def process_img(img, key):
if 'distance' in key or 'depth' in key or 'seg' in key:
img = img.astype(np.float)
if not "seg" in key:
min_val = np.min(img)
img -= min_val
max_val = np.max(img)
if "seg" in key:
max_val = 36.0
print(
"Assuming, there are 36 total classes (NYU), if there are more this will cause a bug!"
)
if max_val != np.inf:
img /= max_val
else:
img /= np.max(img)
# clipping to avoid image errors
img = np.clip(img, 0.0, 1.0)
if len(img.shape) == 3:
img = img[:, :, 0]
elif 'flow' in key:
try:
# This import here is ugly, but else everytime someone uses this script it demands opencv and the progressbar
sys.path.append(os.path.join(os.path.dirname(__file__)))
from utils import flow_to_rgb
except ImportError:
raise ImportError(
"Using .hdf5 containers, which contain flow images needs opencv-python and progressbar "
"to be installed!")
img = flow_to_rgb(img)
elif "normals" in key:
img = np.clip(img, 0.0, 1.0)
return img
def process_img(img, key):
if 'distance' in key or 'depth' in key or 'seg' in key:
img = img.astype(np.float)
if not "seg" in key:
min_val = np.min(img)
img -= min_val
max_val = np.max(img)
if "seg" in key:
max_val = 36.0
print(
"Assuming, there are 36 total classes (NYU), if there are more this will cause a bug!"
)
if max_val != np.inf:
img /= max_val
else:
img /= np.max(img)
# clipping to avoid image errors
img = np.clip(img, 0.0, 1.0)
if len(img.shape) == 3:
img = img[:, :, 0]
elif 'flow' in key:
img = flow_to_rgb(img)
elif "normals" in key:
img = np.clip(img, 0.0, 1.0)
return img
def visFile(filePath, show=True):
if os.path.exists(filePath):
if os.path.isfile(filePath):
with h5py.File(filePath, 'r') as data:
if args.key is not None and args.key in [
key for key in data.keys()
]:
keys = [key for key in data.keys() if key == args.key]
else:
keys = [key for key in data.keys()]
for key in keys:
val = np.array(data[key])
if 'flow' in key and 'version' not in key:
val = flow_to_rgb(val)
if len(val.shape) == 2 or len(
val.shape) == 3 and val.shape[2] == 3:
plt.figure()
plt.title("{} in {}".format(
key, os.path.basename(filePath)))
val = process_img(val, key)
if len(val.shape) == 2 or len(
val.shape) == 3 and val.shape[2] == 1:
plt.imshow(val, cmap='jet')
else:
plt.imshow(val)
if show:
plt.show()
else:
print("The path is not a file")
else:
print("The file does not exist: {}".format(args.hdf5))
def vis_data(key, data, full_hdf5_data, file_label):
# If key is valid and does not contain segmentation data, create figure and add title
if key_matches(key, args.flow_keys + args.rgb_keys + args.other_non_rgb_keys):
plt.figure()
plt.title("{} in {}".format(key, file_label))
if key_matches(key, args.flow_keys):
try:
# This import here is ugly, but else everytime someone uses this script it demands opencv and the progressbar
sys.path.append(os.path.join(os.path.dirname(__file__)))
from utils import flow_to_rgb
except ImportError:
raise ImportError("Using .hdf5 containers, which contain flow images needs opencv-python and progressbar "
"to be installed!")
# Visualize optical flow
plt.imshow(flow_to_rgb(data), cmap='jet')
elif key_matches(key, args.segmap_keys):
# Try to find labels for each channel in the segcolormap
channel_labels = {}
_, key_index = key_matches(key, args.segmap_keys, return_index=True)
if key_index < len(args.segcolormap_keys):
# Check if segcolormap_key for the current segmap key is configured and exists
segcolormap_key = args.segcolormap_keys[key_index]
if segcolormap_key in full_hdf5_data:
# Extract segcolormap data
segcolormap = json.loads(np.array(full_hdf5_data[segcolormap_key]).tostring())
if len(segcolormap) > 0:
# Go though all columns, we are looking for channel_* ones
for colormap_key, colormap_value in segcolormap[0].items():
if colormap_key.startswith("channel_") and colormap_value.isdigit():
channel_labels[int(colormap_value)] = colormap_key[len("channel_"):]
# Make sure we have three dimensions
if len(data.shape) == 2:
data = data[:, :, None]
# Go through all channels
for i in range(data.shape[2]):
# Try to determine label
if i in channel_labels:
channel_label = channel_labels[i]
else:
channel_label = i
# Visualize channel
plt.figure()
plt.title("{} / {} in {}".format(key, channel_label, file_label))
plt.imshow(data[:, :, i], cmap='jet')
elif key_matches(key, args.other_non_rgb_keys):
# Make sure the data has only one channel, otherwise matplotlib will treat it as an rgb image
if len(data.shape) == 3:
if data.shape[2] != 1:
print("Warning: The data with key '" + key + "' has more than one channel which would not allow using a jet color map. Therefore only the first channel is visualized.")
data = data[:, :, 0]
plt.imshow(data, cmap='summer')
elif key_matches(key, args.rgb_keys):
plt.imshow(data)
def vis_data(key, data, full_hdf5_data, file_label):
# If key is valid and does not contain segmentation data, create figure and add title
if key in args.flow_keys + args.rgb_keys + args.other_non_rgb_keys:
plt.figure()
plt.title("{} in {}".format(key, file_label))
if key in args.flow_keys:
# Visualize optical flow
plt.imshow(flow_to_rgb(data), cmap='jet')
elif key in args.segmap_keys:
# Try to find labels for each channel in the segcolormap
channel_labels = {}
if args.segmap_keys.index(key) < len(args.segcolormap_keys):
# Check if segcolormap_key for the current segmap key is configured and exists
segcolormap_key = args.segcolormap_keys[args.segmap_keys.index(
key)]
if segcolormap_key in full_hdf5_data:
# Extract segcolormap data
segcolormap = json.loads(
np.array(full_hdf5_data[segcolormap_key]).tostring())
if len(segcolormap) > 0:
# Go though all columns, we are looking for channel_* ones
for colormap_key, colormap_value in segcolormap[0].items():
if colormap_key.startswith(
"channel_") and colormap_value.isdigit():
channel_labels[int(colormap_value)] = colormap_key[
len("channel_"):]
# Make sure we have three dimensions
if len(data.shape) == 2:
data = data[:, :, None]
# Go through all channels
for i in range(data.shape[2]):
# Try to determine label
if i in channel_labels:
channel_label = channel_labels[i]
else:
channel_label = i
# Visualize channel
plt.figure()
plt.title("{} / {} in {}".format(key, channel_label, file_label))
plt.imshow(data[:, :, i], cmap='jet')
elif key in args.other_non_rgb_keys:
# Make sure the data has only one channel, otherwise matplotlib will treat it as an rgb image
if len(data.shape) == 3:
if data.shape[2] != 1:
print(
"Warning: The data with key '" + key +
"' has more than one channel which would not allow using a jet color map. Therefore only the first channel is visualized."
)
data = data[:, :, 0]
plt.imshow(data, cmap='jet')
elif key in args.rgb_keys:
plt.imshow(data)
def process_img(img, key):
if 'depth' in key or 'seg' in key:
img = img.astype(np.float)
img -= np.min(img)
max_val = np.max(img)
if max_val != np.inf:
img /= max_val
else:
img /= np.max(img[img != np.inf])
if len(img.shape) == 3:
img = img[:, :, 0]
elif 'flow' in key:
img = flow_to_rgb(img)
return img
def vis_data(key, data, full_hdf5_data, file_label):
# If key is valid and does not contain segmentation data, create figure and add title
if key in args.flow_keys + args.rgb_keys + args.other_non_rgb_keys:
plt.figure()
plt.title("{} in {}".format(key, file_label))
if key in args.flow_keys:
# Visualize optical flow
plt.imshow(flow_to_rgb(data), cmap='jet')
elif key in args.segmap_keys:
# Try to find labels for each channel in the segcolormap
channel_labels = {}
if args.segmap_keys.index(key) < len(args.segcolormap_keys):
# Check if segcolormap_key for the current segmap key is configured and exists
segcolormap_key = args.segcolormap_keys[args.segmap_keys.index(key)]
if segcolormap_key in full_hdf5_data:
# Extract segcolormap data
segcolormap = json.loads(np.array(full_hdf5_data[segcolormap_key]).tostring())
if len(segcolormap) > 0:
# Go though all columns, we are looking for channel_* ones
for colormap_key, colormap_value in segcolormap[0].items():
if colormap_key.startswith("channel_") and colormap_value.isdigit():
channel_labels[int(colormap_value)] = colormap_key[len("channel_"):]
# Go through all channels
for i in range(data.shape[2]):
# Try to determine label
if i in channel_labels:
channel_label = channel_labels[i]
else:
channel_label = i
# Visualize channel
plt.figure()
plt.title("{} / {} in {}".format(key, channel_label, file_label))
plt.imshow(data[:, :, i], cmap='jet')
elif key in args.other_non_rgb_keys:
plt.imshow(data, cmap='jet')
elif key in args.rgb_keys:
plt.imshow(data)