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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()
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