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Add carrot_weeds_uk and onions_weeds_uk datasets #14

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Add carrot_weeds_uk and onions_weeds_uk datasets #14

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42 changes: 42 additions & 0 deletions agml/_assets/public_datasources.json
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Expand Up @@ -919,5 +919,47 @@
0.22038120031356812
]
}
},
"carrot_weeds_uk": {
"ml_task": "semantic_segmentation",
"ag_task": "weed_segmentation",
"location": {
"continent": "europe",
"country": "united kingdom"
},
"sensor_modality": "multispectral",
"real_synthetic": "real",
"platform": "ground_mobile",
"input_data_format": "png",
"annotation_format": "image",
"n_images": "20",
"docs_url": "https://lcas.lincoln.ac.uk/wp/research/data-sets-software/crop-vs-weed-discrimination-dataset/",
"classes": {
"0": "carrot",
"1": "weeds",
"2": "non-vegetation"
},
"external_image_sources": ["ndvi-images", "nir-images"]
},
"onions_weeds_uk": {
"ml_task": "semantic_segmentation",
"ag_task": "weed_segmentation",
"location": {
"continent": "europe",
"country": "united kingdom"
},
"sensor_modality": "multispectral",
"real_synthetic": "real",
"platform": "ground_mobile",
"input_data_format": "png",
"annotation_format": "image",
"n_images": "20",
"docs_url": "https://lcas.lincoln.ac.uk/wp/research/data-sets-software/crop-vs-weed-discrimination-dataset/",
"classes": {
"0": "onion",
"1": "weeds",
"2": "non-vegetation"
},
"external_image_sources": ["ndvi-images", "nir-images"]
}
}
8 changes: 8 additions & 0 deletions agml/_assets/source_citations.json
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Expand Up @@ -102,5 +102,13 @@
"plant_doc_detection": {
"license": "CC BY-SA 4.0",
"citation": "@inproceedings{10.1145/3371158.3371196,\n author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},\n title = {PlantDoc: A Dataset for Visual Plant Disease Detection},\n year = {2020},\n isbn = {9781450377386},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n url = {https://doi.org/10.1145/3371158.3371196},\n doi = {10.1145/3371158.3371196},\n booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},\n pages = {249–253},\n numpages = {5},\n keywords = {Deep Learning, Object Detection, Image Classification},\n location = {Hyderabad, India},\n series = {CoDS COMAD 2020}\n }"
},
"carrot_weeds_uk": {
"license": "CC BY-NC-SA 3.0",
"citation": "@article{bosilj2019transfer,\n author = {Bosilj, Petra and Aptoula, Erchan and Duckett, Tom and Cielniak, Grzegorz},\n title = {Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture},\n journal = {Journal of Field Robotics},\n year = 2019,\n volume = {to be determined (published online)}\n }"
},
"onions_weeds_uk": {
"license": "CC BY-NC-SA 3.0",
"citation": "@article{bosilj2019transfer,\n author = {Bosilj, Petra and Aptoula, Erchan and Duckett, Tom and Cielniak, Grzegorz},\n title = {Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture},\n journal = {Journal of Field Robotics},\n year = 2019,\n volume = {to be determined (published online)}\n }"
}
}
79 changes: 76 additions & 3 deletions agml/_internal/preprocess.py
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Expand Up @@ -41,7 +41,7 @@
read_txt_file, get_image_info, get_label2id,
convert_bbox_to_coco, get_coco_annotation_from_obj, convert_xmls_to_cocojson,
mask_annotation_per_bbox, move_segmentation_dataset,
create_sub_masks, create_sub_mask_annotation_per_bbox
create_sub_masks, create_sub_mask_annotation_per_bbox, rgb2mask
)


Expand Down Expand Up @@ -816,9 +816,82 @@ def plant_doc_detection(self, dataset_name):
extract_num_from_imgid=False
)

def carrot_weeds_uk(self, dataset_name):
dataset_dir = os.path.join(self.data_original_dir, dataset_name)
rgb_paths, nir_paths, ndvi_paths, rgb_masks = ['images'], ['nir-images'], ['ndvi-images'], []

# get all rbg images, add unique identifier
for root, subdirs, files in list(os.walk(dataset_dir))[1:]:
id_ = root.split('/')[-1]
rgb_paths.append([os.path.join(root, "rgbreg_crop.png"), id_ + ".png"])
nir_paths.append([os.path.join(root, "depth_crop.png"), id_ + ".png"])
ndvi_paths.append([os.path.join(root, "ndvi_crop.png"), id_ + ".png"])
rgb_masks.append([os.path.join(root, "truth_crop.png"), id_ + "mask.png"])

processed_dir = os.path.join(self.data_processed_dir, dataset_name)
os.makedirs(processed_dir, exist_ok = True)
processed_annotation_dir = os.path.join(processed_dir, 'annotations')
os.makedirs(processed_annotation_dir, exist_ok = True)

image_types = [rgb_paths, nir_paths, ndvi_paths]



for image_type in image_types:
processed_image_dir = os.path.join(processed_dir, image_type[0])
os.makedirs(processed_image_dir, exist_ok = True)
for image_path in image_type[1:]:
shutil.copyfile(image_path[0], os.path.join(processed_image_dir, image_path[1]))

color2index = {
(0, 0, 0) : 0, # black is non-vegetation
(0, 0, 255) : 1, # red is carrot
(255, 0, 0) : 2, # blue is weed
}

for rgb_mask in rgb_masks:
rgb_mask_img = cv2.imread(rgb_mask[0])
index_mask = rgb2mask(rgb_mask_img, color2index)
anno_out = os.path.join(processed_annotation_dir, rgb_mask[1])
cv2.imwrite(anno_out, index_mask)

def onion_weeds_uk(self, dataset_name):
dataset_dir = os.path.join(self.data_original_dir, dataset_name)
rgb_paths, nir_paths, ndvi_paths, rgb_masks = ['images'], ['nir-images'], ['ndvi-images'], []

# get all rbg images, add unique identifier
for root, subdirs, files in list(os.walk(dataset_dir))[1:]:
id_ = root.split('/')[-1]
for file in files:
if file != 'partialc_crop.png' and file != 'truth.png':
img_type = file.split('_')[-2]
if img_type == 'depth':
nir_paths.append([os.path.join(root, file), id_ + ".png"])
elif img_type == 'ndvi':
ndvi_paths.append([os.path.join(root, file), id_ + ".png"])
elif img_type == 'rgbreg':
rgb_paths.append([os.path.join(root, file), id_ + ".png"])
rgb_masks.append([os.path.join(root, "truth.png"), id_ + "mask.png"])

processed_dir = os.path.join(self.data_processed_dir, dataset_name)
os.makedirs(processed_dir, exist_ok = True)
processed_annotation_dir = os.path.join(processed_dir, 'annotations')
os.makedirs(processed_annotation_dir, exist_ok = True)

image_types = [rgb_paths, nir_paths, ndvi_paths]

for image_type in image_types:
processed_image_dir = os.path.join(processed_dir, image_type[0])
os.makedirs(processed_image_dir, exist_ok = True)
for image_path in image_type[1:]:
shutil.copyfile(image_path[0], os.path.join(processed_image_dir, image_path[1]))

color2index = {
(0, 0, 0) : 0, # black is non-vegetation
(0, 0, 255) : 1, # red is onion
(255, 0, 0) : 2, # blue is weed
}

for rgb_mask in rgb_masks:
rgb_mask_img = cv2.imread(rgb_mask[0])
index_mask = rgb2mask(rgb_mask_img, color2index)
anno_out = os.path.join(processed_annotation_dir, rgb_mask[1])
cv2.imwrite(anno_out, index_mask)
28 changes: 28 additions & 0 deletions agml/_internal/process_utils.py
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Expand Up @@ -568,3 +568,31 @@ def move_segmentation_dataset(
shutil.copyfile(orig_annotation_path, out_label_path)
else:
annotation_preprocess_fn(orig_annotation_path, out_label_path)

def rgb2mask(img, color2index):
'''
Convert rgb image to mask
Arguments:
img: image with 3 channels, rbg
color2index: dictionary. key: tuple containing color values (b, g, r). value: corresponding index.
Returns:
a mask with no channels and index values assigned to each pixel
Source: https://stackoverflow.com/a/62170172
'''
assert len(img.shape) == 3
height, width, ch = img.shape
assert ch == 3

W = np.power(256, [[0],[1],[2]])

img_id = img.dot(W).squeeze(-1)
values = np.unique(img_id)

mask = np.zeros(img_id.shape)

for i, c in enumerate(values):
try:
mask[img_id==c] = color2index[tuple(img[img_id==c][0])]
except:
pass
return mask