diff --git a/agml/_assets/public_datasources.json b/agml/_assets/public_datasources.json index fa31ef876..45176080b 100644 --- a/agml/_assets/public_datasources.json +++ b/agml/_assets/public_datasources.json @@ -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"] } } \ No newline at end of file diff --git a/agml/_assets/source_citations.json b/agml/_assets/source_citations.json index d13ba9433..9d0fc27da 100644 --- a/agml/_assets/source_citations.json +++ b/agml/_assets/source_citations.json @@ -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 }" } } \ No newline at end of file diff --git a/agml/_internal/preprocess.py b/agml/_internal/preprocess.py index 91b6e291a..9f4e83fde 100644 --- a/agml/_internal/preprocess.py +++ b/agml/_internal/preprocess.py @@ -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 ) @@ -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) diff --git a/agml/_internal/process_utils.py b/agml/_internal/process_utils.py index 3dc0154f5..db6653a42 100644 --- a/agml/_internal/process_utils.py +++ b/agml/_internal/process_utils.py @@ -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 \ No newline at end of file