except BaseException:
print("Undable to find correspoding input image.")
break
segmentation = np.array(Image.open(
os.path.join(segmentations_folder, ann['file_name'])),
dtype=np.uint8)
segmentation_id = rgb2id(segmentation)
# find segments boundaries
boundaries = find_boundaries(segmentation_id, mode='thick')
if generate_new_colors:
segmentation[:, :, :] = 0
color_generator = IdGenerator(categegories)
for segment_info in ann['segments_info']:
color = color_generator.get_color(segment_info['category_id'])
mask = segmentation_id == segment_info['id']
segmentation[mask] = color
# depict boundaries
segmentation[boundaries] = [0, 0, 0]
if img is None:
plt.figure()
plt.imshow(segmentation)
plt.axis('off')
else:
plt.figure(figsize=(9, 5))
plt.subplot(121)
plt.imshow(img)
plt.axis('off')
def visualize(image_path, label_path):
"""
Visualizes in a pyplot window an image and a label pair from
provided paths. For reading Pillow is used so all paths and formats
must be Pillow-compatible.
Args:
image_path: an image path provided to Pillow.Image.open
label_path: a label path provided to Pillow.Image.open
"""
assert op.exists(image_path)
assert op.exists(label_path)
# Prepare canvases and decode the labels.
image = np.array(Image.open(image_path), dtype=np.uint8)
label = np.array(Image.open(label_path), dtype=np.int32)
uids_unique_org = np.unique(label)
semantic_segmentation = np.zeros((image.shape[0], image.shape[1], 3),
dtype=np.uint8)
instance_segmentation = np.zeros((image.shape[0], image.shape[1], 3),
dtype=np.uint8)
parts_segmentation = np.zeros((image.shape[0], image.shape[1], 3),
dtype=np.uint8)
sids, iids, _ = decode_uids(label)
# Color at the semantic level.
color_generator = IdGenerator(CATEGORIES)
for sid in np.unique(sids):
mask = np.equal(sids, sid)
color = CATEGORIES[sid]['color']
semantic_segmentation[mask] = color
# Color at the semantic and instance level and find the instance-level boundaries.
sids_only = np.where(iids < 0, sids, np.zeros_like(iids))
for sid in np.unique(sids_only):
mask = np.equal(sids_only, sid)
color = color_generator.get_color(sid)
instance_segmentation[mask] = color
sid_iids = np.where(iids >= 0, sids * 10**3 + iids, np.zeros_like(iids))
boundaries = np.full(sid_iids.shape, False)
for sid_iid in np.unique(sid_iids):
if sid_iid != 0:
mask = np.equal(sid_iids, sid_iid)
color = color_generator.get_color(sid_iid // 1000)
instance_segmentation[mask] = color
boundary_horizon = ndimage.sobel(mask, 0)
boundary_vertical = ndimage.sobel(mask, 1)
boundaries = np.logical_or(
np.hypot(boundary_horizon, boundary_vertical), boundaries)
# Color at the part level.
# Conver the original labels into the form for visualization with IdGenerator.
for uid in uids_unique_org:
# If uid is sid or sid_iid, encode them as they are.
if uid <= 99_999:
sid_iid = uid
# If uid is sid_iid_pid, map sid_pid to its corresponding sid and create new label as sid_iid.
else:
sid, iid, pid = decode_uids(uid)
sid_pid = sid * 10**2 + pid
if sid_pid in SID_PID2PARTS_CID:
sid_iid = SID_PID2PARTS_CID[sid_pid] * 10**3 + iid
else:
sid_iid = sid * 10**3 + iid
label[label == uid] = sid_iid
color_generator = IdGenerator(CATEGORIES_PARTS)
for sid_iid in np.unique(label):
# If sid_iid is in the format of sid , use sid for color generation (things and stuff classes differentiated by IdGenerator inherently).
if sid_iid <= 99:
id_ = sid_iid
# If sid_iid is in the format of sid_iid, get sid.
else:
id_ = sid_iid // 1000
mask = label == sid_iid
color = color_generator.get_color(id_)
parts_segmentation[mask] = color
# Depict boundaries.
instance_segmentation[boundaries] = [255, 255, 255]
parts_segmentation[boundaries] = [255, 255, 255]
# plot
# initialize figure for plotting
_, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)
# for ax in axes:
# ax.set_axis_off()
ax1.imshow(image)
ax1.set_title('image')
ax2.imshow(semantic_segmentation)
ax2.set_title('labels colored on semantic level')
ax3.imshow(instance_segmentation)
ax3.set_title('labels colored on semantic and instance levels')
ax4.imshow(parts_segmentation)
ax4.set_title('labels colored on semantic, instance, and parts levels')
plt.show()
