def process_files(ftp_client, ftp_files, args):
results = []
for ftp_file in ftp_files:
if not args.delete and processed is not None and ftp_file in processed:
continue
logging.info(ftp_file)
with tempfile.NamedTemporaryFile(suffix='_' + ftp_file,
mode='rb+') as image:
ftp_client.retrbinary('RETR ' + ftp_file, image.write)
api_res = recognition_api(image,
args.regions,
args.api_key,
args.sdk_url,
camera_id=args.camera_id,
timestamp=args.timestamp)
results.append(api_res)
if args.delete:
ftp_client.delete(ftp_file)
elif processed is not None:
processed.append(ftp_file)
if args.output_file:
save_results(results, args)
else:
print(json.dumps(results, indent=2))
def ftp_process(args, skip=None):
ftp = FTP()
ftp.connect(args.ftp_host)
ftp.login(args.ftp_user, args.ftp_password)
ftp.cwd(args.folder)
ftp_files = ftp.nlst()
logging.info('Connected. Found %s file(s) in %s.', len(ftp_files),
args.folder)
results = []
for ftp_file in ftp_files:
if skip and ftp_file in skip:
continue
logging.info(ftp_file)
with tempfile.NamedTemporaryFile(suffix='_' + ftp_file,
mode='rb+') as image:
ftp.retrbinary('RETR ' + ftp_file, image.write)
api_res = recognition_api(image,
args.regions,
args.api_key,
args.sdk_url,
camera_id=args.camera_id,
timestamp=args.timestamp)
results.append(api_res)
if args.delete:
ftp.delete(ftp_file)
if args.output_file:
save_results(results, args)
else:
print(json.dumps(results, indent=2))
return ftp_files
def process_image(path, args, i):
config = dict(threshold_d=args.detection_threshold,
threshold_o=args.ocr_threshold,
mode='redaction')
# Predictions
source_im = Image.open(path)
if source_im.mode != 'RGB':
source_im = source_im.convert('RGB')
images = [((0, 0), source_im)] # Entire image
# Top left and top right crops
if args.split_image:
y = 0
win_size = .55
width, height = source_im.width * win_size, source_im.height * win_size
for x in [0, int((1 - win_size) * source_im.width)]:
images.append(((x, y), source_im.crop(
(x, y, x + width, y + height))))
# Inference
results = []
for (x, y), im in images:
im_bytes = io.BytesIO()
im.save(im_bytes, 'JPEG', quality=95)
im_bytes.seek(0)
im_results = recognition_api(im_bytes,
args.regions,
args.api_key,
args.sdk_url,
config=config)
results.append(dict(prediction=im_results, x=x, y=y))
results = post_processing(merge_results(results))
results['filename'] = Path(path).name
# Set bounding box padding
for item in results['results']:
# Decrease padding size for large bounding boxes
b = item['box']
width, height = b['xmax'] - b['xmin'], b['ymax'] - b['ymin']
padding_x = int(
max(0, width * (.3 * math.exp(-10 * width / source_im.width))))
padding_y = int(
max(0, height * (.3 * math.exp(-10 * height / source_im.height))))
b['xmin'] = b['xmin'] - padding_x
b['ymin'] = b['ymin'] - padding_y
b['xmax'] = b['xmax'] + padding_x
b['ymax'] = b['ymax'] + padding_y
if args.show_boxes or args.save_blurred:
im = blur(source_im, 5, results)
if args.show_boxes:
im.show()
if args.save_blurred:
filename = Path(path)
im.save(filename.parent / ('%s_blurred%s' %
(filename.stem, filename.suffix)))
if 0:
draw_bb(source_im, results['results']).show()
return results
def recognition(my_file, regions, sdk_url, mmc, config):
with st.spinner('Processing'):
res = recognition_api(my_file,
regions=regions,
sdk_url=sdk_url,
mmc=mmc,
config=config)
return res
def process_files(ftp_client, ftp_files, args):
"""
Process a list of file paths by:
1. Deletes old files in ftp_files from ftp_client
2. For new files, retrieving the full file from ftp_client
3. Calling Snapshot API and Tracking Successfully Processed file paths
:param ftp_client:
:param ftp_files: List of files in the format [path, modified datetime], usually from a single folder.
:param args:
:return:
"""
results = []
for file_last_modified in ftp_files:
ftp_file = file_last_modified[0]
last_modified = file_last_modified[1]
if track_processed(args) and ftp_file in processed:
if args.delete is not None:
rm_older_than_date = datetime.now() - timedelta(
seconds=args.delete)
if rm_older_than_date > last_modified:
ftp_client.delete(ftp_file)
processed.remove(ftp_file)
continue
logging.info(ftp_file)
with tempfile.NamedTemporaryFile(suffix='_' + ftp_file,
mode='rb+') as image:
ftp_client.retrbinary('RETR ' + ftp_file, image.write)
api_res = recognition_api(image,
args.regions,
args.api_key,
args.sdk_url,
camera_id=args.camera_id,
timestamp=args.timestamp,
mmc=args.mmc,
exit_on_error=False)
results.append(api_res)
if track_processed(args):
processed.append(ftp_file)
if args.output_file:
save_results(results, args)
else:
print(json.dumps(results, indent=2))
def process_image(path, args):
options = CAMERA_OPTIONS[args.camera]
config = dict(threshold_d=.2, threshold_o=.3, mode='redaction')
# Pre process image
source_im = Image.open(path)
rotated = source_im.rotate(options['rotation'], expand=True)
top = rotated.height * options['crop_top']
bottom = rotated.height * options['crop_bottom']
api_im = rotated.crop((0, top, rotated.width, bottom))
# Predictions
images = [((0, 0), api_im)] # Entire image
# Top left and top right crops
y = 0
win_size = .55
width, height = api_im.width * win_size, api_im.height * win_size
for x in [0, (1 - win_size) * api_im.width]:
images.append(((x, y), api_im.crop((x, y, x + width, y + height))))
# Inference
results = []
for (x, y), im in images:
im_bytes = io.BytesIO()
im.save(im_bytes, 'JPEG', quality=95)
im_bytes.seek(0)
im_results = recognition_api(im_bytes,
args.regions,
args.api_key,
args.sdk_url,
config=config)
results.append(dict(prediction=im_results, x=x, y=y))
results = merge_results(results)
# Update bounding boxes
for lp in results['results']:
box = lp['box']
box['ymin'] += top
box['ymax'] += top
# Rotate
lp['box'] = rotate_bb(box, options['rotation'], rotated.size)
if 0:
draw_bb(source_im, results['results']).show()
return results
def process_image(path, args, i):
config = dict(threshold_d=args.detection_threshold,
threshold_o=args.ocr_threshold,
mode='redaction')
# Predictions
source_im = Image.open(path)
images = [((0, 0), source_im)] # Entire image
# Top left and top right crops
if args.split_image:
y = 0
win_size = .55
width, height = source_im.width * win_size, source_im.height * win_size
for x in [0, int((1 - win_size) * source_im.width)]:
images.append(((x, y), source_im.crop(
(x, y, x + width, y + height))))
# Inference
results = []
for (x, y), im in images:
im_bytes = io.BytesIO()
if im.mode == 'RGBA':
im = im.convert('RGB')
im.save(im_bytes, 'JPEG', quality=95)
im_bytes.seek(0)
im_results = recognition_api(im_bytes,
args.regions,
args.api_key,
args.sdk_url,
config=config)
results.append(dict(prediction=im_results, x=x, y=y))
results = merge_results(results)
if args.show_boxes:
blur(source_im, 5, results).show()
if 0:
draw_bb(source_im, results['results']).show()
return results
def main():
args = parse_arguments()
scores = []
for path in args.files:
blur_amount = 0
init_value = ''
while True:
# Blur image
image = Image.open(path)
if image.height > 1080:
image = image.resize(
(int(image.width * 1080 / image.height), 1080))
elif image.width > 1980:
image = image.resize(
(1980, int(image.height * 1980 / image.width)))
if blur_amount > 0:
image = image.filter(
ImageFilter.GaussianBlur(radius=blur_amount))
buffer = io.BytesIO()
image.save(buffer, 'jpeg')
buffer.seek(0)
# Do prediction
api_res = recognition_api(buffer,
args.regions,
args.api_key,
args.sdk_url,
camera_id=args.camera_id)
if not init_value:
init_value = api_res['results'][0]['plate']
elif not api_res[
'results'] or init_value != api_res['results'][0]['plate']:
break
blur_amount += .5
scores.append(blur_amount)
print(path, blur_amount)
print('Blur score', np.mean(scores))
def process_files(ftp_client, ftp_files, args):
results = []
rm_older_than_date = datetime.now() - timedelta(seconds=args.delete)
for file_last_modified in ftp_files:
ftp_file = file_last_modified[0]
last_modified = file_last_modified[1]
if ftp_file in processed:
if args.delete:
if rm_older_than_date > last_modified:
ftp_client.delete(ftp_file)
processed.remove(ftp_file)
continue
logging.info(ftp_file)
with tempfile.NamedTemporaryFile(suffix='_' + ftp_file,
mode='rb+') as image:
ftp_client.retrbinary('RETR ' + ftp_file, image.write)
api_res = recognition_api(image,
args.regions,
args.api_key,
args.sdk_url,
camera_id=args.camera_id,
timestamp=args.timestamp,
exit_on_error=False)
results.append(api_res)
processed.append(ftp_file)
if args.output_file:
save_results(results, args)
else:
print(json.dumps(results, indent=2))
def call_duration(path, sdk_url, config):
now = default_timer()
with open(path, 'rb') as fp:
recognition_api(fp, sdk_url=sdk_url, config=config)
return (default_timer() - now) * 1000