Update app.py

app.py

@@ -1,31 +1,10 @@
 import gradio as gr
 import matplotlib.pyplot as plt
 from PIL import Image
-from ultralyticsplus import YOLO
+from ultralyticsplus import YOLO, render_result
 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')
 
@@ -33,15 +12,72 @@ def response(image):
   print(image)
   results = model(image)
   for i, r in enumerate(results):
+      
     # Plot results image
-      im_bgr = r.plot()  # BGR-order numpy array
+      im_bgr = r.plot()  
       im_rgb = im_bgr[..., ::-1]  # Convert BGR to RGB
     
     # im_rgb = Image.fromarray(im_rgb)
       
       return im_rgb
 
+def yoloV8_func(image: gr.Image = None,
+                image_size: gr.Slider = 640,
+                conf_threshold: gr.Slider = 0.4,
+                iou_threshold: gr.Slider = 0.50):
+    
+   
+    
+
+    # Perform object detection on the input image using the YOLOv8 model
+    results = model.predict(image,
+                            conf=conf_threshold,
+                            iou=iou_threshold,
+                            imgsz=image_size)
+
+    # Print the detected objects' information (class, coordinates, and probability)
+    box = results[0].boxes
+    print("Object type:", box.cls)
+    print("Coordinates:", box.xyxy)
+    print("Probability:", box.conf)
+
+    # Render the output image with bounding boxes around detected objects
+    render = render_result(model=model, image=image, result=results[0], rect_th = 4, text_th = 4)
+    return render
+
+
+inputs = [
+    gr.Image(type="filepath", label="Input Image"),
+    gr.Slider(minimum=320, maximum=1280, value=640,
+                     step=32, label="Image Size"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.25,
+                     step=0.05, label="Confidence Threshold"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.45,
+                     step=0.05, label="IOU Threshold"),
+]
+
+
+outputs = gr.Image(type="filepath", label="Output Image")
+
+title = "YOLOv8 Custom Object Detection by Uyen Nguyen"
+
+
+examples = [['one.jpg', 900, 0.5, 0.8],
+            ['two.jpg', 1152, 0.05, 0.05],
+            ['three.jpg', 1024, 0.25, 0.25],
+            ['four.jpg', 832, 0.3, 0.3]]
+
+yolo_app = gr.Interface(
+    fn=yoloV8_func,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+)
 
+# Launch the Gradio interface in debug mode with queue enabled
+yolo_app.launch(debug=True, share=True)
 
 
 iface = gr.Interface(fn=response, inputs="image", outputs="image")