"""
示例 1: 基础用法 - 单传感器同步处理

本示例演示 orisys 的基本使用流程：
1. 初始化传感器（视频文件或摄像头）
2. 循环获取图像并计算形变和触碰点
3. 读取各类传感器数据
4. 可视化显示


"""
import os
os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" 
import cv2
import orisys
import numpy as np
import argparse

def flow_to_colormap(flow):
    """Visualize optical flow with hue for direction and value for magnitude."""
    if flow is None or flow.size == 0:
        return np.zeros((1, 1, 3), dtype=np.uint8)

    fx = flow[:, :, 0].astype(np.float32, copy=False)
    fy = flow[:, :, 1].astype(np.float32, copy=False)
    mag, ang = cv2.cartToPolar(fx, fy, angleInDegrees=True)

    hsv = np.zeros(flow.shape[:2] + (3,), dtype=np.uint8)
    hsv[:, :, 0] = (ang / 2).astype(np.uint8)
    hsv[:, :, 1] = 255
    if mag.max() > 0:
        hsv[:, :, 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
    return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--video","-v", type=str, default="0", help="Camera ID / Video file path")
    parser.add_argument("--config","-c", type=str, default="./config/ddjx01.json", help="Configuration file path")
    parser.add_argument("--cal","-cal", type=str, default="./config/ddjx01.npy", help="Calibration file path")
    parser.add_argument("--verbose","-verbose", type=bool, default=True, help="Print detailed information")
    args = parser.parse_args()

    # 步骤1：创建传感器对象 (必要步骤)，
    # config_name为配置文件名（内置配置文件名）或路径（自定义配置文件路径）
    # cal_path为校准文件路径, 每一个设备需要创建/使用一个校准文件

    # 方式1: 从视频文件读取
    try:
        input = int(args.video)
        is_camera = True
        sensor = orisys.Sensor(input, config_name=args.config, cal_path= args.cal, verbose=args.verbose)
    except:
        is_camera = False
        sensor = orisys.Sensor(args.video, config_name=args.config, cal_path= args.cal, verbose=args.verbose)

    print("\n按 'q' 退出程序，按 'r' 重置传感器追踪器")

    while True:
        # 步骤2：信息提取
        # 步骤 2.1: 获取并拼接图像 (必要步骤)
        sensor.get_img() 
        # 步骤 2.2: 计算形变场 (必要步骤)
        is_touched = sensor.compute_deformation(check_motion=True, threshold=6)
        # 步骤 2.3: 计算触碰点 
        if is_touched:
            sensor.compute_contact()

    
        #步骤 2.4: 读取信息
        fps, flow, vnormal, img, contour, centroid, depth_map, fnormal, fshearx, fsheary = sensor.read_info(
            sensor.info.FPS,
            sensor.info.VRAW,
            sensor.info.VNORMAL,
            sensor.info.IMG,
            sensor.info.CONTOUR,
            sensor.info.CENTROID,
            sensor.info.DEPTH,
            sensor.info.FNORMAL,
            sensor.info.FSHEARX,
            sensor.info.FSHEARY
        )
        
        flow_colormap = flow_to_colormap(vnormal)
        cv2.imshow("Optical Flow Colormap", flow_colormap)

        # 步骤 3.2: 在图像上触碰点和区域
        image_with_foe = orisys.util.draw_contact(img, contour, centroid)
        cv2.imshow("Contact Centroid", image_with_foe)

        # 步骤 3.3: 绘制深度图
        # 归一化散度值到0-255范围（使用绝对值，然后归一化）
        div_abs = depth_map
        if div_abs.max() > div_abs.min():
            div_normalized = ((div_abs - div_abs.min()) / (div_abs.max() - div_abs.min()) * 255).astype(np.uint8)
        else:
            div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
        
        # 应用颜色映射（使用JET：蓝色=低散度，红色=高散度）
        cv2.imshow("Depth Map", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))

        #步骤 3.4: 打印资料
        print(f"fps: {fps}, fnormal: {fnormal}, fshearx: {fshearx}, fsheary: {fsheary}, depth_shape: {depth_map.shape}, sensor.resolution_raw: {sensor.img_size_raw}")
            
        #步骤 3.5: 键盘控制
        key = cv2.waitKey(1) & 0xFF
        if key == ord("q"):
            print("\n退出程序...")
            break
        if key == ord("r"):
            # 重置光流追踪器（当追踪漂移时使用）
            sensor.reset()
            print("传感器已重置")
        
    # =========================================================================
    # 4. 清理资源
    # =========================================================================
    sensor.disconnect()  # 释放视频源和相关资源


if __name__ == '__main__':
    main()