in src/controlnet_aux/midas/__init__.py [0:0]
def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1, depth_and_normal=False, detect_resolution=512, image_resolution=512, output_type=None):
device = next(iter(self.model.parameters())).device
if not isinstance(input_image, np.ndarray):
input_image = np.array(input_image, dtype=np.uint8)
output_type = output_type or "pil"
else:
output_type = output_type or "np"
input_image = HWC3(input_image)
input_image = resize_image(input_image, detect_resolution)
assert input_image.ndim == 3
image_depth = input_image
with torch.no_grad():
image_depth = torch.from_numpy(image_depth).float()
image_depth = image_depth.to(device)
image_depth = image_depth / 127.5 - 1.0
image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
depth = self.model(image_depth)[0]
depth_pt = depth.clone()
depth_pt -= torch.min(depth_pt)
depth_pt /= torch.max(depth_pt)
depth_pt = depth_pt.cpu().numpy()
depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
if depth_and_normal:
depth_np = depth.cpu().numpy()
x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3)
y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3)
z = np.ones_like(x) * a
x[depth_pt < bg_th] = 0
y[depth_pt < bg_th] = 0
normal = np.stack([x, y, z], axis=2)
normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)[:, :, ::-1]
depth_image = HWC3(depth_image)
if depth_and_normal:
normal_image = HWC3(normal_image)
img = resize_image(input_image, image_resolution)
H, W, C = img.shape
depth_image = cv2.resize(depth_image, (W, H), interpolation=cv2.INTER_LINEAR)
if depth_and_normal:
normal_image = cv2.resize(normal_image, (W, H), interpolation=cv2.INTER_LINEAR)
if output_type == "pil":
depth_image = Image.fromarray(depth_image)
if depth_and_normal:
normal_image = Image.fromarray(normal_image)
if depth_and_normal:
return depth_image, normal_image
else:
return depth_image