adds generator script for cvat data

coco_cvat_gen.py

@@ -0,0 +1,72 @@
+import json, os
+import matplotlib.pyplot as plt
+import numpy as np
+
+from PIL import Image
+
+no_label = 0
+small = 0
+passed = 0
+count = 0
+
+
+
+print("loading coco annotations...")
+coco = json.load(open('./coco/annotations/instances_default.json'))
+print("done")
+
+def findAnnotationName(annotationId):
+    for c in coco['categories']:
+        if c['id'] == annotationId:
+            return c['name']
+
+def findAnnotationToId(ident):
+    for annotation in coco['annotations']:
+        img_an = annotation['image_id']
+        if img_an == ident:
+            return annotation 
+
+def show(pil, pause=0.2):
+    ImageNumpyFormat = np.asarray(pil)
+    plt.imshow(ImageNumpyFormat)
+    plt.draw()
+    plt.pause(pause) # pause how many seconds
+    plt.close()
+
+
+def parseImage(coImg):
+    global no_label, small, passed, coco
+    # open image file
+
+    path = "coco/roto_frank/images/" + coImg['file_name'].split('/')[4]
+    img = Image.open(path)
+
+    an = findAnnotationToId(coImg['id'])
+    if an == None:
+        no_label += 1
+        return
+
+    c = an['bbox']
+    crop = img.crop((c[0], c[1], c[0]+c[2], c[1]+c[3]))
+
+    if crop.width < 64 or crop.height < 64:
+        small += 1
+        return
+
+    imagePath = f"classified_roto/{findAnnotationName(an['category_id'])}/{an['id']}.png"
+    os.makedirs(os.path.dirname(imagePath), exist_ok=True)
+    crop.save(imagePath)
+    passed += 1
+
+if __name__ == "__main__":
+
+    for coImg in coco['images']:
+        parseImage(coImg)
+        count += 1
+        if count % 100 == 0:
+            print("status:")
+            print(f"no labels: {no_label}")
+            print(f"to small: {small}")
+            print(f"passed: {passed}")
+            print("-----")
+