if not os.path.exists(img_path):
print('Image {} cannot be found at the path.'.format(img_path))
continue
annos_i = df_anno.loc[df_anno['image_id'] ==
img_name, :] # all annotations on this image
# if len(annos_i) < 20:
# continue
# if len(images_html) > 400: # cap on maximum to save
# break
try:
image = vis_utils.open_image(img_path).resize(viz_size)
except Exception as e:
print('Image {} failed to open. Error: {}'.format(img_path, e))
continue
# only save images with a particular class
# classes = list(annos_i.loc[:, 'category_id'])
# classes = [str(i) for i in classes]
# if '3' not in classes: # only save images with the 'group' class
# continue
if len(annos_i) > 0:
bboxes = list(annos_i.loc[:, 'bbox'])
classes = list(annos_i.loc[:, 'category_id'])
vis_utils.render_iMerit_boxes(bboxes, classes, image,
label_map) # image changed in place
boxes_and_scores = json.loads(row[2])
if images_local:
image_obj = os.path.join(args.images_dir, image_id)
if not os.path.exists(image_obj):
print('Image {} is not found at images_dir; skipped.'.format(
image_id))
continue
else:
if not blob_service.exists(container_name, blob_name=image_id):
print('Image {} is not found in the blob container {}; skipped.'.
format(image_id, container_name))
continue
image_obj = io.BytesIO()
_ = blob_service.get_blob_to_stream(container_name, image_id,
image_obj)
image = vis_utils.open_image(image_obj).resize(
viz_size) # resize is to display them more quickly
vis_utils.render_detection_bounding_boxes(
boxes_and_scores, image, confidence_threshold=args.confidence)
annotated_img_name = image_id.replace('/', '~')
annotated_img_path = os.path.join(args.out_dir, annotated_img_name)
image.save(annotated_img_path)
num_saved += 1
print('Rendered detection results on {} images, saved to {}.'.format(
num_saved, args.out_dir))
max_conf = float(row[1])
boxes_and_scores = json.loads(row[2])
if images_local:
image_obj = os.path.join(args.images_dir, image_id)
if not os.path.exists(image_obj):
print('Image {} is not found at local images_dir; skipped.'.format(image_id))
continue
else:
print('image_id:', image_id)
print('container_name:', container_name)
if not blob_service.exists(container_name, blob_name=image_id):
print('Image {} is not found in the blob container {}; skipped.'.format(image_id, container_name))
continue
image_obj = io.BytesIO()
_ = blob_service.get_blob_to_stream(container_name, image_id, image_obj)
# resize is for displaying them more quickly
image = vis_utils.resize_image(vis_utils.open_image(image_obj), args.output_image_width)
vis_utils.render_detection_bounding_boxes(boxes_and_scores, image, label_map=DETECTOR_LABEL_MAP,
confidence_threshold=args.confidence)
annotated_img_name = image_id.replace('/', '~').replace('\\', '~')
annotated_img_path = os.path.join(args.out_dir, annotated_img_name)
image.save(annotated_img_path)
num_saved += 1
print('Rendered detection results on {} images, saved to {}.'.format(num_saved, args.out_dir))