Update app.py (#1)

- Update app.py (21d17ccace5a20f2b9b3a8715f3b1163db532a4b)

app.py

@@ -2,20 +2,12 @@ import cv2
 import torch
 import numpy as np
 from transformers import DPTForDepthEstimation, DPTImageProcessor
-import time
-import warnings
-import asyncio
-import json
-import websockets
-
-warnings.filterwarnings("ignore", message="It looks like you are trying to rescale already rescaled images.")
+import gradio as gr
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = DPTForDepthEstimation.from_pretrained("Intel/dpt-swinv2-tiny-256", torch_dtype=torch.float16).to(device)
 processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256")
 
-cap = cv2.VideoCapture(0)
-
 def resize_image(image, target_size=(256, 256)):
     return cv2.resize(image, target_size)
 
@@ -28,84 +20,43 @@ def manual_normalize(depth_map):
     else:
         return np.zeros_like(depth_map, dtype=np.uint8)
 
-frame_skip = 4
 color_map = cv2.applyColorMap(np.arange(256, dtype=np.uint8), cv2.COLORMAP_INFERNO)
 
-connected = set()
-
-async def broadcast(message):
-    for websocket in connected:
-        try:
-            await websocket.send(message)
-        except websockets.exceptions.ConnectionClosed:
-            connected.remove(websocket)
-
-async def handler(websocket, path):
-    connected.add(websocket)
-    try:
-        await websocket.wait_closed()
-    finally:
-        connected.remove(websocket)
-
-async def process_frames():
-    frame_count = 0
-    prev_frame_time = 0
-
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
+def process_frame(image):
+    rgb_frame = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    resized_frame = resize_image(rgb_frame)
 
-        frame_count += 1
-        if frame_count % frame_skip != 0:
-            continue
+    inputs = processor(images=resized_frame, return_tensors="pt").to(device)
+    inputs = {k: v.to(torch.float16) for k, v in inputs.items()}
 
-        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        resized_frame = resize_image(rgb_frame)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_depth = outputs.predicted_depth
 
-        inputs = processor(images=resized_frame, return_tensors="pt").to(device)
-        inputs = {k: v.to(torch.float16) for k, v in inputs.items()}
+    depth_map = predicted_depth.squeeze().cpu().numpy()
 
-        with torch.no_grad():
-            outputs = model(**inputs)
-            predicted_depth = outputs.predicted_depth
+    depth_map = np.nan_to_num(depth_map, nan=0.0, posinf=0.0, neginf=0.0)
+    depth_map = depth_map.astype(np.float32)
 
-        depth_map = predicted_depth.squeeze().cpu().numpy()
-
-        depth_map = np.nan_to_num(depth_map, nan=0.0, posinf=0.0, neginf=0.0)
-        depth_map = depth_map.astype(np.float32)
-
-        if depth_map.size == 0:
-            depth_map = np.zeros((256, 256), dtype=np.uint8)
+    if depth_map.size == 0:
+        depth_map = np.zeros((256, 256), dtype=np.uint8)
+    else:
+        if np.any(depth_map) and np.min(depth_map) != np.max(depth_map):
+            depth_map = cv2.normalize(depth_map, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)
         else:
-            if np.any(depth_map) and np.min(depth_map) != np.max(depth_map):
-                depth_map = cv2.normalize(depth_map, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)
-            else:
-                depth_map = np.zeros_like(depth_map, dtype=np.uint8)
-
-        if np.all(depth_map == 0):
-            depth_map = manual_normalize(depth_map)
-
-        data = {
-            'depthMap': depth_map.tolist(),
-            'rgbFrame': rgb_frame.tolist()
-        }
-        
-        await broadcast(json.dumps(data))
-
-        new_frame_time = time.time()
-        fps = 1 / (new_frame_time - prev_frame_time)
-        prev_frame_time = new_frame_time
+            depth_map = np.zeros_like(depth_map, dtype=np.uint8)
 
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
+    if np.all(depth_map == 0):
+        depth_map = manual_normalize(depth_map)
 
-    cap.release()
-    cv2.destroyAllWindows()
+    depth_map_colored = cv2.applyColorMap(depth_map, color_map)
+    return cv2.cvtColor(depth_map_colored, cv2.COLOR_BGR2RGB)
 
-async def main():
-    server = await websockets.serve(handler, "localhost", 8765)
-    await asyncio.gather(server.wait_closed(), process_frames())
+interface = gr.Interface(
+    fn=process_frame,
+    inputs=gr.Image(source="webcam", streaming=True),
+    outputs="image",
+    live=True
+)
 
-if __name__ == "__main__":
-    asyncio.run(main())
+interface.launch()