Format code with black, yapf, autopep8 and isort

Author: deepsource-autofix[bot]

download.py

@@ -1,13 +1,15 @@
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
 from tqdm import tqdm
+
 try:
     from urllib.request import urlretrieve  # Python 3
 except ImportError:
     from urllib import urlretrieve  # Python 2
-classes = pd.read_csv('./classes.csv')
-labelnames = classes['LabelName'].tolist()
-classnames = classes['DisplayName'].tolist()
+
+classes = pd.read_csv("./classes.csv")
+labelnames = classes["LabelName"].tolist()
+classnames = classes["DisplayName"].tolist()
 __imageids = []
 __imageids_and_bbox = {}
 imageids = []
@@ -22,74 +24,82 @@
 image_id = 0
 
 imageid_and_labelname = pd.read_csv(
-    './open_images_data/oidv6-train-annotations-human-imagelabels.csv')
-imageid_and_labelname.append(pd.read_csv(
-    './open_images_data/test-annotations-human-imagelabels-boxable.csv'))
-imageid_and_labelname.append(pd.read_csv(
-    './open_images_data/test-annotations-machine-imagelabels.csv'))
-imageid_and_labelname.append(pd.read_csv(
-    './open_images_data/train-annotations-human-imagelabels-boxable.csv'))
-imageid_and_labelname.append(pd.read_csv(
-    './open_images_data/train-annotations-machine-imagelabels.csv'))
-imageid_and_labelname.append(pd.read_csv(
-    './open_images_data/validation-annotations-human-imagelabels-boxable.csv'))
-imageid_and_labelname.append(pd.read_csv(
-    './open_images_data/validation-annotations-machine-imagelabels.csv'))
-tqdm_iter = tqdm(imageid_and_labelname['ImageID'])
-for imageid, labelname in zip(tqdm_iter, imageid_and_labelname['LabelName']):
+    "./open_images_data/oidv6-train-annotations-human-imagelabels.csv")
+imageid_and_labelname.append(
+    pd.read_csv(
+        "./open_images_data/test-annotations-human-imagelabels-boxable.csv"))
+imageid_and_labelname.append(
+    pd.read_csv("./open_images_data/test-annotations-machine-imagelabels.csv"))
+imageid_and_labelname.append(
+    pd.read_csv(
+        "./open_images_data/train-annotations-human-imagelabels-boxable.csv"))
+imageid_and_labelname.append(
+    pd.read_csv(
+        "./open_images_data/train-annotations-machine-imagelabels.csv"))
+imageid_and_labelname.append(
+    pd.read_csv(
+        "./open_images_data/validation-annotations-human-imagelabels-boxable.csv"
+    ))
+imageid_and_labelname.append(
+    pd.read_csv(
+        "./open_images_data/validation-annotations-machine-imagelabels.csv"))
+tqdm_iter = tqdm(imageid_and_labelname["ImageID"])
+for imageid, labelname in zip(tqdm_iter, imageid_and_labelname["LabelName"]):
     if labelname in labelnames:
-        tqdm_iter.set_description(f'{imageid}-{labelname}')
+        tqdm_iter.set_description(f"{imageid}-{labelname}")
         __imageids.append(imageid)
 
 del imageid_and_labelname
 
-
 xmin_ymin_xmax_ymax = pd.read_csv(
-    './open_images_data/oidv6-train-annotations-bbox.csv')
-xmin_ymin_xmax_ymax.append(pd.read_csv(
-    './open_images_data/test-annotations-bbox.csv'))
-xmin_ymin_xmax_ymax.append(pd.read_csv(
-    './open_images_data/validation-annotations-bbox.csv'))
+    "./open_images_data/oidv6-train-annotations-bbox.csv")
+xmin_ymin_xmax_ymax.append(
+    pd.read_csv("./open_images_data/test-annotations-bbox.csv"))
+xmin_ymin_xmax_ymax.append(
+    pd.read_csv("./open_images_data/validation-annotations-bbox.csv"))
 for i in tqdm(range(len(xmin_ymin_xmax_ymax))):
     info = xmin_ymin_xmax_ymax.iloc[i]
-    if info['ImageID'] in __imageids:
-        __imageids_and_bbox[info['ImageID']] = [
-            info['XMin'], info['YMin'], info['XMax'], info['YMax']]
+    if info["ImageID"] in __imageids:
+        __imageids_and_bbox[info["ImageID"]] = [
+            info["XMin"],
+            info["YMin"],
+            info["XMax"],
+            info["YMax"],
+        ]
 del xmin_ymin_xmax_ymax
 
 urls = pd.read_csv(
-    './open_images_data/oidv6-train-images-with-labels-with-rotation.csv')
-urls.append(pd.read_csv(
-    './open_images_data/test-images-with-rotation.csv'))
-urls.append(pd.read_csv(
-    './open_images_data/train-images-boxable-with-rotation.csv'))
-urls.append(pd.read_csv(
-    './open_images_data/validation-images-with-rotation.csv'))
+    "./open_images_data/oidv6-train-images-with-labels-with-rotation.csv")
+urls.append(pd.read_csv("./open_images_data/test-images-with-rotation.csv"))
+urls.append(
+    pd.read_csv("./open_images_data/train-images-boxable-with-rotation.csv"))
+urls.append(
+    pd.read_csv("./open_images_data/validation-images-with-rotation.csv"))
 for i in tqdm(range(len(urls))):
     url = urls.iloc[i]
-    if url['ImageID'] in __imageids:
-        urlretrieve(url['OriginalURL'], f"./data/{image_id}.png")
-        xmin, ymin, xmax, ymax = __imageids_and_bbox[url['ImageID']]
+    if url["ImageID"] in __imageids:
+        urlretrieve(url["OriginalURL"], f"./data/{image_id}.png")
+        xmin, ymin, xmax, ymax = __imageids_and_bbox[url["ImageID"]]
         file_names.append(f"./data/{image_id}.png")
-        type_of_data.append(url['Subset'])
-        imageurls.append(url['OriginalURL'])
-        imageurls_original.append(url['OriginalLandingURL'])
-        imageids.append(url['ImageID'])
+        type_of_data.append(url["Subset"])
+        imageurls.append(url["OriginalURL"])
+        imageurls_original.append(url["OriginalLandingURL"])
+        imageids.append(url["ImageID"])
         xmins.append(xmin)
         ymins.append(ymin)
         xmaxs.append(xmax)
         ymaxs.append(ymax)
         image_id += 1
 
 data = pd.DataFrame({
-    'ImageIds': imageids,
-    'XMin': xmins,
-    'YMin': ymins,
-    'XMax': xmaxs,
-    'YMax': ymaxs,
-    'File Name': file_names,
-    'ImageUrls': imageurls,
-    'Og_ImageUrls': imageurls_original,
-    'Type of Data': type_of_data,
+    "ImageIds": imageids,
+    "XMin": xmins,
+    "YMin": ymins,
+    "XMax": xmaxs,
+    "YMax": ymaxs,
+    "File Name": file_names,
+    "ImageUrls": imageurls,
+    "Og_ImageUrls": imageurls_original,
+    "Type of Data": type_of_data,
 })
-data.to_csv('./data.csv')
+data.to_csv("./data.csv")