remove tomatoes video file

.gitignore

@@ -1,6 +1,6 @@
 flagged/
 *.png
 *.jpg
-*.mp4
 *.mkv
-gradio_cached_examples/
+gradio_cached_examples/
+venv/

app.py

@@ -33,20 +33,49 @@ model = YOLO('best.pt')
 path  = [['image_0.jpg'], ['image_1.jpg']]
 video_path = [['video.mp4']]
 
+
+
+
 def show_preds_image(image_path):
     image = cv2.imread(image_path)
     outputs = model.predict(source=image_path)
     results = outputs[0].cpu().numpy()
-    for i, det in enumerate(results.boxes.xyxy):
+
+    # Print the detected objects' information (class, coordinates, and probability)
+    box = results[0].boxes
+    names = model.model.names
+    boxes = results.boxes
+
+    for box, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
+
+        x1, y1, x2, y2 = map(int, box)
+
+        class_name = names[int(cls)]
+        print(class_name, "class_name", class_name.lower() == 'ripe')
+        if class_name.lower() == 'ripe':
+            color = (0, 0, 255)  # Red for ripe
+        else:
+            color = (0, 255, 0)  # Green for unripe
+
+        # Draw rectangle around object
         cv2.rectangle(
             image,
-            (int(det[0]), int(det[1])),
-            (int(det[2]), int(det[3])),
-            color=(0, 0, 255),
+            (x1, y1),
+            (x2, y2),
+            color=color,
             thickness=2,
             lineType=cv2.LINE_AA
         )
+
+        # Display class label on top of rectangle
+        label = f"{class_name.capitalize()}: {conf:.2f}"
+        cv2.putText(image, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color,  # Use the same color as the rectangle
+            2,
+            cv2.LINE_AA)
+        
+    # Convert image to RGB (Gradio expects RGB format)
     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
 
 inputs_image = [
     gr.components.Image(type="filepath", label="Input Image"),
@@ -71,19 +100,54 @@ def show_preds_video(video_path):
             frame_copy = frame.copy()
             outputs = model.predict(source=frame)
             results = outputs[0].cpu().numpy()
-            for i, det in enumerate(results.boxes.xyxy):
+            
+            boxes = results.boxes
+            confidences = boxes.conf
+            classes = boxes.cls
+            names = model.model.names
+
+            for box, conf, cls in zip(boxes.xyxy, confidences, classes):
+                x1, y1, x2, y2 = map(int, box)
+
+                # Determine color based on class
+                class_name = names[int(cls)]
+                if class_name.lower() == 'ripe':
+                    color = (0, 0, 255)  # Red for ripe
+                else:
+                    color = (0, 255, 0)  # Green for unripe
+
+                # Draw rectangle around object
                 cv2.rectangle(
                     frame_copy,
-                    (int(det[0]), int(det[1])),
-                    (int(det[2]), int(det[3])),
-                    color=(0, 0, 255),
+                    (x1, y1),
+                    (x2, y2),
+                    color=color,
                     thickness=2,
                     lineType=cv2.LINE_AA
                 )
+
+                # Display class label on top of rectangle with capitalized class name
+                label = f"{class_name.capitalize()}: {conf:.2f}"
+                cv2.putText(
+                    frame_copy,
+                    label,
+                    (x1, y1 - 10),  # Position slightly above the top of the rectangle
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.5,
+                    color,  # Use the same color as the rectangle
+                    1,
+                    cv2.LINE_AA
+                )
+
+            # Convert frame to RGB (Gradio expects RGB format)
             yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+        else:
+            break
+
+    cap.release()
 
 inputs_video = [
-    gr.components.Video(type="filepath", label="Input Video"),
+    gr.components.Video(label="Input Video"),
 
 ]
 outputs_video = [

best.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36
-size 6549796
+oid sha256:5327d18a7f7555da46b1200f7a0b6929bb5d2f91fddb02ac0c79e9c481c32e51
+size 6247395

samples/tomatoes.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:605d5fb1a532865bd93c5cc08d92b9804d8987f6aedc66c88e9ca32ad9932fc1
-size 2537426