87 lines
2.1 KiB
Python
87 lines
2.1 KiB
Python
import json, os
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import matplotlib.pyplot as plt
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import numpy as np
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from PIL import Image
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no_label = 0
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small = 0
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passed = 0
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count = 0
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def findAnnotationName(annotationId, annotations):
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for c in annotations['categories']:
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if c['id'] == annotationId:
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return c['name']
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def findAnnotationToId(ident, annotations):
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for annotation in annotations['annotations']:
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img_an = annotation['image_id']
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if img_an == ident:
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return annotation
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def show(pil, pause=0.2):
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ImageNumpyFormat = np.asarray(pil)
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plt.imshow(ImageNumpyFormat)
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plt.draw()
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plt.pause(pause) # pause how many seconds
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plt.close()
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def parseImage(coImg, annotations, subset):
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global no_label, small, passed
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# open image file
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path = "coco/"+subset+"/images/" + coImg['file_name'].split('/')[4]
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img = Image.open(path)
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an = findAnnotationToId(coImg['id'], annotations)
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if an == None:
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no_label += 1
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return
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c = an['bbox']
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crop = img.crop((c[0], c[1], c[0]+c[2], c[1]+c[3]))
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if crop.width < 64 or crop.height < 64:
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small += 1
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return
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imagePath = f"classified/classified_{subset}/{findAnnotationName(an['category_id'], annotations)}/{an['id']}.png"
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os.makedirs(os.path.dirname(imagePath), exist_ok=True)
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crop.save(imagePath)
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passed += 1
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def parseSubset(subset):
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global count
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print("loading" + subset + "annotations...")
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annotations = json.load(open('./coco/'+subset+'/annotations/instances_default.json'))
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print("done")
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for coImg in annotations['images']:
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parseImage(coImg, annotations,subset)
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count += 1
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if count % 100 == 0:
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print("status:")
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print(f"no labels: {no_label}")
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print(f"to small: {small}")
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print(f"passed: {passed}")
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print("-----")
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if __name__ == "__main__":
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parseSubset('donaulager')
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parseSubset('instantina')
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parseSubset('lkw_walter_2019')
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parseSubset('lkw_walter_2020')
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parseSubset('tkl')
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parseSubset('vw_bratislava')
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parseSubset('vw_portugal')
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parseSubset('watt')
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parseSubset('wls')
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