Update app.py

app.py

@@ -2,15 +2,36 @@ import gradio as gr
 import matplotlib.pyplot as plt
 from PIL import Image
 from ultralytics import YOLO
+import cv2
+import numpy as np
+
+def image_preprocess(image):
+
+  img_height, img_width = image.shape[0:2]
+  image_converted = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+  ih, iw = [input_size, input_size] # [input_size, input_size] = [640, 640]
+  h, w, _ = image.shape # [1944, 2592]
+
+  scale = min(iw/w, ih/h) # min(0.2469, 0.3292) = 0.2469
+  nw, nh = int(scale * w), int(scale * h) # [640, 480]
+  image_resized = cv2.resize(image_converted, (nw, nh))
+
+  image_padded = np.full(shape=[ih, iw, 3], fill_value=128.0)
+  dw, dh = (iw - nw) // 2, (ih-nh) // 2 # [0, 80]
+  image_padded[dh:nh+dh, dw:nw+dw, :] = image_resized # image_padded[80:256, 32:224]
+  image_padded = image_padded / 255.
+  # image_resized = image_resized / 255.
+  image_padded = image_padded[np.newaxis, ...].astype(np.float32)
+  image_padded = np.moveaxis(image_padded, -1, 1)
+
+
+  return image_padded, img_width, img_height, image
 
 model = YOLO('best (1).pt')
 
 def response(image):
-    for i, r in enumerate(results):
-    # Plot results image
-    im_bgr = r.plot()  # BGR-order numpy array
-    im_rgb = im_bgr[..., ::-1]  # Convert BGR to RGB
-    
+    res = image_preprocess(image)
+    im_rgb = model(res)
     return im_rgb
 
 iface = gr.Interface(fn=response, inputs="image", outputs="image")