Create app.py

app.py

@@ -0,0 +1,66 @@
+import gradio as gr
+import cv2
+import numpy as np
+import tempfile
+import os
+
+def detect_and_predict(video):
+    # Save uploaded video
+    temp_video_path = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4').name
+    with open(temp_video_path, "wb") as f:
+        f.write(video.read())
+
+    cap = cv2.VideoCapture(temp_video_path)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out_path = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4').name
+    out = cv2.VideoWriter(out_path, fourcc, 20.0, (width, height))
+
+    ball_color_lower = np.array([5, 50, 50])     # Orange/red lower
+    ball_color_upper = np.array([15, 255, 255])  # Orange/red upper
+    trajectory_points = []
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        blurred = cv2.GaussianBlur(frame, (11, 11), 0)
+        hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
+        mask = cv2.inRange(hsv, ball_color_lower, ball_color_upper)
+        mask = cv2.erode(mask, None, iterations=2)
+        mask = cv2.dilate(mask, None, iterations=2)
+
+        contours, _ = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        if contours:
+            c = max(contours, key=cv2.contourArea)
+            ((x, y), radius) = cv2.minEnclosingCircle(c)
+
+            if radius > 3:
+                trajectory_points.append((int(x), int(y)))
+                cv2.circle(frame, (int(x), int(y)), int(radius), (0, 0, 255), 2)
+
+        # Draw trajectory
+        for i in range(1, len(trajectory_points)):
+            cv2.line(frame, trajectory_points[i - 1], trajectory_points[i], (255, 0, 0), 2)
+
+        # Draw stumps line (for simplicity, fixed zone)
+        cv2.rectangle(frame, (width // 2 - 20, height - 200), (width // 2 + 20, height - 50), (0, 255, 255), 2)
+
+        out.write(frame)
+
+    cap.release()
+    out.release()
+
+    return out_path
+
+iface = gr.Interface(fn=detect_and_predict,
+                     inputs=gr.Video(label="Upload Bowling Video"),
+                     outputs=gr.Video(label="Ball Tracking Result"),
+                     title="DRS Ball Tracker",
+                     description="Detect and visualize ball trajectory for LBW simulation.")
+
+iface.launch()