Datasets:
Size:
10K<n<100K
License:
| from datasets import load_dataset | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from scipy import stats | |
| # Additional Information for Depth and Camera Parameters | |
| # | |
| # Creating intrinsics for the camera | |
| # fov = 95.452621 # degrees | |
| # fx = (2448 / np.tan((fov*np.pi/180.0)/2.0)) / 2 | |
| # intrinsics = o3d.camera.PinholeCameraIntrinsic(2448,2048,fx,fx,2448/2,2048/2) | |
| # baseline = 3.88112 # cm | |
| # Note: Depth is also in centimeters | |
| # | |
| def maj_vote(img,x,y,semantic_map,n=3): | |
| half = n // 2 | |
| x_min, x_max = max(0, x - half), min(img.shape[1], x + half + 1) | |
| y_min, y_max = max(0, y - half), min(img.shape[0], y + half + 1) | |
| window = img[y_min:y_max, x_min:x_max].flatten() | |
| window = window[window != 255] | |
| if len(window) > 0: | |
| # Perform majority voting | |
| most_common_label = stats.mode(window, keepdims=True)[0][0] | |
| return most_common_label | |
| else: | |
| return semantic_map["background"][0] | |
| def color_to_id(img_semantic, semantic_map, top_k_disease = 10): | |
| semantic_id_img = np.ones(img_semantic.shape) * 255 | |
| disease_counts = [] | |
| # remap rendered color to semantic id | |
| for _, id_value_map in semantic_map.items(): | |
| # track disease pixel counts for top_k_disease filtering | |
| if id_value_map[1] < 60 and id_value_map[1] > 1: | |
| disease_counts.append(np.sum(np.where(img_semantic == id_value_map[1], 1, 0))) | |
| semantic_id_img[img_semantic == id_value_map[1]] = id_value_map[0] | |
| # filter for most common disease labels | |
| for i, item_i in enumerate(np.argsort(disease_counts)[::-1]): | |
| if i >= top_k_disease: | |
| id_value_map = list(semantic_map.items())[item_i][1] | |
| semantic_id_img[img_semantic == id_value_map[1]] = 255 | |
| # Apply majority voting for unlabeled pixels (needed as the rendering process can blend pixels) | |
| unknown_mask = (semantic_id_img == 255) | |
| for y,x in np.argwhere(unknown_mask): | |
| semantic_id_img[y, x] = maj_vote(semantic_id_img, x, y, semantic_map, 3) | |
| return semantic_id_img | |
| if __name__ == "__main__": | |
| # similar to cityscapes for mmsegmentation | |
| # class name, (new_id, img_id) | |
| semantic_map = { | |
| "bacterial_spot": (0, 5), | |
| "early_blight": (1, 10), | |
| "late_blight": (2, 20), | |
| "leaf_mold": (3, 25), | |
| "septoria_leaf_spot": (4,30), | |
| "spider_mites": (5,35), | |
| "target_spot": (6,40), | |
| "mosaic_virus": (7,45), | |
| "yellow_leaf_curl_virus":(8,50), | |
| "healthy_leaf_pv": (9, 15), # plant village healthy leaf | |
| "healthy_leaf_t": (9, 255), # texture leaf (healthy) | |
| "background": (10, 0), | |
| "tomato": (11, 121), | |
| "stem": (12, 111), | |
| "wood_rod": (13, 101), | |
| "red_band": (14, 140), | |
| "yellow_flower": (15, 131) | |
| } | |
| dataset = load_dataset("xingjianli/tomatotest", 'sample',trust_remote_code=True, num_proc=4) | |
| print(dataset["train"][0]) | |
| left_rgb_img = dataset["train"][0]['left_rgb'] | |
| right_rgb_img = dataset["train"][0]['right_rgb'] | |
| left_semantic_img = np.asarray(dataset["train"][0]['left_semantic']) | |
| left_instance_img = np.asarray(dataset["train"][0]['left_instance']) | |
| left_depth_img = np.asarray(dataset["train"][0]['left_depth']) | |
| right_depth_img = np.asarray(dataset["train"][0]['right_depth']) | |
| plt.subplot(231) | |
| plt.imshow(left_rgb_img) | |
| plt.subplot(232) | |
| plt.imshow(right_rgb_img) | |
| plt.subplot(233) | |
| plt.imshow(color_to_id(left_semantic_img, semantic_map)) | |
| plt.subplot(234) | |
| plt.imshow(np.where(left_depth_img>500,0,left_depth_img)) | |
| plt.subplot(235) | |
| plt.imshow(np.where(right_depth_img>500,0,right_depth_img)) | |
| plt.subplot(236) | |
| plt.imshow(left_instance_img) | |
| plt.show() | |