def visualize_results(img_path,
bboxes,
labels,
scores,
cfg,
store_to_path=None):
"""
Renders the detection results (bboxes and labels) onto the image.
:param img_path: the path to the image
:param bboxes: the predicted bounding boxes
:param labels: the single class label per bounding box
:param scores: the probability for the assigned class label per bounding box
:param cfg: the configuration
:param store_to_path: optional: a path where to store the rendered image.
If set to 'None' the image will be displayed on screen.
:return:
"""
from matplotlib.pyplot import imsave, imshow, show
from utils.plot_helpers import visualize_detections
img = visualize_detections(img_path,
bboxes,
labels,
scores,
cfg.IMAGE_WIDTH,
cfg.IMAGE_HEIGHT,
classes=cfg["DATA"].CLASSES,
draw_negative_rois=cfg.DRAW_NEGATIVE_ROIS)
if store_to_path is not None:
imsave(store_to_path, img)
else:
imshow(img)
show()
def visualize_results(img_path, bboxes, labels, scores, cfg, store_to_path=None):
"""
Renders the detection results (bboxes and labels) onto the image.
:param img_path: the path to the image
:param bboxes: the predicted bounding boxes
:param labels: the single class label per bounding box
:param scores: the probability for the assigned class label per bounding box
:param cfg: the configuration
:param store_to_path: optional: a path where to store the rendered image.
If set to 'None' the image will be displayed on screen.
:return:
"""
from matplotlib.pyplot import imsave, imshow, show
from utils.plot_helpers import visualize_detections
img = visualize_detections(img_path, bboxes, labels, scores,
cfg.IMAGE_WIDTH, cfg.IMAGE_HEIGHT,
classes = cfg["DATA"].CLASSES,
draw_negative_rois = cfg.DRAW_NEGATIVE_ROIS)
if store_to_path is not None:
imsave(store_to_path, img)
else:
imshow(img)
show()