Datasets:
Size:
10K<n<100K
License:
File size: 3,568 Bytes
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from datasets import load_dataset
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
# 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)
}
def maj_vote(img,x,y,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, top_k_disease = 10, semantic_map = semantic_map):
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, 3)
return semantic_id_img
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))
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()
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