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import matplotlib.pyplot as plt |
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from pycocotools.coco import COCO |
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import skimage.io as io |
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import os |
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def plot_result(output_filename_format, im, image_id, coco): |
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print("Plotting image" + output_filename_format.format(image_id)) |
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annotation_ids = coco.getAnnIds(imgIds=image_id) |
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annotations = coco.loadAnns(annotation_ids) |
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dpi = 100 |
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f, axis = plt.subplots(1, 1, figsize=(im.shape[1] / dpi, im.shape[0] / dpi), dpi=dpi) |
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axis.imshow(im) |
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coco.showAnns(annotations) |
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axis.autoscale(False) |
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axis.axis('equal') |
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axis.axis('off') |
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axis.set_xlim([0, im.shape[1]]) |
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axis.set_ylim([im.shape[0], 0]) |
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plt.subplots_adjust(left=0, right=1, top=1, bottom=0) |
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plt.savefig(output_filename_format.format(image_id)) |
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plt.show() |
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def show_result_image(val_annotations_filepath, val_images_dirpath, info_list, image_id_list): |
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coco_gt = COCO(val_annotations_filepath) |
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image_ids = coco_gt.getImgIds(catIds=coco_gt.getCatIds()) |
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for info in info_list: |
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if info["annotations_filepath"] == "gt_annotations": |
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coco_dt = coco_gt |
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else: |
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coco_dt = coco_gt.loadRes(info["annotations_filepath"]) |
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for image_id in image_id_list: |
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assert image_id in image_ids, "image_id is invalid" |
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img_info = coco_gt.loadImgs(image_id)[0] |
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image_path = os.path.join(val_images_dirpath, img_info["file_name"]) |
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im = io.imread(image_path) |
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plot_result(info["output_filename_format"], im, image_id, coco_dt) |
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def main(): |
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val_annotations_filepath = "/data/data/mapping_challenge_dataset/raw/val/annotation.json" |
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val_images_dirpath = "/data/data/mapping_challenge_dataset/raw/val/images" |
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info_list = [ |
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{ |
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"output_filename_format": "crowdai_gt_{:012d}.pdf", |
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"annotations_filepath": "gt_annotations" |
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}, |
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{ |
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"output_filename_format": "crowdai_unet_resnet101_full_{:012d}.poly_viz.acm.tol_0.125.pdf", |
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"annotations_filepath": "/data/data/mapping_challenge_dataset/eval_runs/mapping_dataset.unet_resnet101_pretrained.train_val | 2020-05-21 08:32:48/test.annotation.poly.acm.tol_0.125.json" |
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}, |
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{ |
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"output_filename_format": "crowdai_unet_resnet101_full_{:012d}.poly_viz.simple.tol_0.125.pdf", |
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"annotations_filepath": "/data/data/mapping_challenge_dataset/eval_runs/mapping_dataset.unet_resnet101_pretrained.train_val | 2020-05-21 08:32:48/test.annotation.poly.simple.tol_0.125.json" |
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}, |
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{ |
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"output_filename_format": "crowdai_unet_resnet101_no_field_{:012d}.poly_viz.simple.tol_0.125.pdf", |
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"annotations_filepath": "/data/data/mapping_challenge_dataset/eval_runs/mapping_dataset.unet_resnet101_pretrained.field_off.train_val | 2020-05-21 08:33:20/test.annotation.poly.simple.tol_0.125.json" |
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}, |
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{ |
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"output_filename_format": "crowdai_unet16_no_coupling_losses_{:012d}.poly_viz.simple.tol_0.125.pdf", |
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"annotations_filepath": "/data/data/mapping_challenge_dataset/eval_runs/mapping_dataset.unet16.coupling_losses_off.train_val | 2020-03-01 13:27:45/test.annotation.poly.simple.tol_0.125.json" |
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}, |
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{ |
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"output_filename_format": "crowdai_unet16_no_coupling_losses_{:012d}.poly_viz.acm.tol_0.125.pdf", |
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"annotations_filepath": "/data/data/mapping_challenge_dataset/eval_runs/mapping_dataset.unet16.coupling_losses_off.train_val | 2020-03-01 13:27:45/test.annotation.poly.acm.tol_0.125.json" |
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}, |
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] |
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image_id_list = [21219, 443, 371, 265, 18205, 1, 2, 3, 4] |
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show_result_image(val_annotations_filepath, val_images_dirpath, info_list, image_id_list) |
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if __name__ == '__main__': |
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main() |
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