Make package

json2yolo/__init__.py

@@ -0,0 +1,3 @@
+from json2yolo.general_json2yolo import convert_coco_json, convert_ath_json, convert_vott_json, convert_infolks_json
+
+__all__ = ["convert_coco_json", "convert_ath_json", "convert_vott_json", "convert_infolks_json"]

general_json2yolo.py → json2yolo/general_json2yolo.py

@@ -4,7 +4,7 @@
 import pandas as pd
 from PIL import Image
 
-from utils import *
+from json2yolo.utils import *
 
 
 # Convert INFOLKS JSON file into YOLO-format labels ----------------------------
@@ -295,72 +295,6 @@ def convert_coco_json(json_dir='../coco/annotations/', use_segments=False, cls91
                 with open((fn / f).with_suffix('.txt'), 'a') as file:
                     file.write(('%g ' * len(line)).rstrip() % line + '\n')
 
-def min_index(arr1, arr2):
-    """Find a pair of indexes with the shortest distance. 
-    Args:
-        arr1: (N, 2).
-        arr2: (M, 2).
-    Return:
-        a pair of indexes(tuple).
-    """
-    # (N, M)
-    dis = ((arr1[:, None, :] - arr2[None, :, :]) ** 2).sum(-1)
-    index = np.unravel_index(np.argmin(dis, axis=None), dis.shape)
-    return index
-
-
-def merge_multi_segment(segments):
-    """Merge multi segments to one list.
-    Find the coordinates with min distance between each segment,
-    then connect these coordinates with one thin line to merge all 
-    segments into one.
-
-    Args:
-        segments(List(List)): original segmentations in coco's json file.
-            like [segmentation1, segmentation2,...], 
-            each segmentation is a list of coordinates.
-    """
-    s = []
-    segments = [np.array(i).reshape(-1, 2) for i in segments]
-    idx_list = [[] for _ in range(len(segments))]
-
-    # record the indexes with min distance between each segment
-    for i in range(1, len(segments)):
-        idx1, idx2 = min_index(segments[i - 1], segments[i])
-        idx_list[i - 1].append(idx1)
-        idx_list[i].append(idx2)
-
-    # use two round to connect all the segments
-    for k in range(2):
-        # forward connection
-        if k == 0:
-            for i, idx in enumerate(idx_list):
-                # middle segments have two indexes
-                # reverse the index of middle segments
-                if len(idx) == 2 and idx[0] > idx[1]:
-                    idx = idx[::-1]       
-                    segments[i] = segments[i][::-1, :]
-
-                segments[i] = np.roll(segments[i], -idx[0], axis=0)
-                segments[i] = np.concatenate([segments[i], segments[i][0:1]])
-                # deal with the first segment and the last one
-                if i == 0 or i == (len(idx_list) - 1):
-                    s.append(segments[i])
-                # deal with the middle ones
-                else:
-                    idx = [0, idx[1] - idx[0]]
-                    s.append(segments[i][idx[0]:idx[1] + 1])
-
-        # backward connection
-        else:
-            for i in range(len(idx_list) - 1, -1, -1):
-                if i != 0 and i != (len(idx_list) - 1):
-                    idx = idx_list[i]
-                    nidx = abs(idx[1] - idx[0])
-                    s.append(segments[i][nidx:])
-    return s
-
-
 
 if __name__ == '__main__':
     source = 'COCO'

labelbox_json2yolo.py → json2yolo/labelbox_json2yolo.py

@@ -7,7 +7,7 @@
 from PIL import Image
 from tqdm import tqdm
 
-from utils import make_dirs
+from json2yolo.utils import make_dirs
 
 
 def convert(file, zip=True):

utils.py → json2yolo/utils.py

@@ -164,3 +164,69 @@ def coco91_to_coco80_class():  # converts 80-index (val2014) to 91-index (paper)
          51, 52, 53, 54, 55, 56, 57, 58, 59, None, 60, None, None, 61, None, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
          None, 73, 74, 75, 76, 77, 78, 79, None]
     return x
+
+
+def min_index(arr1, arr2):
+    """Find a pair of indexes with the shortest distance. 
+    Args:
+        arr1: (N, 2).
+        arr2: (M, 2).
+    Return:
+        a pair of indexes(tuple).
+    """
+    # (N, M)
+    dis = ((arr1[:, None, :] - arr2[None, :, :]) ** 2).sum(-1)
+    index = np.unravel_index(np.argmin(dis, axis=None), dis.shape)
+    return index
+
+
+def merge_multi_segment(segments):
+    """Merge multi segments to one list.
+    Find the coordinates with min distance between each segment,
+    then connect these coordinates with one thin line to merge all 
+    segments into one.
+
+    Args:
+        segments(List(List)): original segmentations in coco's json file.
+            like [segmentation1, segmentation2,...], 
+            each segmentation is a list of coordinates.
+    """
+    s = []
+    segments = [np.array(i).reshape(-1, 2) for i in segments]
+    idx_list = [[] for _ in range(len(segments))]
+
+    # record the indexes with min distance between each segment
+    for i in range(1, len(segments)):
+        idx1, idx2 = min_index(segments[i - 1], segments[i])
+        idx_list[i - 1].append(idx1)
+        idx_list[i].append(idx2)
+
+    # use two round to connect all the segments
+    for k in range(2):
+        # forward connection
+        if k == 0:
+            for i, idx in enumerate(idx_list):
+                # middle segments have two indexes
+                # reverse the index of middle segments
+                if len(idx) == 2 and idx[0] > idx[1]:
+                    idx = idx[::-1]       
+                    segments[i] = segments[i][::-1, :]
+
+                segments[i] = np.roll(segments[i], -idx[0], axis=0)
+                segments[i] = np.concatenate([segments[i], segments[i][0:1]])
+                # deal with the first segment and the last one
+                if i == 0 or i == (len(idx_list) - 1):
+                    s.append(segments[i])
+                # deal with the middle ones
+                else:
+                    idx = [0, idx[1] - idx[0]]
+                    s.append(segments[i][idx[0]:idx[1] + 1])
+
+        # backward connection
+        else:
+            for i in range(len(idx_list) - 1, -1, -1):
+                if i != 0 and i != (len(idx_list) - 1):
+                    idx = idx_list[i]
+                    nidx = abs(idx[1] - idx[0])
+                    s.append(segments[i][nidx:])
+    return s

setup.py

@@ -0,0 +1,18 @@
+from setuptools import setup
+
+setup(name='json2yolo',
+      version='0.0.1',
+      author='Ultralytics',
+      packages=['json2yolo'],
+      description='Dataset converter for yolo',
+      license='GPL',
+      install_requires=[
+        "numpy",
+        "opencv-python>=4.1.2",
+        "pandas",
+        "Pillow",
+        "pyYAML",
+        "requests",
+        "tqdm",
+      ],
+)