def visualize_h5f(h5_path):
'''
:param h5_path:
:return: void
visualize bgr + colorized depth + colorized LiDAR
'''
h5f = h5py.File(h5_path, "r")
rgb_data = np.array(h5f['rgb'], dtype=np.uint8)
# Originally, shape of rgb_data: (3, 480, 640)
#print("Shape of rgb: ", rgb_data.shape)
rgb = np.transpose(rgb_data, (1, 2, 0))
bgr = rgb[:, :, ::-1]
depth = np.array(h5f['depth'], dtype=np.float32)
#print("Shape of depth: ", depth.shape)
max_depth = np.amax(depth)
min_depth = np.amin(depth)
colored_depth = np.array(colored_depthmap(depth, min_depth, max_depth), dtype=np.uint8)
#print("Shape of colored_depth: ", colored_depth.shape)
colored_depth = colored_depth[:, :, ::-1]
projected = np.array(h5f['scan_projected'], dtype=np.float32)
#print("Shape of projected: ", projected.shape)
# max_depth = np.amax(projected)
# min_depth = np.amin(projected)
colored_pc = np.array(colored_depthmap(projected, min_depth, max_depth), dtype=np.uint8)
#print("Shape of colored_pc: ", colored_pc.shape)
colored_pc = colored_pc[:, :, ::-1]
im_result = cv2.hconcat([bgr, colored_depth, colored_pc])
cv2.imshow("visualized", im_result)
cv2.waitKey(0)
def save_h5f2RGB(h5_path, file, save_path):
'''
:param h5_path, file, save_path:
:return: void
save concated bgr + colorized depth + colorized LiDAR as .png
'''
h5f = h5py.File(h5_path, "r")
rgb_data = np.array(h5f['rgb'], dtype=np.uint8)
# Originally, shape of rgb_data: (3, 480, 640)
#print("Shape of rgb: ", rgb_data.shape)
rgb = np.transpose(rgb_data, (1, 2, 0))
bgr = rgb[:, :, ::-1]
depth = np.array(h5f['depth'], dtype=np.float32)
#print("Shape of depth: ", depth.shape)
max_depth = np.amax(depth)
min_depth = np.amin(depth)
colored_depth = np.array(colored_depthmap(depth, min_depth, max_depth), dtype=np.uint8)
#print("Shape of colored_depth: ", colored_depth.shape)
colored_depth = colored_depth[:, :, ::-1]
projected = np.array(h5f['scan_projected'], dtype=np.float32)
#print("Shape of projected: ", projected.shape)
# max_depth = np.amax(projected)
# min_depth = np.amin(projected)
colored_pc = np.array(colored_depthmap(projected, min_depth, max_depth), dtype=np.uint8)
#print("Shape of colored_pc: ", colored_pc.shape)
colored_pc = colored_pc[:, :, ::-1]
im_result = cv2.hconcat([bgr, colored_depth, colored_pc])
save_file_path = os.path.join(save_path, file[:-2]) + 'png'
print(save_file_path)
cv2.imwrite(save_file_path, im_result)
def test(self):
n_test = len(self.testset)
device = torch.device('cuda:0' if self.use_gpu else 'cpu')
self.net.to(device)
self.net.eval()
self.print("<-------------Test the model-------------->")
colormaps = {'nyu': plt.cm.jet, 'kitti': plt.cm.plasma}
cm = colormaps[self.params.dataset]
#measure_list = ['a1', 'a2', 'a3', 'rmse', 'rmse_log', 'log10', 'abs_rel', 'sq_rel']
measures = {key: 0 for key in measure_list}
test_total_time = 0
with torch.no_grad():
#sys.stdout.flush()
#tbar = tqdm(self.testloader)
for step, data in enumerate(self.testloader):
images, labels = data[0].to(device), data[1].to(device)
before_op_time = time.time()
scales = [1]
if self.params.use_ms:
scales = [1, 1.25]
depths = predict_multi_scale(self.net, images, scales,
self.params.classes,
self.params.use_flip)
duration = time.time() - before_op_time
test_total_time += duration
# accuracy
new = self.eval_func(labels, depths)
print_str = "Test step [{}/{}], a1: {:.5f}, rmse: {:.5f}.".format(
step + 1, n_test, new['a1'], new['rmse'])
self.print(print_str)
#tbar.set_description(print_str)
#sys.stdout.flush()
images = images.cpu().numpy().squeeze().transpose(1, 2, 0)
labels = labels.cpu().numpy().squeeze()
depths = depths.cpu().numpy().squeeze()
labels = colored_depthmap(labels, cmap=cm).squeeze()
depths = colored_depthmap(depths, cmap=cm).squeeze()
#fuse = merge_images(images, labels, depths, self.params.min_depth, self.params.max_depth)
plt.imsave(
os.path.join(self.resdir, '{:04}_rgb.png'.format(step)),
images)
plt.imsave(
os.path.join(self.resdir, '{:04}_gt.png'.format(step)),
labels)
plt.imsave(
os.path.join(self.resdir, '{:04}_depth.png'.format(step)),
depths)
for k, v in new.items():
measures[k] += v.item()
fps = n_test / test_total_time
measures = {
key: round(value / n_test, 5)
for key, value in measures.items()
}
self.print('Testing done, fps {:4.2f} | {}'.format(fps, measures))
return
def save_depth_filled_csv(h5_path, file, save_path):
'''
:param h5_path, file, save_path:
:return: void
save new_depth2csv file (depth-filled)
'''
h5f = h5py.File(h5_path, "r")
rgb_data = np.array(h5f['rgb'], dtype=np.uint8)
# Originally, shape of rgb_data: (3, 480, 640)
rgb = np.transpose(rgb_data, (1, 2, 0))
bgr = rgb[:, :, ::-1]
bgr_input = bgr / 255
depth = np.array(h5f['depth'], dtype=np.float32)
new_depth = fill_depth_colorization(bgr_input, depth, 0.1)
new_depth = new_depth * 1000
new_depth = new_depth.astype(int)
max_depth = np.amax(new_depth)
min_depth = np.amin(new_depth)
colored_depth = np.array(colored_depthmap(new_depth, min_depth, max_depth),
dtype=np.uint8)
colored_depth = colored_depth[:, :, ::-1]
save_file_path = os.path.join(save_path, file[:-2]) + 'png'
print(save_file_path)
cv2.imwrite(save_file_path, colored_depth)
np.savetxt(os.path.join(save_path, file[:-2]) + 'csv',
new_depth,
delimiter=',')
def visualize_h5f2(h5_path):
'''
:param h5_path:
:return: void
NOT USED! needs modification.
Basically, First attempt was to use ONLY this function to save final image.
Problem : bgr type changed to CV_8SC3 (Don't know how to convert it to CV_8UC3 again.)
'''
h5f = h5py.File(h5_path, "r")
rgb_data = np.array(h5f['rgb'], dtype=np.uint8)
# Originally, shape of rgb_data: (3, 480, 640)
#print("Shape of rgb: ", rgb_data.shape)
rgb = np.transpose(rgb_data, (1, 2, 0))
bgr = rgb[:, :, ::-1]
# print(type((255,0,0)))
# bgr_new = cv2.circle(bgr, (100, 100), 10, (255,0,0),-1)
depth = np.array(h5f['depth'], dtype=np.float32)
#print("Shape of depth: ", depth.shape)
max_depth = np.amax(depth)
min_depth = np.amin(depth)
colored_depth = np.array(colored_depthmap(depth, min_depth, max_depth), dtype=np.uint8)
#print("Shape of colored_depth: ", colored_depth.shape)
colored_depth = colored_depth[:, :, ::-1]
projected = np.array(h5f['scan_projected'], dtype=np.float32)
#print("Shape of projected: ", projected.shape)
# max_depth = np.amax(projected)
# min_depth = np.amin(projected)
colored_pc = np.array(colored_depthmap(projected, min_depth, max_depth), dtype=np.uint8)
print("Shape of colored_pc: ", colored_pc.shape)
colored_pc = colored_pc[:, :, ::-1]
for i in range(480):
for j in range(640):
#get_color = tuple(map(int, colored_pc[i, j, :]))
reference_color = colored_pc[0, 0, :].tolist()
get_color = colored_pc[i, j, :].tolist()
#print(get_color, type(get_color))
if get_color != reference_color:
bgr = cv2.circle(bgr, (j, i), 2, get_color, -1)
bgr = np.asarray(bgr)
im_result = cv2.hconcat([bgr, colored_depth, colored_pc])
cv2.imshow("visualized", im_result)
#cv2.imshow("bgr", bgr_new)
cv2.waitKey(0)
def visualize_depth_filled_h5f(h5_path):
'''
:param h5_path:
:return: void
visualize bgr + depth-filled colorized depth + colorized LiDAR
'''
h5f = h5py.File(h5_path, "r")
rgb_data = np.array(h5f['rgb'], dtype=np.uint8)
# Originally, shape of rgb_data: (3, 480, 640)
print("Shape of rgb: ", rgb_data.shape)
rgb = np.transpose(rgb_data, (1, 2, 0))
bgr = rgb[:, :, ::-1]
bgr_input = bgr / 255
assert isinstance(bgr_input.shape, object)
#print("Shape of bgr_input: ", bgr_input.shape)
depth = np.array(h5f['depth'], dtype=np.float32)
#print("Shape of depth: ", depth.shape)
max_depth = np.amax(depth)
min_depth = np.amin(depth)
new_depth = fill_depth_colorization(bgr_input, depth, 1)
colored_depth = np.array(colored_depthmap(new_depth, min_depth, max_depth), dtype=np.uint8)
colored_depth = colored_depth[:, :, ::-1]
projected = np.array(h5f['scan_projected'], dtype=np.float32)
#print("Shape of projected: ", projected.shape)
# max_depth = np.amax(projected)
# min_depth = np.amin(projected)
colored_pc = np.array(colored_depthmap(projected, min_depth, max_depth), dtype=np.uint8)
#print("Shape of colored_pc: ", colored_pc.shape)
colored_pc = colored_pc[:, :, ::-1]
im_result = cv2.hconcat([bgr, colored_depth, colored_pc])
cv2.imshow("visualized", im_result)
cv2.waitKey(0)
def save_colored_depth(rel_path):
fList = os.listdir(rel_path)
NewList = sorted(fList, key=lambda x: int(x[7:-4]))
count = 0
for file in NewList:
depth_path = rel_path + file
depth_array = convert_img2csv_str2nparray(depth_path)
new_depth_image = colored_depthmap(depth_array)
save_path = save_rel_path + file[:-3] +'png'
cv2.imwrite(save_path, new_depth_image)
count = count + 1
def visualize_depth_and_projected_img(img_path, h5_path):
'''
:param h5_path, LiDAR-projected RGB img_path:
:return: void
visualize bgr with colorized LiDAR projected + colorized depth
'''
h5f = h5py.File(h5_path, "r")
depth = np.array(h5f['depth'], dtype=np.float32)
#print("Shape of depth: ", depth.shape)
max_depth = np.amax(depth)
min_depth = np.amin(depth)
colored_depth = np.array(colored_depthmap(depth, min_depth, max_depth), dtype=np.uint8)
#print("Shape of colored_depth: ", colored_depth.shape)
colored_depth = colored_depth[:, :, ::-1]
projected_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
print(projected_img.shape)
im_result = cv2.hconcat([projected_img, colored_depth])
cv2.imshow("visualized", im_result)
cv2.waitKey(0)
def save_concat_img(h5_path, img_path, file, save_path):
'''
:param h5_path, img_path, file, save_path:
:return: void
concat LiDAR-projected bgr img + colorized depth img and save it as .png
'''
h5f = h5py.File(h5_path, "r")
depth = np.array(h5f['depth'], dtype=np.float32)
#print("Shape of depth: ", depth.shape)
max_depth = np.amax(depth)
min_depth = np.amin(depth)
colored_depth = np.array(colored_depthmap(depth, min_depth, max_depth), dtype=np.uint8)
#print("Shape of colored_depth: ", colored_depth.shape)
colored_depth = colored_depth[:, :, ::-1]
projected_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
print(projected_img.shape)
im_result = cv2.hconcat([projected_img, colored_depth])
save_file_path = os.path.join(save_path, file[:-3]) + '.png'
print(save_file_path)
cv2.imwrite(save_file_path, im_result)
def save_projected_img(h5_path, file, save_path):
'''
:param h5_path, file name, save_path:
:return: void
save bgr img with colorized LiDAR projected
'''
h5f = h5py.File(h5_path, "r")
rgb_data = np.array(h5f['rgb'], dtype=np.uint8)
# Originally, shape of rgb_data: (3, 480, 640)
rgb = np.transpose(rgb_data, (1, 2, 0))
bgr = rgb[:, :, ::-1]
bgr = np.ascontiguousarray(
bgr, dtype=np.uint8) #Don't know why i should put this.
'''Used for finding min and max'''
depth = np.array(h5f['depth'], dtype=np.float32)
max_depth = np.amax(depth)
min_depth = np.amin(depth)
max_depth = 3
min_depth = 0
projected = np.array(h5f['scan_projected'], dtype=np.float32)
# max_depth = np.amax(projected)
# min_depth = np.amin(projected)
colored_pc = np.array(colored_depthmap(projected, min_depth, max_depth),
dtype=np.uint8)
colored_pc = colored_pc[:, :, ::-1]
for i in range(480):
for j in range(640):
reference_color = colored_pc[0, 0, :].tolist()
get_color = colored_pc[i, j, :].tolist()
if get_color != reference_color:
bgr = cv2.circle(bgr, (j, i), 5, get_color, -1)
save_file_path = os.path.join(save_path, file[:-2]) + 'png'
cv2.imwrite(save_file_path, bgr)
def plot_colored_depth(rel_path):
fList = os.listdir(rel_path)
NewList = sorted(fList, key=lambda x: int(x[7:-4]))
count = 0
for file in NewList:
depth_path = rel_path + file
depth_array = convert_img2csv_str2nparray(depth_path)
new_depth_image = colored_depthmap(depth_array)
'''
Use the following
if you want to plot rgb and depth concurrently:
img_path = img_rel_path + file[:-3] + 'png'
img = imread(img_path)
f = plt.figure()
f.set_size_inches(18.5, 10.5, forward=True)
f.add_subplot(1, 2, 1)
plt.imshow(new_depth_image, cmap='jet')
f.add_subplot(1, 2, 2)
plt.imshow(img)
plt.show()
plt.clf()
plt.close()
'''
cv2.imshow("colored depth", new_depth_image)
cv2.waitKey(0)
count = count + 1
if count == 30:
break
from utils import colored_depthmap
from convert_str2nparray import convert_img2csv_str2nparray
rel_path = '/home/hj/ICRA2020/190403/rp_img2csv/'
img_rel_path = '/home/hj/ICRA2020/190403/img/'
save_rel_path = '/home/hj/ICRA2020/190403/debug/'
fList = os.listdir(rel_path)
NewList = sorted(fList, key=lambda x: int(x[7:-4]))
count = 0
for file in NewList:
depth_path = rel_path + file
NpArray = convert_img2csv_str2nparray(depth_path)
CMap = colored_depthmap(NpArray, 0)
img_path = img_rel_path + file[:-3] + 'png'
img = imread(img_path)
save_path = save_rel_path + file[:-3] + 'png'
f = plt.figure()
f.set_size_inches(18.5, 10.5, forward=True)
f.add_subplot(1, 2, 1)
plt.imshow(CMap, 'bwr', origin='lower')
f.add_subplot(1, 2, 2)
plt.imshow(img)
plt.show()
#f.savefig(save_path)