import cv2
import numpy as np
import requests
from scipy.interpolate import splprep, splev

# Camera setup (replace with your camera indices or IP streams)
caps = [cv2.VideoCapture(0)]  # Add more cameras as needed

def smooth_trajectory(points):
    if len(points) < 3:
        return points
    x = [p["x"] for p in points]
    y = [p["y"] for p in points]
    tck, u = splprep([x, y], s=0)
    u_new = np.linspace(0, 1, 50)
    x_new, y_new = splev(u_new, tck)
    return [{"x": x, "y": y} for x, y in zip(x_new, y_new)]

def process_frame(frame):
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, (0, 120, 70), (10, 255, 255))  # Adjust for your ball color
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if contours:
        c = max(contours, key=cv2.contourArea)
        x, y, w, h = cv2.boundingRect(c)
        return x + w / 2, y + h / 2
    return None, None

actual_path = []
y_positions = []
pitching_detected = False
impact_detected = False
last_point = None
frame_count = 0
spin = 0

while True:
    frames = []
    for cap in caps:
        ret, frame = cap.read()
        if ret:
            frames.append(frame)

    if not frames:
        break

    # Process the first camera feed (add logic for multiple cameras)
    frame = frames[0]
    center_x, center_y = process_frame(frame)
    if center_x is not None:
        norm_x = center_x / 1280
        norm_y = center_y / 720
        current_point = (norm_x, norm_y)

        if last_point != current_point:
            actual_path.append({"x": norm_x, "y": norm_y})
            y_positions.append(norm_y)
            last_point = current_point

        if len(y_positions) > 2 and not pitching_detected:
            if y_positions[-1] < y_positions[-2] and y_positions[-2] < y_positions[-3]:
                pitching_detected = True
                pitching_x = actual_path[-2]["x"]
                pitching_y = actual_path[-2]["y"]

        if len(actual_path) > 2 and not impact_detected:
            speed_current = abs(y_positions[-1] - y_positions[-2])
            speed_prev = abs(y_positions[-2] - y_positions[-3])
            if speed_current < speed_prev * 0.3:
                impact_detected = True
                impact_x = actual_path[-1]["x"]
                impact_y = actual_path[-1]["y"]

    frame_count += 1
    if impact_detected or frame_count > 50:
        break

    cv2.imshow('Frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

for cap in caps:
    cap.release()
cv2.destroyAllWindows()

if not actual_path:
    print("No ball detected")
    exit()

if not pitching_detected:
    pitching_x = actual_path[len(actual_path)//2]["x"]
    pitching_y = actual_path[len(actual_path)//2]["y"]

if not impact_detected:
    impact_x = actual_path[-1]["x"]
    impact_y = actual_path[-1]["y"]

actual_path = smooth_trajectory(actual_path)
projected_path = [
    {"x": impact_x, "y": impact_y},
    {"x": impact_x + spin * 0.1, "y": 1.0}
]

# Send data to Hugging Face app
data = {
    'actual_path': actual_path,
    'projected_path': projected_path,
    'pitching': {'x': pitching_x, 'y': pitching_y},
    'impact': {'x': impact_x, 'y': impact_y},
    'speed': frame_count / 30 * 0.5,  # Rough speed estimate
    'spin': spin
}

# Replace with your Hugging Face Space URL
response = requests.post('https://your-username-cricket-lbw-analyzer.hf.space/analyze_data', json=data)
print(response.json())