help=
"Please provide the absolute or relative path where the download items go"
)
opt = parser.parse_args()
ENDPOINT = "https://southcentralus.api.cognitive.microsoft.com"
# Replace with a valid key
training_key = opt.training_key
project_id = opt.project_id
IMAGES_PATH = opt.result_path
os.makedirs(IMAGES_PATH, exist_ok=True)
trainer = CustomVisionTrainingClient(training_key, endpoint=ENDPOINT)
tagged_image_count = trainer.get_tagged_image_count(project_id)
print("Number of tagged images to download: %d" % tagged_image_count)
NUM_PER_REQUEST = 100
REQUEST_NUM = math.ceil(tagged_image_count / NUM_PER_REQUEST)
with open('../id.json', 'r') as f:
id_to_class = json.load(f)
class_to_id = {v: k for k, v in id_to_class.items()}
image_count = 0
anno = {}
for i in range(REQUEST_NUM):
image_list = trainer.get_tagged_images(project_id,
take=NUM_PER_REQUEST,
for tag in dest_tags:
dest_tags_dict[tag.name] = tag.id
dest_tag_ids.append(tag.id)
print(dest_tags_dict)
print(dest_tag_ids)
# ## Get the images on origin project
# In[48]:
import math
tagged_image_count=0
tagged_image_count=trainer.get_tagged_image_count(Project.id)
batchStartIndex=0
batchMaxIndex=256
num_batches=math.ceil(tagged_image_count/256)
print(tagged_image_count)
print(batchStartIndex)
print(batchMaxIndex)
print(num_batches)
tagged_images = []
for index in range(batchStartIndex, num_batches):
print(batchStartIndex, batchMaxIndex)
tagged_images.extend( trainer.get_tagged_images(Project.id, skip=batchStartIndex, take=256) )
batchStartIndex+=256
def main():
# Parse arguments
parser = argparse.ArgumentParser(description='Fetch data from CustomVision. Grab endpoint, training_key and project_id from https://www.customvision.ai/projects/<project_id>#/settings')
parser.add_argument('--endpoint', type=str,
help='"Endpoint" from Custom Vision project settings, e.g. https://westus2.api.cognitive.microsoft.com/')
parser.add_argument('--training_key', type=str,
help='"Key" from Custom Vision project settings, e.g. e46b53**************************')
parser.add_argument('--project_id', type=str,
help='"Project Id" from Custom Vision project settings, e.g. 5539cc35-****-****-****-************')
parser.add_argument('--output_directory', '-o', type=str,
help='e.g. "./downloads/"')
args = parser.parse_args()
endpoint = args.endpoint
training_key = args.training_key
project_id = args.project_id
download_directory = args.output_directory
# Prepare for Custom Vision download
credentials = ApiKeyCredentials(in_headers={"Training-key": training_key})
trainer = CustomVisionTrainingClient(endpoint, credentials)
unique_tags = []
image_count = trainer.get_tagged_image_count(project_id=project_id)
progress_bar = tqdm(total=image_count)
num_batches = math.ceil(image_count / download_batch_size)
print(f"There will be {num_batches} batches downloaded ({image_count} images in total).")
for batch_index in range(num_batches):
image_batch = trainer.get_tagged_images(project_id=project_id, take=download_batch_size, skip=batch_index * download_batch_size)
image_urls = []
for index, image in enumerate(image_batch):
base_filename = str((batch_index * download_batch_size) + index)
lines = []
for region in image.regions:
# Register tag in unique_tags
if region.tag_name not in unique_tags:
unique_tags.append(region.tag_name)
# Get index of region tag in unique_tags
tag_index = unique_tags.index(region.tag_name)
# Construct YOLO line (format is "<object-class> <x-center> <y-center> <width> <height>", all numbers normalized between 0 and 1)
line = f"{tag_index} {region.left + (region.width / 2)} {region.top + (region.height / 2)} {region.width} {region.height}"
lines.append(line)
# Create data file
data_filename = base_filename + ".txt"
with open(os.path.join(download_directory, data_filename), "w+") as data_file:
data_file.write("\n".join(lines))
# Queue image URL for download
image_download_url = image.original_image_uri
image_destination_path = os.path.join(download_directory, base_filename + ".jpg")
image_urls.append((image_download_url, image_destination_path))
def download_url(data):
request.urlretrieve(url=data[0], filename=data[1])
progress_bar.update(1)
# Download all image files in the batch simultaneously
ThreadPool(download_batch_size).map(download_url, image_urls)
progress_bar.close()
# Save unique tags into class.names file
with open(os.path.join(download_directory, "class.names"), "w+") as tags_file:
tags_file.write("\n".join(unique_tags))
