class TestImageDownloader(unittest.TestCase):
@classmethod
def setUpClass(cls):
pass
@classmethod
def tearDownClass(cls):
pass
def setUp(self):
file_urls = 'C:/Users/brian.dsouza/Pictures/image_downloader/fileurls.txt'
destination_folder = 'C:/Users/brian.dsouza/Pictures/image_downloader/Images'
self.downloader_1 = ImageDownloader(file_urls=file_urls,
destination=destination_folder)
self.downloader_2 = ImageDownloader(file_urls=file_urls,
destination=destination_folder)
def tearDown(self):
self.downloader_1 = None
self.downloader_2 = None
def test_download_image(self):
""" Mock download for requests"""
with patch('image_downloader.requests.get') as mocked_get:
with patch('builtins.open',
unittest.mock.mock_open()) as mocked_file:
# Mock when return value is True
mocked_get.return_value.ok = True
mocked_get.return_value.content = b'imagevalue'
output1 = self.downloader_1.download_image(
'http://company.com/image1.png')
mocked_get.assert_called_with('http://company.com/image1.png',
timeout=10)
self.assertEqual(output1, 'Download Success')
# Mock when return value is False
mocked_get.return_value.ok = False
schedule = self.downloader_2.download_image(
'http://company.com/image1.png')
mocked_get.assert_called_with('http://company.com/image1.png',
timeout=10)
self.assertEqual(schedule, 'Download Failed')
def test_download_images(self):
with self.assertRaises(FileNotFoundError):
self.downloader_1.file_urls = "wrongurl"
self.downloader_1.download_images()
def download_and_classify_in_batches(complete_links_list, classifier):
print("Total amount of images to be downloaded and classified: %d" %
len(complete_links_list))
for index in range(0, len(complete_links_list), BATCH_SIZE):
time_start = time.time()
print("Downloading and classifying batch: %d -> %d" %
(index, index + BATCH_SIZE))
links_batch = complete_links_list[index:index + BATCH_SIZE]
tensor_images = ImageDownloader.download_images(
links_batch, NUM_DOWNLOAD_THREADS)
if len(tensor_images) == 0:
print("Skipping classification of empy list")
continue
results = classifier.classify_image_tensors(tensor_images)
results_df = DataHandler.convert_classification_result_to_dataframe(
results)
DataHandler.write_classification_result(results_df,
PARQUET_FILE_OUTPUT_LOCATION)
duration = time.time() - time_start
print("Duration of donwloading and classification for batch: %.2f" %
duration)
from image_downloader import ImageDownloader
if __name__ == '__main__':
file_urls = 'C:/Users/brian.dsouza/Pictures/image_downloader/fileurls.txt'
destination_folder = 'C:/Users/brian.dsouza/Pictures/image_downloader/Images'
image_downloader = ImageDownloader(file_urls=file_urls, destination=destination_folder)
image_downloader.download_images()