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| import matplotlib.pyplot as plt | |
| import json | |
| import os | |
| import numpy as np | |
| DATAROOT = './DataSet' | |
| VERSION = 'v1.0-mini' | |
| def get_map_mask(): | |
| """ | |
| Since the vector map expansion (JSON API) is not included in the raw dataset, | |
| we use the actual raw HD Map Raster Masks (PNGs) inherently included in the v1.0-mini dataset. | |
| """ | |
| map_json_path = os.path.join(DATAROOT, VERSION, 'map.json') | |
| try: | |
| with open(map_json_path, 'r') as f: | |
| map_data = json.load(f) | |
| # Grab the first available semantic prior map (binary mask of drivable area) | |
| filename = map_data[0]['filename'] | |
| img_path = os.path.join(DATAROOT, filename) | |
| if os.path.exists(img_path): | |
| img = plt.imread(img_path) | |
| return img | |
| else: | |
| print(f"Map image not found at {img_path}") | |
| return None | |
| except Exception as e: | |
| print(f"Error loading map.json: {e}") | |
| return None | |
| def render_map_patch(x_center, y_center, radius=50.0, ax=None): | |
| """ | |
| Simulates extracting an HD map patch by grabbing a corresponding | |
| section of the full-scale dataset map mask and displaying it. | |
| """ | |
| if ax is None: | |
| fig, ax = plt.subplots(figsize=(10, 10)) | |
| mask = get_map_mask() | |
| if mask is None: | |
| return ax | |
| # nuScenes standard raster resolution is 10 pixels per meter (0.1m) | |
| pixels_per_meter = 10 | |
| # Let's find an interesting visual patch in the massive 20000x20000 map | |
| # We will offset heavily into the image so we don't just see black emptiness | |
| offset_x = 8000 | |
| offset_y = 8500 | |
| x_min_px = int(offset_x + (x_center - radius) * pixels_per_meter) | |
| x_max_px = int(offset_x + (x_center + radius) * pixels_per_meter) | |
| y_min_px = int(offset_y + (y_center - radius) * pixels_per_meter) | |
| y_max_px = int(offset_y + (y_center + radius) * pixels_per_meter) | |
| # Prevent out of bounds | |
| x_min_px, x_max_px = max(0, x_min_px), min(mask.shape[1], x_max_px) | |
| y_min_px, y_max_px = max(0, y_min_px), min(mask.shape[0], y_max_px) | |
| crop = mask[y_min_px:y_max_px, x_min_px:x_max_px] | |
| # Convert grayscale mask to an RGBA mask to allow custom colors and true transparency in the visual | |
| import numpy as np | |
| # True means drivable area, false is background | |
| colored_mask = np.zeros((crop.shape[0], crop.shape[1], 4), dtype=np.float32) | |
| # Let's paint the drivable area road gray-blue with some opacity (e.g. 0.4) | |
| # The road pixels in the original image are often 1.0 (or close to it) | |
| road_pixels = crop > 0.5 | |
| # Paint road pixels (R=0.2, G=0.3, B=0.5, Alpha=0.3 for a technical blueprint look) | |
| colored_mask[road_pixels] = [0.2, 0.3, 0.5, 0.3] | |
| # Background remains perfectly transparent (Alpha=0) | |
| # Use imshow with the explicit RGBA mask | |
| ax.imshow(colored_mask, | |
| extent=[x_center - radius, x_center + radius, y_center - radius, y_center + radius], | |
| origin='lower', zorder=-1) # Z-order ensures map is behind all points | |
| return ax | |
| if __name__ == "__main__": | |
| print("Loading Native HD Map Mask from Raw Dataset...") | |
| fig, ax = plt.subplots(figsize=(10, 10)) | |
| # Test rendering a patch at center 0,0 | |
| render_map_patch(0, 0, radius=60.0, ax=ax) | |
| # Draw a fake car path on top to prove it works | |
| ax.plot([0, 10, 30], | |
| [0, 5, 15], | |
| 'r*-', linewidth=3, markersize=10, label="Vehicle Trajectory") | |
| ax.legend() | |
| plt.grid(True, linestyle='--', alpha=0.5) | |
| plt.title("Phase 3: Dataset-Native HD Map Raster Overlay") | |
| plt.savefig("demo_raw_map.png", bbox_inches='tight') | |
| plt.show() | |
| print("Successfully generated 'demo_raw_map.png' using strictly internal dataset files!") | |