import matplotlib.pyplot as plt from utils import * from PIL import Image import imutils from vispy_utilities import * from CameraModels.Sphere import Sphere import vispy data_path_gt = "/home/kike/Documents/Dataset/ICCV_dataset/evaluation/Visualization/reasonable_image_MP3D" data_path_est = "/home/kike/Documents/Dataset/ICCV_dataset/evaluation/Visualization/evaluation/ASW_MP3D/eval" dir_disp_gt = os.path.join(data_path_gt, "disp_up_gt") dir_disp_est = os.path.join(data_path_est, "disp_up_est") dir_rgb_map = os.path.join(data_path_gt, "image_up") list_disp_gt = list_directories(dir_disp_gt) list_disp_est = list_directories(dir_disp_est) list_rgb_maps = list_directories(dir_rgb_map) i = 0 disp_gt = np.load(os.path.join(dir_disp_gt, list_disp_gt[i])) mask_range = disp_gt > 67.5 disp_gt[mask_range] = 0 im = Image.open(os.path.join(dir_disp_est, list_disp_est[i])) imarray = np.array(im) * (-1) disp_est = imutils.rotate_bound(imarray, 90) * 180 / 512 mask = disp_gt > 0 disp_est[(mask * (-1) + 1).astype(np.bool)] = 0
|--- image_up
|--- imag_down
"""
report_name = "MP3D_StereoMatching_disp_range.csv"
data_path_gt = "/home/kike/Documents/Dataset/ICCV_dataset/MP3D/test"
# report_name = "SF3D_StereoMatching_disp_range.csv"
# data_path_gt = "/home/kike/Documents/Dataset/ICCV_dataset/SF3D/test"
dir_disp_gt = os.path.join(data_path_gt, "disp_up")
dir_depth_gt = os.path.join(data_path_gt, "depth_up")
dir_rgb_map_up = os.path.join(data_path_gt, "image_up")
dir_rgb_map_down = os.path.join(data_path_gt, "image_down")
list_disp_gt = list_directories(dir_disp_gt)
list_depth_gt = list_directories(dir_depth_gt)
list_rgb_map_up = list_directories(dir_rgb_map_up)
list_rgb_map_down = list_directories(dir_rgb_map_down)
global_disp_range_1 = 0
global_disp_range_2 = 0
global_disp_range_3 = 0
report = []
stereo = StereoMatching()
tbar = tqdm(total=len(list_disp_gt))
for i in range(len(list_disp_gt)):
disp_gt = np.load(os.path.join(dir_disp_gt, list_disp_gt[i]))
|--- asw_estimation : disparity estimation
|--- disp_up: GT disparity
|--- depth_up: GT depth map
|--- image_up
|--- imag_down
"""
report_name = "MP3D_StereoMatching.csv"
data_path_gt = "/home/kike/Documents/Dataset/ICCV_dataset/MP3D/test"
data_path_est = "/home/kike/Documents/Dataset/ICCV_dataset/evaluation/Whole_estimations/MP3D/ASW"
dir_disp_gt = os.path.join(data_path_gt, "disp_up")
dir_disp_est = os.path.join(data_path_est, "disp_up_est")
dir_depth_gt = os.path.join(data_path_gt, "depth_up")
dir_rgb_map = os.path.join(data_path_gt, "image_up")
list_disp_gt = list_directories(dir_disp_gt)
list_disp_est = list_directories(dir_disp_est, key="_pp")
list_depth_gt = list_directories(dir_depth_gt)
list_rgb_map = list_directories(dir_rgb_map)
global_rmse_disp = 0
global_mae_disp = 0
global_rmse_depth = 0
global_mae_depth = 0
tbar = tqdm(total=len(list_disp_est))
for i in range(len(list_disp_est)):
disp_gt = np.load(os.path.join(dir_disp_gt, list_disp_gt[i]))
mask = disp_gt > 67.5
disp_gt[mask] = 0
|--- depth_up: GT depth map
|--- image_up
|--- imag_down
"""
report_name = "MP3D_subpixels.csv"
data_path_gt = "/home/kike/Documents/Dataset/ICCV_dataset/MP3D/test"
data_path_est = "/home/kike/Documents/Dataset/ICCV_dataset/evaluation/Whole_estimations/MP3D/subpixel"
dir_disp_gt = os.path.join(data_path_gt, "disp_up")
dir_disp_est = os.path.join(data_path_est, "disp_up_est")
dir_depth_gt = os.path.join(data_path_gt, "depth_up")
dir_depth_est = os.path.join(data_path_est, "depth_up_est")
dir_rgb_map = os.path.join(data_path_gt, "image_up")
list_disp_gt = list_directories(dir_disp_gt)
list_disp_est = list_directories(dir_disp_est)
list_depth_gt = list_directories(dir_depth_gt)
list_depth_est = list_directories(dir_depth_est)
list_rgb_map = list_directories(dir_rgb_map)
global_rmse_disp = 0
global_mae_disp = 0
global_rmse_depth = 0
global_mae_depth = 0
report = []
tbar = tqdm(total=len(list_disp_est))
for i in range(len(list_disp_est)):
try:
# set depth output for this x,y location to the best match
disp[y, x] = best_offset
save_obj("disp_1", disp)
tbar.close()
return disp
if __name__ == '__main__':
data_path = "/home/kike/Documents/Dataset/ICCV_dataset/MP3D/test"
dir_depth_map = os.path.join(data_path, "depth_up")
dir_rgb_map_up = os.path.join(data_path, "image_up")
dir_rgb_map_down = os.path.join(data_path, "image_down")
list_depth_maps = list_directories(dir_depth_map)
list_rgb_maps_up = list_directories(dir_rgb_map_up)
list_rgb_maps_down = list_directories(dir_rgb_map_down)
i = 0
depth_map = np.load(os.path.join(dir_depth_map, list_depth_maps[i]))[:, :,
0]
rgb_map_up = cv2.imread(os.path.join(dir_rgb_map_up, list_rgb_maps_up[i]))
rgb_map_down = cv2.imread(
os.path.join(dir_rgb_map_down, list_rgb_maps_down[i]))
img_up = cv2.cvtColor(rgb_map_up, cv2.COLOR_BGR2GRAY)
img_down = cv2.cvtColor(rgb_map_down, cv2.COLOR_BGR2GRAY)
baseline = 0.2
stereo_match(img_up, img_down, 6, 70)
angle2[j, i] = baseline * math.cos(theta_T)
mask = disp > 0
depth = np.zeros_like(disp).astype(np.float)
depth[mask] = (angle[mask] / np.tan(disp[mask] / 180 * math.pi)) + angle2[mask]
return depth
data_path = "/home/kike/Documents/Dataset/ICCV_dataset/MP3D/test"
rgb_map_dir = os.path.join(data_path, "image_up")
disp_map_gt_dir = os.path.join(data_path, "disp_up")
disp_map_est_dir = os.path.join(data_path, "asw_estimation")
rgb_maps = list_directories(rgb_map_dir)
disp_maps_gt = list_directories(disp_map_gt_dir)
disp_maps_est = list_directories(disp_map_est_dir, key="_pp")
for i in range(len(disp_maps_est)):
# i = 1
rgb_map = cv2.imread(os.path.join(rgb_map_dir, rgb_maps[i]))
disp_gt = np.load(os.path.join(disp_map_gt_dir, disp_maps_gt[i]))
mask = disp_gt > 192
disp_gt[mask] = 0
im = Image.open(os.path.join(disp_map_est_dir, disp_maps_est[i]))
imarray = np.array(im) * (-1)
disp_asw = imutils.rotate_bound(imarray, 90) * 180 / 512
import os import numpy as np from file_utilities import list_directories import cv2 import matplotlib.pyplot as plt from vispy_utilities import * from CameraModels.Sphere import Sphere import vispy data_path = "/home/kike/Documents/Dataset/ICCV_dataset/evaluation/SSD" depth_map_dir = os.path.join(data_path, "depth_up") rgb_map_dir = os.path.join(data_path, "image_up") estimation_dir = os.path.join(data_path, "est") depth_maps = list_directories(depth_map_dir) rgb_maps = list_directories(rgb_map_dir) estimation_maps = list_directories(estimation_dir) i = 0 depth_map = np.load(os.path.join(depth_map_dir, depth_maps[i]))[:, :, 0] rgb_map = cv2.imread(os.path.join(rgb_map_dir, rgb_maps[i])) estimation_map = np.load(os.path.join(estimation_dir, estimation_maps[i])) plt.figure(1) plt.imshow(depth_map, cmap='RdYlBu') plt.colorbar() plt.figure(2) plt.imshow(estimation_map, cmap='RdYlBu') plt.colorbar() plt.figure(3) # mask = depth_map > 0
import os
import numpy as np
from file_utilities import list_directories
import cv2
import imutils
import matplotlib.pyplot as plt
from PIL import Image
from tqdm import tqdm
data_path = "/home/kike/Documents/Dataset/ICCV_dataset/MP3D/test"
dir_image_up = os.path.join(data_path, "image_up_rotated")
dir_image_down = os.path.join(data_path, "image_down_rotated")
list_image_up = list_directories(dir_image_up)
list_image_down = list_directories(dir_image_down)
assert len(list_image_down) == len(list_image_up)
tbar = tqdm(total=len(list_image_up))
for i in range(len(list_image_up)):
image_up = cv2.imread(os.path.join(dir_image_up, list_image_up[i]))
imarray = np.array(image_up)
rotated = imutils.rotate_bound(imarray, -90)
cv2.imwrite(os.path.join(dir_image_up, list_image_up[i]), rotated)
image_down = cv2.imread(os.path.join(dir_image_down, list_image_down[i]))
imarray = np.array(image_down)
rotated = imutils.rotate_bound(imarray, -90)
cv2.imwrite(os.path.join(dir_image_down, list_image_down[i]), rotated)
tbar.update(1)
tbar.close()