app.py

import cv2
import numpy as np
import gradio as gr
import tempfile
from pathlib import Path
from cvzone.ColorModule import ColorFinder
from batsman import batsman_detect
from ball_detect import ball_detect

# ---------------------------------------------------
# Static detection parameters (tune these if needed)
# ---------------------------------------------------
mycolorFinder = ColorFinder(False)

# HSV range for ball – tune for your footage
hsvVals = {
    "hmin": 10,
    "smin": 44,
    "vmin": 192,
    "hmax": 125,
    "smax": 114,
    "vmax": 255,
}

# RGB range & Canny thresholds for batsman – replace with tuned values
tuned_rgb_lower = np.array([112, 0, 181])
tuned_rgb_upper = np.array([255, 255, 255])
tuned_canny_threshold1 = 100
tuned_canny_threshold2 = 200

# ---------------------------------------------------
# Helper to classify each frame event
# ---------------------------------------------------

def ball_pitch_pad(x, x_prev, prev_x_diff, y, y_prev, prev_y_diff, batLeg):
    """Return 'Pad', 'Pitch' or 'Motion' based on ball & batsman coords."""
    if x_prev == 0 and y_prev == 0:
        return "Motion", 0, 0

    if abs(x - x_prev) > 3 * abs(prev_x_diff) and abs(prev_x_diff) > 0:
        if y < batLeg:
            return "Pad", x - x_prev, y - y_prev

    if y - y_prev < 0 and prev_y_diff > 0:
        if y < batLeg:
            return "Pad", x - x_prev, y - y_prev
        else:
            return "Pitch", x - x_prev, y - y_prev

    return "Motion", x - x_prev, y - y_prev

# ---------------------------------------------------
# Main analysis routine wrapped for Gradio
# ---------------------------------------------------

def detect_lbw(video):
    """Run the LBW detector on an uploaded video, return annotated clip + verdict."""
    # Accept both dict (gradio 4.x) or str
    video_path = video if isinstance(video, (str, Path)) else video.get("name")

    cap = cv2.VideoCapture(str(video_path))
    if not cap.isOpened():
        raise ValueError("Unable to open uploaded video.")

    fps = cap.get(cv2.CAP_PROP_FPS) or 25
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    # Prepare temporary output file for annotated video
    tmpfile = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
    out_path = tmpfile.name
    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
    writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))

    # Tracking variables
    x = y = batLeg = 0
    x_prev = y_prev = 0
    prev_x_diff = prev_y_diff = 0
    lbw_detected = False

    while True:
        x_prev, y_prev = x, y
        success, img = cap.read()
        if not success:
            break

        overlay = img.copy()

        # 1️⃣ Ball detection
        _, x, y = ball_detect(img, mycolorFinder, hsvVals)
        if x and y:
            cv2.circle(overlay, (x, y), 8, (255, 0, 0), -1)

        # 2️⃣ Batsman detection
        batsmanContours = batsman_detect(
            img,
            tuned_rgb_lower,
            tuned_rgb_upper,
            tuned_canny_threshold1,
            tuned_canny_threshold2,
        )

        # Compute batsman's leg (lowest y among contours above the ball)
        current_batLeg = float("inf")
        for cnt in batsmanContours:
            if cv2.contourArea(cnt) > 5000 and y != 0 and min(cnt[:, :, 1]) < y:
                leg_candidate = max(cnt[:, :, 1])
                current_batLeg = min(current_batLeg, leg_candidate)
                cv2.drawContours(overlay, cnt, -1, (0, 255, 0), 3)
        batLeg = current_batLeg if current_batLeg != float("inf") else batLeg

        # 3️⃣ Classify the motion event for this frame
        motion_type, prev_x_diff, prev_y_diff = ball_pitch_pad(
            x, x_prev, prev_x_diff, y, y_prev, prev_y_diff, batLeg
        )

        if motion_type == "Pad":
            lbw_detected = True
            cv2.putText(
                overlay,
                "PAD CONTACT",
                (50, 80),
                cv2.FONT_HERSHEY_SIMPLEX,
                1.6,
                (0, 0, 255),
                4,
                cv2.LINE_AA,
            )
        elif motion_type == "Pitch":
            cv2.putText(
                overlay,
                "Bounced",
                (50, 80),
                cv2.FONT_HERSHEY_SIMPLEX,
                1.6,
                (0, 255, 255),
                4,
                cv2.LINE_AA,
            )

        writer.write(overlay)

    cap.release()
    writer.release()

    verdict = "✅ Potential LBW Detected!" if lbw_detected else "❌ No LBW Detected."
    return out_path, verdict

# ---------------------------------------------------
# Gradio interface
# ---------------------------------------------------

demo = gr.Interface(
    fn=detect_lbw,
    inputs=gr.Video(label="Upload cricket clip (side-on view)"),
    outputs=[
        gr.Video(label="Annotated Review"),
        gr.Textbox(label="Decision"),
    ],
    title="Automated LBW Detector",
    description=(
        "Upload a short video of the delivery. The system analyses the ball & batsman "
        "interaction frame-by-frame, overlays detections, and flags potential LBW instances."
    ),
)

if __name__ == "__main__":
    demo.launch()