def vis_one_image_opencv(
image: np.ndarray,
boxes: np.ndarray,
segms: Optional[Sequence[Segment]] = None,
show_box: bool = False,
show_class: bool = True,
) -> np.ndarray:
"""Constructs a numpy array with the detections visualized.
Args:
image: The image data
boxes: The box data
segms: Segmentations
show_box: Whether to show the boxes
show_class: Whether to show the object classes
Return:
The newly constructed image
"""
if boxes is None or boxes.shape[0] == 0:
return image
if segms:
color_list = colormap()
mask_color_id = 0
# Display in largest to smallest order to reduce occlusion
areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
sorted_inds = np.argsort(-areas)
for i in sorted_inds:
bbox = boxes[i, :4]
boxes[i, -1]
# show box (off by default)
if show_box:
image = vis_bbox(
image,
(bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))
# show class (off by default)
if show_class:
class_str = "hello"
image = vis_class(image, (bbox[0], bbox[1] - 2), class_str)
# show mask
if segms and len(segms) > i:
color_mask = color_list[mask_color_id % len(color_list), 0:3]
mask_color_id += 1
mask = decode_segment_to_mask(segms[i], image)
image = vis_mask(image, mask, color_mask)
return image
def vis_one_image(
image: np.ndarray,
image_name: str,
output_dir: str,
boxes: np.ndarray,
segms: Optional[Sequence[Segment]] = None,
dpi: int = 200,
box_alpha: float = 0.0,
show_class: bool = True,
extension: str = "pdf",
) -> None:
"""Visual debugging of detections.
Args:
image: The image data
image_name: The name of the image
output_dir: Directory to output to
boxes: Boxes
segms: Segmentations
dpi: DPI
box_alpha: Alpha channel of the boxes
show_class: Whether to show object classes
extension: Extension of the output file
"""
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if boxes is None or boxes.shape[0] == 0:
return
color_list = colormap(rgb=True) / 255
plt.get_cmap("rainbow")
fig = plt.figure(frameon=False)
fig.set_size_inches(image.shape[1] / dpi, image.shape[0] / dpi)
ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])
ax.axis("off")
fig.add_axes(ax)
ax.imshow(image)
sorted_inds: Union[List[Any], np.ndarray]
if boxes is None:
sorted_inds = [] # avoid crash when 'boxes' is None
else:
# Display in largest to smallest order to reduce occlusion
areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
sorted_inds = np.argsort(-areas)
mask_color_id = 0
for i in sorted_inds:
bbox = boxes[i, :4]
# show box (off by default)
ax.add_patch(
plt.Rectangle(
(bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
fill=False,
edgecolor="g",
linewidth=0.5,
alpha=box_alpha,
)
)
if show_class:
ax.text(
bbox[0],
bbox[1] - 2,
"WHERE IS THE TEXT car",
fontsize=30,
family="serif",
bbox=dict(facecolor="g", alpha=0.4, pad=0, edgecolor="none"),
color="white",
)
# show mask
if segms is not None and len(segms) > i:
img = np.ones(image.shape)
color_mask = color_list[mask_color_id % len(color_list), 0:3]
mask_color_id += 1
w_ratio = 0.4
for c in range(3):
color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio
for c in range(3):
img[:, :, c] = color_mask[c]
e = decode_segment_to_mask(segms[i], image)
e = e.astype(np.uint8)
_, contours, hier = cv2.findContours(e.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
for contour in contours:
polygon = Polygon(
contour.reshape((-1, 2)),
fill=True,
facecolor=color_mask,
edgecolor="w",
linewidth=1.2,
alpha=0.5,
)
ax.add_patch(polygon)
output_name = os.path.basename(image_name) + "." + extension
fig.savefig(os.path.join(output_dir, "{}".format(output_name)), dpi=dpi)
plt.close("all")