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import base64
import math
import re
from io import BytesIO
import matplotlib.cm
import numpy as np
import torch
import torch.nn
from PIL import Image
class RunningAverage:
def __init__(self):
self.avg = 0
self.count = 0
def append(self, value):
self.avg = (value + self.count * self.avg) / (self.count + 1)
self.count += 1
def get_value(self):
return self.avg
def denormalize(x, device='cpu'):
mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
return x * std + mean
class RunningAverageDict:
def __init__(self):
self._dict = None
def update(self, new_dict):
if self._dict is None:
self._dict = dict()
for key, value in new_dict.items():
self._dict[key] = RunningAverage()
for key, value in new_dict.items():
self._dict[key].append(value)
def get_value(self):
return {key: value.get_value() for key, value in self._dict.items()}
def colorize(value, vmin=10, vmax=1000, cmap='magma_r'):
value = value.cpu().numpy()[0, :, :]
invalid_mask = value == -1
# normalize
vmin = value.min() if vmin is None else vmin
vmax = value.max() if vmax is None else vmax
if vmin != vmax:
value = (value - vmin) / (vmax - vmin) # vmin..vmax
else:
# Avoid 0-division
value = value * 0.
# squeeze last dim if it exists
# value = value.squeeze(axis=0)
cmapper = matplotlib.cm.get_cmap(cmap)
value = cmapper(value, bytes=True) # (nxmx4)
value[invalid_mask] = 255
img = value[:, :, :3]
# return img.transpose((2, 0, 1))
return img
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def compute_errors(gt, pred):
thresh = np.maximum((gt / pred), (pred / gt))
a1 = (thresh < 1.25).mean()
a2 = (thresh < 1.25 ** 2).mean()
a3 = (thresh < 1.25 ** 3).mean()
abs_rel = np.mean(np.abs(gt - pred) / gt)
sq_rel = np.mean(((gt - pred) ** 2) / gt)
rmse = (gt - pred) ** 2
rmse = np.sqrt(rmse.mean())
rmse_log = (np.log(gt) - np.log(pred)) ** 2
rmse_log = np.sqrt(rmse_log.mean())
err = np.log(pred) - np.log(gt)
silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100
log_10 = (np.abs(np.log10(gt) - np.log10(pred))).mean()
return dict(a1=a1, a2=a2, a3=a3, abs_rel=abs_rel, rmse=rmse, log_10=log_10, rmse_log=rmse_log,
silog=silog, sq_rel=sq_rel)
##################################### Demo Utilities ############################################
def b64_to_pil(b64string):
image_data = re.sub('^data:image/.+;base64,', '', b64string)
# image = Image.open(cStringIO.StringIO(image_data))
return Image.open(BytesIO(base64.b64decode(image_data)))
# Compute edge magnitudes
from scipy import ndimage
def edges(d):
dx = ndimage.sobel(d, 0) # horizontal derivative
dy = ndimage.sobel(d, 1) # vertical derivative
return np.abs(dx) + np.abs(dy)
class PointCloudHelper():
def __init__(self, width=640, height=480):
self.xx, self.yy = self.worldCoords(width, height)
def worldCoords(self, width=640, height=480):
hfov_degrees, vfov_degrees = 57, 43
hFov = math.radians(hfov_degrees)
vFov = math.radians(vfov_degrees)
cx, cy = width / 2, height / 2
fx = width / (2 * math.tan(hFov / 2))
fy = height / (2 * math.tan(vFov / 2))
xx, yy = np.tile(range(width), height), np.repeat(range(height), width)
xx = (xx - cx) / fx
yy = (yy - cy) / fy
return xx, yy
def depth_to_points(self, depth):
depth[edges(depth) > 0.3] = np.nan # Hide depth edges
length = depth.shape[0] * depth.shape[1]
# depth[edges(depth) > 0.3] = 1e6 # Hide depth edges
z = depth.reshape(length)
return np.dstack((self.xx * z, self.yy * z, z)).reshape((length, 3))
#####################################################################################################
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