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119 changes: 65 additions & 54 deletions download.py
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Original file line number Diff line number Diff line change
@@ -1,15 +1,18 @@
import pandas as pd
import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from tqdm import tqdm
import os

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 = []
Expand All @@ -24,74 +27,82 @@
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")