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Running
on
Zero
Running
on
Zero
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| from controlnet_aux import OpenposeDetector | |
| from model_util import get_torch_device | |
| import cv2 | |
| from transformers import DPTImageProcessor, DPTForDepthEstimation | |
| device = get_torch_device() | |
| depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device) | |
| feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas") | |
| openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") | |
| def get_depth_map(image): | |
| image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda") | |
| with torch.no_grad(), torch.autocast("cuda"): | |
| depth_map = depth_estimator(image).predicted_depth | |
| depth_map = torch.nn.functional.interpolate( | |
| depth_map.unsqueeze(1), | |
| size=(1024, 1024), | |
| mode="bicubic", | |
| align_corners=False, | |
| ) | |
| depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True) | |
| depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True) | |
| depth_map = (depth_map - depth_min) / (depth_max - depth_min) | |
| image = torch.cat([depth_map] * 3, dim=1) | |
| image = image.permute(0, 2, 3, 1).cpu().numpy()[0] | |
| image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8)) | |
| return image | |
| def get_canny_image(image, t1=100, t2=200): | |
| image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) | |
| edges = cv2.Canny(image, t1, t2) | |
| return Image.fromarray(edges, "L") |