add dpt

.gitignore

@@ -0,0 +1,3 @@
+.vscode
+flagged
+gradio_cached_examples

app.py

@@ -0,0 +1,42 @@
+import os
+from PIL import Image
+
+import gradio as gr
+import numpy as np
+import torch
+from transformers import AutoModelForDepthEstimation, DPTImageProcessor
+
+
+processor = DPTImageProcessor.from_pretrained(
+    "Intel/dpt-large")
+model = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-large")
+
+
+def main(image, input_size=384):
+    # prepare image for the model
+    inputs = processor(images=image, return_tensors="pt", do_resize=True, size=(
+        input_size, input_size), keep_aspect_ratio=True)
+    print(type(inputs), inputs.data["pixel_values"].shape)
+
+    # do inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_depth = outputs.predicted_depth
+
+    # interpolate to original size
+    prediction = torch.nn.functional.interpolate(predicted_depth.unsqueeze(
+        1), size=image.shape[:-1], mode="bicubic").squeeze()
+    output = prediction.cpu().numpy().copy()
+    formatted = (output * 255 / output.max()).astype("uint8")
+    depth = Image.fromarray(formatted)
+    return depth
+
+
+title = "Demo: monocular depth estimation with DPT"
+description = "This demo uses <a href='https://huggingface.co/Intel/dpt-large' target='_blank'>DPT</a> to estimate depth from monocular image."
+examples = [[f"examples/{file}"]
+            for file in os.listdir("examples") if file[0] != "."]
+
+demo = gr.Interface(fn=main, inputs=[gr.Image(label="Input Image"), gr.Slider(128, 512, value=384, label="Input Size")], outputs="image",
+                    title=title, description=description, examples=examples, cache_examples=True)
+demo.launch(debug=True, share=True)