from depth_rgb_to_bboxes import utils_depth as viz if __name__=='__main__': import open3d as o3d import numpy as np # List all files on the directory dir_ = '/data/HectorSanchez/database/PeopleCounter/camara3/00000000/' fpaths = sorted(os.listdir(dir_)) FLAG_FIRST_TIME = False for path in fpaths: if '.png' in path: #print(path) if not FLAG_FIRST_TIME: z = viz.load_dim(dir_+path) x,y = viz.projection_correction(z) x = x[1:-1,1:-1] y = y[1:-1,1:-1] z = z[1:-1,1:-1] background = np.zeros((np.size(x),3)) background[:,0] = np.reshape(x,-1) background[:,1] = np.reshape(y,-1) background[:,2] = np.reshape(z,-1) FLAG_FIRST_TIME = True continue if True: # Uses matplotlib
std_path = os.path.join(mean_std_folder, folder, 'bg_std.npy')
bb_generator = udp.BBoxGenerator()
bb_generator.set_background(mean_path, std_path)
# For heads location files
files = sorted(os.listdir(os.path.join(heads_main_folder, folder)))
files = [x.replace('.txt', '.png') for x in files]
head_files[folder] = files
folder_path = os.path.join(ims_main_folder, folder)
pbar = tqdm(total=len(head_files[folder]))
for file in head_files[folder]:
fpath = os.path.join(folder_path, file)
dim = udp.load_dim(fpath)
bboxes = bb_generator.determine_bboxes(dim)
###
# Inspect visualiy that the bboxes are correct
if False:
print(file)
plt.imshow(dim)
plt.show()
plt.imshow(bb_generator.mask)
plt.show()
for bbox in bboxes:
dim = cv2.rectangle(dim, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
(0, 255, 0), 3)
mpath = os.path.join(mean_std_dir, 'bg_camera1_mean.npy')
spath = os.path.join(mean_std_dir, 'bg_camera1_std.npy')
# Get all the depth images
#fdir = '../ims/'
fdir = '/data/HectorSanchez/database/PeopleCounter/camara1/00000025/'
files = sorted(os.listdir(fdir))
files = [x for i, x in enumerate(files) if '.png' in x and i > 45]
# This is the only thing needed
bb_generator = udp.BBoxGenerator()
bb_generator.set_background(mpath, spath)
bb_generator.set_translation_matrix(trans)
bb_generator.set_rotation_matrix(rot)
bb_generator.set_focal_lenght(np.array(fl))
bb_generator.set_principal_point(np.array(pp))
#out_dir = '../ims_lbs/'
out_dir = '/data/HectorSanchez/database/PeopleCounter/camara1_lbs_v2/00000025/'
if not os.path.exists(out_dir):
os.makedirs(out_dir)
for file in files:
print('--->', file)
fim = file.replace('depth', 'color')
fim = fim.replace('png', 'jpg')
dim = udp.load_dim(fdir + file)
bboxes = bb_generator.determine_bboxes(dim, dx=-10, dy=30, fx=1, fy=1)
create_lbs([fim], [bboxes], out_dir)
files = sorted(os.listdir(fdir))
files = [x for i, x in enumerate(files) if '.png' in x and i > 100]
# This is the only thing needed
bb_generator = udp.BBoxGenerator()
bb_generator.set_background(mpath, spath)
for file in tqdm(files):
#print('--->',file)
fim = file.replace('depth', 'color')
fim = fim.replace('png', 'jpg')
rgb_im_path = os.path.join(fdir, fim)
im = cv2.imread(rgb_im_path)
dpt_im_path = os.path.join(fdir, file)
dim = udp.load_dim(dpt_im_path, M=M)
x, y = udp.projection_correction(dim)
bboxes_test = bb_generator.determine_bboxes(
dim) #,dx=0,dy=0,fx=1,fy=1)
# Show mask in im
im = im + np.expand_dims(bb_generator.mask, axis=-1) * 100
for bbox in bboxes_test:
im = cv2.rectangle(im,
pt1=(bbox[0], bbox[1]),
pt2=(bbox[2], bbox[3]),
color=(0, 0, 255),
thickness=3)
print('{}/{} Folder: {}'.format(i,len(folders),folder))
fdir = os.path.join(main_dir,folder)
print('--- Calculating SSIM:')
bg_extractor.calculate_sim(fdir)
#bg_extractor.ssim_chart()
th = ssim_ths[i]
#th = float(input('Select a threshold: '))
#th = 0.78
bg_extractor.determine_background(th)
print(' ====> BG frames',len(bg_extractor.background_fnames))
if False:
# Visualize background depth images
for file in bg_extractor.background_fnames:
z = udp.load_dim(fdir+file)
fig,ax = plt.subplots()
ax.imshow(z)
plt.show()
plt.close(fig)
# Calculate Mean and Std dev
if len(bg_extractor.background_fnames)==0: continue
tmp_dim_path = os.path.join(fdir,bg_extractor.background_fnames[0])
tmp_dim = udp.load_dim(tmp_dim_path)
#all_dims = np.zeros((tmp_dim.shape[0],tmp_dim.shape[1],0),dtype=np.float64)
all_dims = []
# Stack all the background depth images
# Set main output dir
main_out_dir = '/data2/HectorSanchez/database/PeopleCounter/FINAL_RUN/camara3/labels/'
for i, folder in enumerate(folders):
if i < 67:
"""
folder 61, 66 have some issues with depth images
"""
continue
print('---> Folder {} of {}: {}'.format(i + 1, len(folders), folder))
#if i<26:
# continue
folder_dir = os.path.join(folders_dir, folder)
files = sorted(os.listdir(folder_dir))
files = [x for x in files if '.png' in x]
out_dir = os.path.join(main_out_dir, folder)
print('out_dir:', out_dir)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
pbar = tqdm(total=len(files))
for file in files:
fim = file.replace('depth', 'color')
fim = fim.replace('png', 'jpg')
dim = udp.load_dim(os.path.join(folder_dir, file), M=M)
bboxes = bb_generator.determine_bboxes(
dim) #,dx=-10,dy=30,fx=1,fy=1)
create_lbs([os.path.join(out_dir, fim)], [bboxes])
pbar.update(1)
pbar.close()
if __name__ == '__main__':
#fdir = '../ims/'
fdir = '/data/HectorSanchez/database/PeopleCounter/camara1/00000000/'
print(fdir)
bg_extractor = udp.FindBackgroundDepth()
bg_extractor.calculate_sim(fdir)
bg_extractor.determine_background(.748)
bg_extractor.ssim_chart()
print('--->', len(bg_extractor.background_fnames))
if True:
# Visualize background depth images
for file in bg_extractor.background_fnames:
z = udp.load_dim(fdir + file)
fig, ax = plt.subplots()
ax.imshow(z)
plt.show()
plt.close(fig)
# Calculate Mean and Std dev
tmp_dim = udp.load_dim(fdir + bg_extractor.background_fnames[0])
all_dims = np.zeros((tmp_dim.shape[0], tmp_dim.shape[1], 0),
dtype=np.float64)
# Stack all the background depth images
pbar = tqdm(total=len(bg_extractor.background_fnames))
for file in bg_extractor.background_fnames:
dim = udp.load_dim(fdir + file)
