Update app.py

app.py

@@ -4,22 +4,46 @@ from utils import colorize
 from utils import get_min_and_max
 from PIL import Image
 import tempfile
+import numpy as np
 
 import json
 
 DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
 model = torch.hub.load('isl-org/ZoeDepth', "ZoeD_N", pretrained=True).to(DEVICE).eval()
 
+def split_numpy_16_bit(numpy_image_16bit):
+  result_numpy_split = np.zeros((numpy_image_16bit.shape[0], numpy_image_16bit.shape[1], 3));
+
+  i = 0;
+  for row in numpy_image_16bit:
+    j = 0
+    for pixel in row:
+      firstPart = np.array(pixel).astype(np.uint8)
+      secondPart = np.array(pixel>>8).astype(np.uint8)
+
+      result_numpy_split[i, j, 0] = firstPart
+      result_numpy_split[i, j, 1] = secondPart
+      j += 1
+    i += 1
+
+  return result_numpy_split
+    
 def predict(image):
     image.thumbnail((1024,1024))
     depth = model.infer_pil(image)
     colored_depth, vmin, vmax = colorize(depth, cmap='gray_r')
 
+    
     tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
     raw_depth = Image.fromarray((depth*256).astype('uint16'))
     raw_depth.save(tmp.name)
+
+    depth_uint16 = (depth*256).astype('uint16')
+    numpy_image_split = split_numpy_16_bit(depth_uint16)
+
+    depth_image_two_components = Image.fromarray(np.uint8(numpy_image_split)).convert('RGB')
     
-    return colored_depth, tmp.name
+    return colored_depth, tmp.name, depth_image_two_components
     
-iface = gr.Interface(fn=predict, inputs=gr.Image(label="Input Image", type='pil'), outputs=["image", "file"])
+iface = gr.Interface(fn=predict, inputs=gr.Image(label="Input Image", type='pil'), outputs=["image", "file", "image"])
 iface.launch()