| import numpy as np |
| import random |
| import matplotlib.pyplot as plt |
| import matplotlib.patches as patches |
| from src.dataset import VOC_CLASSES |
|
|
| def plot_image_with_annotations(img_array, annotation_dict, img_width=None, img_height=None): |
| fig, ax = plt.subplots(1) |
| ax.imshow(img_array) |
|
|
| if img_width is None: |
| img_width = annotation_dict["size"]["width"] |
| if img_height is None: |
| img_height = annotation_dict["size"]["height"] |
|
|
| for obj in annotation_dict["objects"]: |
| x_min = obj["bndbox"]["x_min"]*img_width |
| y_min = obj["bndbox"]["y_min"]*img_height |
| x_max = obj["bndbox"]["x_max"]*img_width |
| y_max = obj["bndbox"]["y_max"]*img_height |
|
|
| width = x_max - x_min |
| height = y_max - y_min |
|
|
| rect = patches.Rectangle((x_min, y_min), width, height, linewidth=2, edgecolor='r', facecolor='none') |
| ax.add_patch(rect) |
|
|
| ax.text(x_min, y_min - 5, obj["name"], color='r', fontsize=12, weight='bold') |
|
|
| plt.show() |
|
|
|
|
| def display_random_images_with_annotations(dataset, |
| num_images=5, |
| display_shape : bool = True, |
| seed : int = None): |
| """ |
| Displays a random selection of images from the dataset along with their annotations. |
| |
| Args: |
| dataset: A dataset object that provides access to images and their annotations. |
| num_images: The number of random images to display. |
| display_shape: If True, displays the shape of each image. |
| seed: Random seed for reproducibility. |
| """ |
|
|
| |
| if seed is not None: |
| random.seed(seed) |
|
|
| if num_images > 10: |
| num_images = 10 |
| print("Warning: Displaying more than 10 images may clutter the output. Displaying only 10 images.") |
|
|
| random_indices = random.sample(range(len(dataset)), num_images) |
|
|
| for idx in random_indices: |
| img_tensor, annotation_dict = dataset[idx] |
| |
| plot_image_with_annotations(img_tensor.permute(1, 2, 0).numpy(), annotation_dict, img_width=img_tensor.shape[2], img_height=img_tensor.shape[1]) |
|
|
|
|
|
|
| def display_random_batch_images(batch_images, batch_boxes, batch_labels=None, num_images=4): |
| fig, axes = plt.subplots(1, num_images, figsize=(15, 5)) |
| for i in range(num_images): |
| idx = random.randint(0, len(batch_images) - 1) |
| img = batch_images[idx].permute(1, 2, 0).cpu().numpy() |
| boxes = batch_boxes[idx].cpu().numpy() |
|
|
| axes[i].imshow(img) |
| axes[i].set_title(f"Image {idx}") |
| axes[i].axis('off') |
|
|
| for j, box in enumerate(boxes): |
| x1, y1, x2, y2 = box |
| rect = plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, color='red', linewidth=2) |
| axes[i].add_patch(rect) |
|
|
| if batch_labels is not None: |
| labels = batch_labels[idx].cpu().numpy() |
|
|
| axes[i].text(x1, y1 - 5, VOC_CLASSES[labels[j]], color='r', fontsize=12, weight='bold') |
|
|
| plt.tight_layout() |
| plt.show() |
|
|
