
| if not img_candidates: | |
| self.logger.warning(f"No image found for mask '{stem}'") | |
| continue | |
| image_path = img_candidates[0] | |
| img = np.array(Image.open(image_path).convert("RGB")) | |
| mask = np.load(mask_path) | |
| # binary mask plot | |
| binary = (mask > 0).astype(np.uint8) | |
| plt.figure(figsize=(10,10)) | |
| plt.imshow(binary, cmap='gray') | |
| plt.axis('off') | |
| plt.title(f"{stem} - Binary Mask") | |
| out_gray = self.output_dir / f"{stem}_binary.png" | |
| plt.savefig(out_gray, bbox_inches='tight', dpi=300) | |
| plt.close() | |
| self.logger.info(f"Saved binary mask plot: {out_gray.name}") | |
| # overlay with boundaries | |
| overlay = img.copy() | |
| mask_bool = mask > 0 | |
| overlay[mask_bool] = ( | |
| (1 - self.alpha) * img[mask_bool] + self.alpha * self.overlay_color | |
| ).astype(np.uint8) | |
| boundaries = find_boundaries(mask_bool, mode='outer') | |
| overlay[boundaries] = self.boundary_color | |
| plt.figure(figsize=(10,10)) | |
| plt.imshow(overlay) | |
| plt.axis('off') | |
| plt.title(f"{stem} - Mask Overlay") | |
| out_overlay = self.output_dir / f"{stem}_overlay.png" | |
| plt.savefig(out_overlay, bbox_inches='tight', dpi=300) | |
| plt.close() | |
| self.logger.info(f"Saved overlay plot: {out_overlay.name}") | |
| # # main.py snippet (to run plots for all masks) | |
| # from utils.generate_plots import PlotGenerator | |
| # plotter = PlotGenerator( | |
| # image_dir=PNG_IMAGES_DIR, | |
| # mask_dir=STITCHED_MASKS_DIR, | |
| # output_dir=OUTPUT_DIR, | |
| # overlay_color=(238,144,144), | |
| # boundary_color=(100,100,255), | |
| # alpha=0.5 | |
| # ) | |
| # plotter.run() | |
| # import matplotlib.pyplot as plt | |
| # import numpy as np | |
| # # Create a binary mask (optional: if combined_mask contains labels like 1,2,3...) | |
| # binary_mask = (combined_mask > 0).astype(np.uint8) | |
| # plt.figure(figsize=(10, 10)) | |
| # plt.imshow(binary_mask, cmap='gray') # all non-zero values will be gray | |
| # plt.title('Combined Mask - Single Color') | |
| # plt.axis('off') | |
| # # Save the figure as PNG | |
| # plt.savefig('combined_mask_grey_testing_model.png', bbox_inches='tight', dpi=300) | |
| # # Show the plot | |
| # plt.show() | |
| # from PIL import Image | |
| # import numpy as np | |
| # image = Image.open("jayden_img.ome.png").convert("RGB") # PNG_IMAGES_DIR = CONFIG_DIR / '2_png_images' | |
| # image_np = np.array(image) | |
| # mask = np.load("combined_full_mask_testing_model.npy") # STITCHED_MASKS_DIR = CONFIG_DIR / '5_stitched_masks' | |
| # import numpy as np | |
| # import matplotlib.pyplot as plt | |
| # from PIL import Image | |
| # from skimage.segmentation import find_boundaries | |
| # # Define colors | |
| # overlay_color = np.array([238, 144, 144]) # Light green | |
| # boundary_color = np.array([100, 100, 255]) # Navy blue | |
| # alpha = 0.5 # Transparency for overlay | |
| # # Ensure mask is binary | |
| # mask = (mask > 0).astype(np.uint8) | |
| # # Create a copy for overlay | |
| # overlay = image_np.copy() | |
| # # Apply overlay color where mask is 1 | |
| # overlay[mask == 1] = ((1 - alpha) * image_np[mask == 1] + alpha * overlay_color).astype(np.uint8) | |
| # # --- Add navy blue boundaries --- | |
| # from skimage.segmentation import find_boundaries | |
| # # Find boundaries in the mask | |
| # boundaries = find_boundaries(mask, mode='outer') | |
| # # Draw boundary color | |
| # overlay[boundaries] = boundary_color | |
| # # Show plot | |
| # plt.figure(figsize=(10, 10)) | |
| # plt.imshow(overlay) | |
| # plt.axis("off") | |
| # plt.title("Image with Mask Overlay and Navy Blue Boundary") | |
| # plt.show() | |
| # # Save the image | |
| # output = Image.fromarray(overlay) | |
| # output.save("0_image_with_mask_overlay_with_white_boundary_model.png") | |
| # # output dir where pltos needs to be saved: OUTPUT_DIR = CONFIG_DIR / '6_output_masks' | |