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"""
@date: 2021/8/4
@description:
"""
import numpy as np
import cv2
import scipy
from evaluation.f1_score import f1_score_2d
from loss import GradLoss
from utils.boundary import corners2boundaries, layout2depth
from utils.conversion import depth2xyz, uv2xyz, get_u, depth2uv, xyz2uv, uv2pixel
from utils.height import calc_ceil_ratio
from evaluation.iou import calc_IoU, calc_Iou_height
from visualization.boundary import draw_boundaries
from visualization.floorplan import draw_iou_floorplan
from visualization.grad import show_grad
def calc_accuracy(dt, gt, visualization=False, h=512):
visb_iou_2ds = []
visb_iou_3ds = []
full_iou_2ds = []
full_iou_3ds = []
iou_heights = []
visb_iou_floodplans = []
full_iou_floodplans = []
pano_bds = []
if 'depth' not in dt.keys():
dt['depth'] = gt['depth']
for i in range(len(gt['depth'])):
# print(i)
dt_xyz = dt['processed_xyz'][i] if 'processed_xyz' in dt else depth2xyz(np.abs(dt['depth'][i]))
visb_gt_xyz = depth2xyz(np.abs(gt['depth'][i]))
corners = gt['corners'][i]
full_gt_corners = corners[corners[..., 0] + corners[..., 1] != 0] # Take effective corners
full_gt_xyz = uv2xyz(full_gt_corners)
dt_xz = dt_xyz[..., ::2]
visb_gt_xz = visb_gt_xyz[..., ::2]
full_gt_xz = full_gt_xyz[..., ::2]
gt_ratio = gt['ratio'][i][0]
if 'ratio' not in dt.keys():
if 'boundary' in dt.keys():
w = len(dt['boundary'][i])
boundary = np.clip(dt['boundary'][i], 0.0001, 0.4999)
depth = np.clip(dt['depth'][i], 0.001, 9999)
dt_ceil_boundary = np.concatenate([get_u(w, is_np=True)[..., None], boundary], axis=-1)
dt_floor_boundary = depth2uv(depth)
dt_ratio = calc_ceil_ratio(boundaries=[dt_ceil_boundary, dt_floor_boundary])
else:
dt_ratio = gt_ratio
else:
dt_ratio = dt['ratio'][i][0]
visb_iou_2d, visb_iou_3d = calc_IoU(dt_xz, visb_gt_xz, dt_height=1 + dt_ratio, gt_height=1 + gt_ratio)
full_iou_2d, full_iou_3d = calc_IoU(dt_xz, full_gt_xz, dt_height=1 + dt_ratio, gt_height=1 + gt_ratio)
iou_height = calc_Iou_height(dt_height=1 + dt_ratio, gt_height=1 + gt_ratio)
visb_iou_2ds.append(visb_iou_2d)
visb_iou_3ds.append(visb_iou_3d)
full_iou_2ds.append(full_iou_2d)
full_iou_3ds.append(full_iou_3d)
iou_heights.append(iou_height)
if visualization:
pano_img = cv2.resize(gt['image'][i].transpose(1, 2, 0), (h*2, h))
# visb_iou_floodplans.append(draw_iou_floorplan(dt_xz, visb_gt_xz, iou_2d=visb_iou_2d, iou_3d=visb_iou_3d, side_l=h))
# full_iou_floodplans.append(draw_iou_floorplan(dt_xz, full_gt_xz, iou_2d=full_iou_2d, iou_3d=full_iou_3d, side_l=h))
visb_iou_floodplans.append(draw_iou_floorplan(dt_xz, visb_gt_xz, side_l=h))
full_iou_floodplans.append(draw_iou_floorplan(dt_xz, full_gt_xz, side_l=h))
gt_boundaries = corners2boundaries(gt_ratio, corners_xyz=full_gt_xyz, step=None, length=1024, visible=False)
dt_boundaries = corners2boundaries(dt_ratio, corners_xyz=dt_xyz, step=None, visible=False,
length=1024)#visb_gt_xyz.shape[0] if dt_xyz.shape[0] != visb_gt_xyz.shape[0] else None)
pano_bd = draw_boundaries(pano_img, boundary_list=gt_boundaries, boundary_color=[0, 0, 1])
pano_bd = draw_boundaries(pano_bd, boundary_list=dt_boundaries, boundary_color=[0, 1, 0])
pano_bds.append(pano_bd)
visb_iou_2d = np.array(visb_iou_2ds).mean()
visb_iou_3d = np.array(visb_iou_3ds).mean()
full_iou_2d = np.array(full_iou_2ds).mean()
full_iou_3d = np.array(full_iou_3ds).mean()
iou_height = np.array(iou_heights).mean()
if visualization:
visb_iou_floodplans = np.array(visb_iou_floodplans).transpose(0, 3, 1, 2) # NCHW
full_iou_floodplans = np.array(full_iou_floodplans).transpose(0, 3, 1, 2) # NCHW
pano_bds = np.array(pano_bds).transpose(0, 3, 1, 2)
return [visb_iou_2d, visb_iou_3d, visb_iou_floodplans],\
[full_iou_2d, full_iou_3d, full_iou_floodplans], iou_height, pano_bds, full_iou_2ds
def calc_ce(dt, gt):
w = 1024
h = 512
ce_s = []
for i in range(len(gt['corners'])):
floor_gt_corners = gt['corners'][i]
# Take effective corners
floor_gt_corners = floor_gt_corners[floor_gt_corners[..., 0] + floor_gt_corners[..., 1] != 0]
floor_gt_corners = np.roll(floor_gt_corners, -np.argmin(floor_gt_corners[..., 0]), 0)
gt_ratio = gt['ratio'][i][0]
ceil_gt_corners = corners2boundaries(gt_ratio, corners_uv=floor_gt_corners, step=None)[1]
gt_corners = np.concatenate((floor_gt_corners, ceil_gt_corners))
gt_corners = uv2pixel(gt_corners, w, h)
floor_dt_corners = xyz2uv(dt['processed_xyz'][i])
floor_dt_corners = np.roll(floor_dt_corners, -np.argmin(floor_dt_corners[..., 0]), 0)
dt_ratio = dt['ratio'][i][0]
ceil_dt_corners = corners2boundaries(dt_ratio, corners_uv=floor_dt_corners, step=None)[1]
dt_corners = np.concatenate((floor_dt_corners, ceil_dt_corners))
dt_corners = uv2pixel(dt_corners, w, h)
mse = np.sqrt(((gt_corners - dt_corners) ** 2).sum(1)).mean()
ce = 100 * mse / np.sqrt(w ** 2 + h ** 2)
ce_s.append(ce)
return np.array(ce_s).mean()
def calc_pe(dt, gt):
w = 1024
h = 512
pe_s = []
for i in range(len(gt['corners'])):
floor_gt_corners = gt['corners'][i]
# Take effective corners
floor_gt_corners = floor_gt_corners[floor_gt_corners[..., 0] + floor_gt_corners[..., 1] != 0]
floor_gt_corners = np.roll(floor_gt_corners, -np.argmin(floor_gt_corners[..., 0]), 0)
gt_ratio = gt['ratio'][i][0]
gt_floor_boundary, gt_ceil_boundary = corners2boundaries(gt_ratio, corners_uv=floor_gt_corners, length=w)
gt_floor_boundary = uv2pixel(gt_floor_boundary, w, h)
gt_ceil_boundary = uv2pixel(gt_ceil_boundary, w, h)
floor_dt_corners = xyz2uv(dt['processed_xyz'][i])
floor_dt_corners = np.roll(floor_dt_corners, -np.argmin(floor_dt_corners[..., 0]), 0)
dt_ratio = dt['ratio'][i][0]
dt_floor_boundary, dt_ceil_boundary = corners2boundaries(dt_ratio, corners_uv=floor_dt_corners, length=w)
dt_floor_boundary = uv2pixel(dt_floor_boundary, w, h)
dt_ceil_boundary = uv2pixel(dt_ceil_boundary, w, h)
gt_surface = np.zeros((h, w), dtype=np.int32)
gt_surface[gt_ceil_boundary[..., 1], np.arange(w)] = 1
gt_surface[gt_floor_boundary[..., 1], np.arange(w)] = 1
gt_surface = np.cumsum(gt_surface, axis=0)
dt_surface = np.zeros((h, w), dtype=np.int32)
dt_surface[dt_ceil_boundary[..., 1], np.arange(w)] = 1
dt_surface[dt_floor_boundary[..., 1], np.arange(w)] = 1
dt_surface = np.cumsum(dt_surface, axis=0)
pe = 100 * (dt_surface != gt_surface).sum() / (h * w)
pe_s.append(pe)
return np.array(pe_s).mean()
def calc_rmse_delta_1(dt, gt):
rmse_s = []
delta_1_s = []
for i in range(len(gt['depth'])):
gt_boundaries = corners2boundaries(gt['ratio'][i], corners_xyz=depth2xyz(gt['depth'][i]), step=None,
visible=False)
dt_xyz = dt['processed_xyz'][i] if 'processed_xyz' in dt else depth2xyz(np.abs(dt['depth'][i]))
dt_boundaries = corners2boundaries(dt['ratio'][i], corners_xyz=dt_xyz, step=None,
length=256 if 'processed_xyz' in dt else None,
visible=True if 'processed_xyz' in dt else False)
gt_layout_depth = layout2depth(gt_boundaries, show=False)
dt_layout_depth = layout2depth(dt_boundaries, show=False)
rmse = ((gt_layout_depth - dt_layout_depth) ** 2).mean() ** 0.5
threshold = np.maximum(gt_layout_depth / dt_layout_depth, dt_layout_depth / gt_layout_depth)
delta_1 = (threshold < 1.25).mean()
rmse_s.append(rmse)
delta_1_s.append(delta_1)
return np.array(rmse_s).mean(), np.array(delta_1_s).mean()
def calc_f1_score(dt, gt, threshold=10):
w = 1024
h = 512
f1_s = []
precision_s = []
recall_s = []
for i in range(len(gt['corners'])):
floor_gt_corners = gt['corners'][i]
# Take effective corners
floor_gt_corners = floor_gt_corners[floor_gt_corners[..., 0] + floor_gt_corners[..., 1] != 0]
floor_gt_corners = np.roll(floor_gt_corners, -np.argmin(floor_gt_corners[..., 0]), 0)
gt_ratio = gt['ratio'][i][0]
ceil_gt_corners = corners2boundaries(gt_ratio, corners_uv=floor_gt_corners, step=None)[1]
gt_corners = np.concatenate((floor_gt_corners, ceil_gt_corners))
gt_corners = uv2pixel(gt_corners, w, h)
floor_dt_corners = xyz2uv(dt['processed_xyz'][i])
floor_dt_corners = np.roll(floor_dt_corners, -np.argmin(floor_dt_corners[..., 0]), 0)
dt_ratio = dt['ratio'][i][0]
ceil_dt_corners = corners2boundaries(dt_ratio, corners_uv=floor_dt_corners, step=None)[1]
dt_corners = np.concatenate((floor_dt_corners, ceil_dt_corners))
dt_corners = uv2pixel(dt_corners, w, h)
Fs, Ps, Rs = f1_score_2d(gt_corners, dt_corners, [threshold])
f1_s.append(Fs[0])
precision_s.append(Ps[0])
recall_s.append(Rs[0])
return np.array(f1_s).mean(), np.array(precision_s).mean(), np.array(recall_s).mean()
def show_heat_map(dt, gt, vis_w=1024):
dt_heat_map = dt['corner_heat_map'].detach().cpu().numpy()
gt_heat_map = gt['corner_heat_map'].detach().cpu().numpy()
dt_heat_map_imgs = []
gt_heat_map_imgs = []
for i in range(len(gt['depth'])):
dt_heat_map_img = dt_heat_map[..., np.newaxis].repeat(3, axis=-1).repeat(20, axis=0)
gt_heat_map_img = gt_heat_map[..., np.newaxis].repeat(3, axis=-1).repeat(20, axis=0)
dt_heat_map_imgs.append(cv2.resize(dt_heat_map_img, (vis_w, dt_heat_map_img.shape[0])).transpose(2, 0, 1))
gt_heat_map_imgs.append(cv2.resize(gt_heat_map_img, (vis_w, dt_heat_map_img.shape[0])).transpose(2, 0, 1))
return dt_heat_map_imgs, gt_heat_map_imgs
def show_depth_normal_grad(dt, gt, device, vis_w=1024):
grad_conv = GradLoss().to(device).grad_conv
gt_grad_imgs = []
dt_grad_imgs = []
if 'depth' not in dt.keys():
dt['depth'] = gt['depth']
if vis_w == 1024:
h = 5
else:
h = int(vis_w / (12 * 10))
for i in range(len(gt['depth'])):
gt_grad_img = show_grad(gt['depth'][i], grad_conv, h)
dt_grad_img = show_grad(dt['depth'][i], grad_conv, h)
vis_h = dt_grad_img.shape[0] * (vis_w // dt_grad_img.shape[1])
gt_grad_imgs.append(cv2.resize(gt_grad_img, (vis_w, vis_h), interpolation=cv2.INTER_NEAREST).transpose(2, 0, 1))
dt_grad_imgs.append(cv2.resize(dt_grad_img, (vis_w, vis_h), interpolation=cv2.INTER_NEAREST).transpose(2, 0, 1))
return gt_grad_imgs, dt_grad_imgs
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