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app.py

@@ -3,13 +3,13 @@ from transformers import DPTFeatureExtractor, DPTForDepthEstimation
 import torch
 import numpy as np
 from PIL import Image
-
-torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
+import os
+import cv2
 
 feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
 model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
 
-def process_image(image):
+def get_image_depth(image):
     # prepare image for the model
     encoding = feature_extractor(image, return_tensors="pt")
     
@@ -17,7 +17,7 @@ def process_image(image):
     with torch.no_grad():
        outputs = model(**encoding)
        predicted_depth = outputs.predicted_depth
-    
+
     # interpolate to original size
     prediction = torch.nn.functional.interpolate(
                         predicted_depth.unsqueeze(1),
@@ -29,18 +29,46 @@ def process_image(image):
     formatted = (output * 255 / np.max(output)).astype('uint8')
     img = Image.fromarray(formatted)
     return img
-    
-    return result
-    
-title = "My Demo: 1zero-shot depth estimation with DPT"
-description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of-the-art dense prediction tasks such as semantic segmentation and depth estimation."
-examples =[['cats.jpg']]
-
-iface = gr.Interface(fn=process_image, 
-                     inputs=gr.inputs.Image(type="pil"), 
-                     outputs=gr.outputs.Image(type="pil", label="predicted depth"),
-                     title=title,
-                     description=description,
-                     examples=examples,
-                     enable_queue=True)
-iface.launch(debug=True)
+
+
+def process_sequence(files):
+    file_paths = [file.name for file in files]
+    for file_path in file_paths:
+        image = Image.open(file_path)
+        depth_image = get_image_depth(image)
+        depth_image.save(os.path.join('output', os.path.basename(file_path)))
+    return file_paths, gr.Info("This is some info")
+
+title = "# Depth estimation demo"
+description = "Demo for Intel's DPT"
+
+with gr.Blocks() as iface:
+
+    gr.Markdown(title)
+    gr.Markdown(description)
+
+    with gr.Row():
+
+        with gr.Column():
+            with gr.Tab(label='Singel image'):
+                image = gr.Image(type="pil")
+                button = gr.Button(value="Get depth", interactive=True, variant="primary")
+                image_output=gr.Image(type="pil", label="predicted depth")
+        
+        with gr.Column():
+            with gr.Tab(label='Frames'):
+                file_output = gr.File(visible=False)
+                upload_button = gr.UploadButton("Select directory", file_types=["image"], file_count="directory")
+                upload_button.upload(process_sequence, upload_button, file_output)
+                
+                #output=gr.Video(label="Predicted Depth")
+                message=gr.Text(value="Check output folder for the depth frames.")
+        
+    button.click(
+        fn=get_image_depth,
+        inputs=[image],
+        outputs=[image_output]
+    )
+
+iface.queue(concurrency_count=1)
+iface.launch(debug=True, enable_queue=True)

requirements.txt

@@ -1,4 +1,4 @@
 torch
 git+https://github.com/nielsrogge/transformers.git@add_dpt_redesign#egg=transformers
 numpy
-Pillow
+Pillow