def test3():
""" Edge with margins clipped """
dir_pth = "/home/quanquan/oneshot-landmark/dataset/Cephalometric/RawImage/TrainingData/"
save_pth = "/home/quanquan/oneshot-landmark/dataset/Cephalometric/RawImage/edge/"
filenames = [x.name for x in os.scandir(dir_pth) if x.name.endswith('.bmp')]
filenames.sort()
for filename in filenames:
file_pth = tfilename(dir_pth, filename)
img = Image.open(file_pth).convert('RGB')
img = img.resize((384,384))
# print(type(img))
img_np = np.array(img)
# print(img_np.shape)
# import ipdb;ipdb.set_trace()
# print(img_np.shape)
mask = canny(img_np)
assert len(mask.shape) == 2
margin = int(mask.shape[0] * 0.05)
m, n = mask.shape
mask[m-margin:, :] = 0
mask[:margin, :] = 0
mask[:, :margin] = 0
mask[:, m-margin:] = 0
mask = Image.fromarray(mask)
mask.save(tfilename(save_pth, f"{filename[:-4]}_bg2.png"))
print("Save over: ", tfilename(save_pth, f"{filename[:-4]}_bg2.png"))
def print_normal(normal, is_gt, epoch=0, fname=None): # [0,1]
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
normal = normal * 255
normal = normal.astype(np.uint8)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"normal_"+subtitle+"/nor_"+generate_name()+".jpg")
cv2.imwrite(fname, normal)
def print_depth(depth, is_gt, epoch=0, fname=None): # [0,1]
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
depth = depth * 255
depth = depth.astype(np.uint8)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"depth_"+subtitle+"/depth_"+generate_name()+".jpg")
cv2.imwrite(fname, depth)
def print_back(background, is_gt, epoch=0, fname=None): # [0,1]
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
background = background * 255
background = background.astype(np.uint8)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"bg_"+subtitle+"/bg_"+generate_name()+".jpg")
cv2.imwrite(fname, background)
def print_exr(cmap, is_gt, epoch=0, fname=None): # [0,1]
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
cmap = cmap * 255
cmap = cmap.astype(np.uint8)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"exr_"+subtitle+"/cmap_"+generate_name()+".jpg")
cv2.imwrite(fname, cmap)
def print_uv(uv, is_gt, epoch=0, fname=None): # [0,1]
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
w,h,c = uv.shape
uv = uv*255
uv = uv.astype(np.uint8)
bb = np.zeros((w,h,1))
uv = np.concatenate([uv, bb], axis=2)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"uv_"+subtitle+"/uv_"+generate_name()+".jpg")
#d("print_uv func: ")
#print(np.sum(uv[:,:,2]))
cv2.imwrite(fname, uv)
def print_bw(bw, is_gt, epoch=0, fname=None):
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
assert np.max(bw) > 1, "Value Error??? all vallues <= 1"
assert np.ndim(bw) == 3, "np.ndim Error"
# print("print_bw: bw.shape", bw.shape)
w,h,c = bw.shape
bw = bw.astype(np.uint8)
bb = np.zeros((w,h,1))
bw = np.concatenate([bw, bb], axis=2)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"bw_"+subtitle+"/bw_"+generate_name()+".jpg")
cv2.imwrite(fname, bw)
def print_deform(df, is_gt, epoch=0, fname=None):
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
df = df / 2.0 + 255. / 2.
df = df.astype(np.uint8)
w,h,c = df.shape
bb = np.zeros((w,h,1))
df = np.concatenate([df, bb], axis=2)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"df_"+subtitle+"/df_"+generate_name()+".jpg")
cv2.imwrite(fname, df)
### For test
cv2.imwrite(tfilename("df_test/c1.jpg"), df[:,:,0])
cv2.imwrite(tfilename("df_test/c2.jpg"), df[:,:,1])
def test2():
""" Standard Edge """
dir_pth = "/home/quanquan/oneshot-landmark/dataset/Cephalometric/RawImage/TrainingData/"
save_pth = "/home/quanquan/oneshot-landmark/dataset/Cephalometric/RawImage/edge/"
filenames = [x.name for x in os.scandir(dir_pth) if x.name.endswith('.bmp')]
filenames.sort()
for filename in filenames:
file_pth = tfilename(dir_pth, filename)
img = Image.open(file_pth).convert('RGB')
img = img.resize((384,384))
# print(type(img))
img_np = np.array(img)
# print(img_np.shape)
mask = canny(img_np)
mask = Image.fromarray(mask)
mask.save(tfilename(save_pth, f"{filename[:-4]}_bg.png"))
print("Save over: ", tfilename(save_pth, f"{filename[:-4]}_bg.png"))
def print_deform_from_bw(bw, is_gt, epoch=0, fname=None):
imsize = bw.shape[1]
x = np.arange(imsize)
y = np.arange(imsize)
xi, yi = np.meshgrid(x, y)
bw[:,:,0] = bw[:,:,0] - xi
bw[:,:,1] = bw[:,:,1] - yi
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
assert np.max(bw) > 1, "Value Error??? all vallues <= 1"
assert np.ndim(bw) == 3, "np.ndim Error"
# print("print_bw: bw.shape", bw.shape)
bw = bw.astype(np.uint8)
w,h,c = bw.shape
bb = np.zeros((w,h,1))
bw = np.concatenate([bw, bb], axis=2)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"bw_"+subtitle+"/bw_"+generate_name()+".jpg")
cv2.imwrite(fname, bw)
def test(self, logger, config, args):
model = Learner(config, logger)
epoch = args.epoch
pth = tfilename(config['runs_dir'], f"model_epoch_{epoch}.pth")
model.load(pth)
model.cuda()
tester_train = Tester(logger, config, mode="Train")
tester_test = Tester(logger, config, mode="Test1+2")
logger.info(f"Dataset Training")
tester_train.test(model)
logger.info(f"Dataset Test 1+2")
tester_test.test(model)
def init_dirs(config):
for dirname in config['basic_dirs']:
tdir(dirname)
# Make __init__.py for each dir
make_file(tfilename(dirname, '__init__.py'))
def print_ab(ab, is_gt, epoch=0, fname=None):
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
ab = ab.astype(np.uint8)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"ab_"+subtitle+"/ab_"+generate_name()+".jpg")
cv2.imwrite(fname, ab)
def print_ori_dewarp(ori, is_gt, epoch=0, fname=None):
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"ori_dewarp"+subtitle+"/bw_uv_"+generate_name()+".jpg")
print_ori(ori, is_gt, epoch=epoch, fname=fname)
def print_ori(ori, is_gt, epoch=0, fname=None):
subtitle = "gt" if is_gt else "pred"
epoch_text = "epoch{}".format(epoch)
ori = ori.astype(np.uint8)
fname = tfilename(fname) if fname is not None else tfilename("imgshow",epoch_text,"ori_"+subtitle+"/ori_"+generate_name()+".jpg")
cv2.imwrite(fname, ori)
img_rgb = np.round(img_rgb)
s_x = s_x[mask]
s_y = s_y[mask]
index = np.argwhere(mask)
t_y = index[:, 0] # t_y and t_x is a map
t_x = index[:, 1]
# x = np.arange(expand_size)
# y = np.arange(expand_size)
# xi, yi = np.meshgrid(x, y)
mesh = np.zeros((expand_size, expand_size))
def test_new_img(img_path, output_name):
# cv2.imread()
uv = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) #[0,1]
uv = uv[:,:,1:]
print_img_auto(uv, "uv", fname=output_name)
if __name__ == "__main__":
import os
from tutils import tfilename
dirname = "/home1/quanquan/datasets/generate/mesh_film_small/uv"
for x in os.scandir(dirname):
if x.name.endswith("exr"):
test_new_img(x.path, tfilename("test_img_old", x.name[:-4]+".jpg"))
print(x.name)
