Update README.md

README.md

@@ -25,19 +25,30 @@ pip install -e .
 ## Usage
 
 ```python
-from promptda.promptda import PromptDA
-from promptda.utils.io_wrapper import load_image, load_depth, save_depth
+import requests
+from PIL import Image
+from transformers import PromptDepthAnythingForDepthEstimation, PromptDepthAnythingImageProcessor
 
-DEVICE = 'cuda'
-image_path = "assets/example_images/image.jpg"
-prompt_depth_path = "assets/example_images/arkit_depth.png"
-image = load_image(image_path).to(DEVICE)
-prompt_depth = load_depth(prompt_depth_path).to(DEVICE) # 192x256, ARKit LiDAR depth in meters
+url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/image.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
 
-model = PromptDA.from_pretrained("depth-anything/prompt-depth-anything-vitl").to(DEVICE).eval()
-depth = model.predict(image, prompt_depth) # HxW, depth in meters
 
-save_depth(depth, prompt_depth=prompt_depth, image=image)
+image_processor = PromptDepthAnythingImageProcessor.from_pretrained("depth-anything/prompt-depth-anything-vitl-hf")
+model = PromptDepthAnythingForDepthEstimation.from_pretrained("depth-anything/prompt-depth-anything-vitl-hf")
+
+prompt_depth_url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/arkit_depth.png?raw=true"
+prompt_depth = Image.open(requests.get(prompt_depth_url, stream=True).raw)
+
+inputs = image_processor(images=image, return_tensors="pt", prompt_depth=prompt_depth)
+with torch.no_grad():
+    outputs = model(**inputs)
+post_processed_output = image_processor.post_process_depth_estimation(
+    outputs,
+    target_sizes=[(image.height, image.width)],
+)
+
+predicted_depth = post_processed_output[0]["predicted_depth"]
+
 ```
 
 ## Citation