from libs.PipeLine import PipeLine, ScopedTiming
from libs.AIBase import AIBase
from libs.AI2D import Ai2d
import os
import ujson
from media.media import *
from time import *
import nncase_runtime as nn
import ulab.numpy as np
import time
import image
import aidemo
import random
import gc
import sys

# 自定义人脸检测任务类
class FaceDetApp(AIBase):
    def __init__(self,kmodel_path,model_input_size,anchors,confidence_threshold=0.25,nms_threshold=0.3,rgb888p_size=[1280,720],display_size=[1920,1080],debug_mode=0):
        super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
        # kmodel路径
        self.kmodel_path=kmodel_path
        # 检测模型输入分辨率
        self.model_input_size=model_input_size
        # 置信度阈值
        self.confidence_threshold=confidence_threshold
        # nms阈值
        self.nms_threshold=nms_threshold
        self.anchors=anchors
        # sensor给到AI的图像分辨率，宽16字节对齐
        self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
        # 视频输出VO分辨率，宽16字节对齐
        self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
        # debug模式
        self.debug_mode=debug_mode
        # 实例化Ai2d，用于实现模型预处理
        self.ai2d=Ai2d(debug_mode)
        # 设置Ai2d的输入输出格式和类型
        self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)

    # 配置预处理操作，这里使用了pad和resize，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
    def config_preprocess(self,input_image_size=None):
        with ScopedTiming("set preprocess config",self.debug_mode > 0):
            # 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，可以通过设置input_image_size自行修改输入尺寸
            ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
            # 计算padding参数，并设置padding预处理
            self.ai2d.pad(self.get_pad_param(), 0, [104,117,123])
            # 设置resize预处理
            self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
            # 构建预处理流程,参数为预处理输入tensor的shape和预处理输出的tensor的shape
            self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]],[1,3,self.model_input_size[1],self.model_input_size[0]])

    # 自定义后处理，results是模型输出的array列表，这里使用了aidemo库的face_det_post_process接口
    def postprocess(self,results):
        with ScopedTiming("postprocess",self.debug_mode > 0):
            res = aidemo.face_det_post_process(self.confidence_threshold,self.nms_threshold,self.model_input_size[0],self.anchors,self.rgb888p_size,results)
            if len(res)==0:
                return res
            else:
                return res[0]

    # 计算padding参数
    def get_pad_param(self):
        dst_w = self.model_input_size[0]
        dst_h = self.model_input_size[1]
        # 计算最小的缩放比例，等比例缩放
        ratio_w = dst_w / self.rgb888p_size[0]
        ratio_h = dst_h / self.rgb888p_size[1]
        if ratio_w < ratio_h:
            ratio = ratio_w
        else:
            ratio = ratio_h
        new_w = (int)(ratio * self.rgb888p_size[0])
        new_h = (int)(ratio * self.rgb888p_size[1])
        dw = (dst_w - new_w) / 2
        dh = (dst_h - new_h) / 2
        top = (int)(round(0))
        bottom = (int)(round(dh * 2 + 0.1))
        left = (int)(round(0))
        right = (int)(round(dw * 2 - 0.1))
        return [0,0,0,0,top, bottom, left, right]

# 自定义人脸姿态任务类
class FacePoseApp(AIBase):
    def __init__(self,kmodel_path,model_input_size,rgb888p_size=[1920,1080],display_size=[1920,1080],debug_mode=0):
        super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
        # kmodel路径
        self.kmodel_path=kmodel_path
        # 人脸姿态模型输入分辨率
        self.model_input_size=model_input_size
        # sensor给到AI的图像分辨率，宽16字节对齐
        self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
        # 视频输出VO分辨率，宽16字节对齐
        self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
        # debug模式
        self.debug_mode=debug_mode
        # 实例化Ai2d，用于实现模型预处理
        self.ai2d=Ai2d(debug_mode)
        # 设置Ai2d的输入输出格式和类型
        self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)

    # 配置预处理操作，这里使用了affine，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
    def config_preprocess(self,det,input_image_size=None):
        with ScopedTiming("set preprocess config",self.debug_mode > 0):
            # 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，可以通过设置input_image_size自行修改输入尺寸
            ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
            # 计算affine矩阵并设置affine预处理
            matrix_dst = self.get_affine_matrix(det)
            self.ai2d.affine(nn.interp_method.cv2_bilinear,0, 0, 127, 1,matrix_dst)
            # 构建预处理流程,参数为预处理输入tensor的shape和预处理输出的tensor的shape
            self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]],[1,3,self.model_input_size[1],self.model_input_size[0]])

    # 自定义后处理，results是模型输出的array列表，计算欧拉角
    def postprocess(self,results):
        with ScopedTiming("postprocess",self.debug_mode > 0):
            R,eular = self.get_euler(results[0][0])
            return R,eular

    def get_affine_matrix(self,bbox):
        # 获取仿射矩阵，用于将边界框映射到模型输入空间
        with ScopedTiming("get_affine_matrix", self.debug_mode > 1):
            # 设置缩放因子
            factor = 2.7
            # 从边界框提取坐标和尺寸
            x1, y1, w, h = map(lambda x: int(round(x, 0)), bbox[:4])
            # 模型输入大小
            edge_size = self.model_input_size[1]
            # 平移距离，使得模型输入空间的中心对准原点
            trans_distance = edge_size / 2.0
            # 计算边界框中心点的坐标
            center_x = x1 + w / 2.0
            center_y = y1 + h / 2.0
            # 计算最大边长
            maximum_edge = factor * (h if h > w else w)
            # 计算缩放比例
            scale = edge_size * 2.0 / maximum_edge
            # 计算平移参数
            cx = trans_distance - scale * center_x
            cy = trans_distance - scale * center_y
            # 创建仿射矩阵
            affine_matrix = [scale, 0, cx, 0, scale, cy]
            return affine_matrix

    def rotation_matrix_to_euler_angles(self,R):
        # 将旋转矩阵（3x3 矩阵）转换为欧拉角（pitch、yaw、roll）
        # 计算 sin(yaw)
        sy = np.sqrt(R[0, 0] ** 2 + R[1, 0] ** 2)
        if sy < 1e-6:
            # 若 sin(yaw) 过小，说明 pitch 接近 ±90 度
            pitch = np.arctan2(-R[1, 2], R[1, 1]) * 180 / np.pi
            yaw = np.arctan2(-R[2, 0], sy) * 180 / np.pi
            roll = 0
        else:
            # 计算 pitch、yaw、roll 的角度
            pitch = np.arctan2(R[2, 1], R[2, 2]) * 180 / np.pi
            yaw = np.arctan2(-R[2, 0], sy) * 180 / np.pi
            roll = np.arctan2(R[1, 0], R[0, 0]) * 180 / np.pi
        return [pitch,yaw,roll]

    def get_euler(self,data):
        # 获取旋转矩阵和欧拉角
        R = data[:3, :3].copy()
        eular = self.rotation_matrix_to_euler_angles(R)
        return R,eular

# 人脸姿态任务类
class FacePose:
    def __init__(self,face_det_kmodel,face_pose_kmodel,det_input_size,pose_input_size,anchors,confidence_threshold=0.25,nms_threshold=0.3,rgb888p_size=[1280,720],display_size=[1920,1080],debug_mode=0):
        # 人脸检测模型路径
        self.face_det_kmodel=face_det_kmodel
        # 人脸姿态模型路径
        self.face_pose_kmodel=face_pose_kmodel
        # 人脸检测模型输入分辨率
        self.det_input_size=det_input_size
        # 人脸姿态模型输入分辨率
        self.pose_input_size=pose_input_size
        # anchors
        self.anchors=anchors
        # 置信度阈值
        self.confidence_threshold=confidence_threshold
        # nms阈值
        self.nms_threshold=nms_threshold
        # sensor给到AI的图像分辨率，宽16字节对齐
        self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
        # 视频输出VO分辨率，宽16字节对齐
        self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
        # debug_mode模式
        self.debug_mode=debug_mode
        self.face_det=FaceDetApp(self.face_det_kmodel,model_input_size=self.det_input_size,anchors=self.anchors,confidence_threshold=self.confidence_threshold,nms_threshold=self.nms_threshold,rgb888p_size=self.rgb888p_size,display_size=self.display_size,debug_mode=0)
        self.face_pose=FacePoseApp(self.face_pose_kmodel,model_input_size=self.pose_input_size,rgb888p_size=self.rgb888p_size,display_size=self.display_size)
        self.face_det.config_preprocess()

    # run函数
    def run(self,input_np):
        # 人脸检测
        det_boxes=self.face_det.run(input_np)
        pose_res=[]
        for det_box in det_boxes:
            # 对检测到的每一个人脸做人脸姿态估计
            self.face_pose.config_preprocess(det_box)
            R,eular=self.face_pose.run(input_np)
            pose_res.append((R,eular))
        return det_boxes,pose_res


    # 绘制人脸姿态角效果
    def draw_result(self,pl,dets,pose_res):
        pl.osd_img.clear()
        if dets:
            draw_img_np = np.zeros((self.display_size[1],self.display_size[0],4),dtype=np.uint8)
            draw_img=image.Image(self.display_size[0], self.display_size[1], image.ARGB8888,alloc=image.ALLOC_REF,data=draw_img_np)
            line_color = np.array([255, 0, 0 ,255],dtype=np.uint8)    #bgra
            for i,det in enumerate(dets):
                # （1）获取人脸姿态矩阵和欧拉角
                projections,center_point = self.build_projection_matrix(det)
                R,euler = pose_res[i]
                # （2）遍历人脸投影矩阵的关键点，进行投影，并将结果画在图像上
                first_points = []
                second_points = []
                for pp in range(8):
                    sum_x, sum_y = 0.0, 0.0
                    for cc in range(3):
                        sum_x += projections[pp][cc] * R[cc][0]
                        sum_y += projections[pp][cc] * (-R[cc][1])
                    center_x,center_y = center_point[0],center_point[1]
                    x = (sum_x + center_x) / self.rgb888p_size[0] * self.display_size[0]
                    y = (sum_y + center_y) / self.rgb888p_size[1] * self.display_size[1]
                    x = max(0, min(x, self.display_size[0]))
                    y = max(0, min(y, self.display_size[1]))
                    if pp < 4:
                        first_points.append((x, y))
                    else:
                        second_points.append((x, y))
                first_points = np.array(first_points,dtype=np.float)
                aidemo.polylines(draw_img_np,first_points,True,line_color,2,8,0)
                second_points = np.array(second_points,dtype=np.float)
                aidemo.polylines(draw_img_np,second_points,True,line_color,2,8,0)
                for ll in range(4):
                    x0, y0 = int(first_points[ll][0]),int(first_points[ll][1])
                    x1, y1 = int(second_points[ll][0]),int(second_points[ll][1])
                    draw_img.draw_line(x0, y0, x1, y1, color = (255, 0, 0 ,255), thickness = 2)
            pl.osd_img.copy_from(draw_img)

    def build_projection_matrix(self,det):
        x1, y1, w, h = map(lambda x: int(round(x, 0)), det[:4])
        # 计算边界框中心坐标
        center_x = x1 + w / 2.0
        center_y = y1 + h / 2.0
        # 定义后部（rear）和前部（front）的尺寸和深度
        rear_width = 0.5 * w
        rear_height = 0.5 * h
        rear_depth = 0
        factor = np.sqrt(2.0)
        front_width = factor * rear_width
        front_height = factor * rear_height
        front_depth = factor * rear_width  # 使用宽度来计算深度，也可以使用高度，取决于需求
        # 定义立方体的顶点坐标
        temp = [
            [-rear_width, -rear_height, rear_depth],
            [-rear_width, rear_height, rear_depth],
            [rear_width, rear_height, rear_depth],
            [rear_width, -rear_height, rear_depth],
            [-front_width, -front_height, front_depth],
            [-front_width, front_height, front_depth],
            [front_width, front_height, front_depth],
            [front_width, -front_height, front_depth]
        ]
        projections = np.array(temp)
        # 返回投影矩阵和中心坐标
        return projections, (center_x, center_y)


if __name__=="__main__":
    # 显示模式，默认"hdmi",可以选择"hdmi"和"lcd"
    display_mode="hdmi"
    if display_mode=="hdmi":
        display_size=[1920,1080]
    else:
        display_size=[800,480]
    # 人脸检测模型路径
    face_det_kmodel_path="/sdcard/app/tests/kmodel/face_detection_320.kmodel"
    # 人脸姿态模型路径
    face_pose_kmodel_path="/sdcard/app/tests/kmodel/face_pose.kmodel"
    # 其它参数
    anchors_path="/sdcard/app/tests/utils/prior_data_320.bin"
    rgb888p_size=[1920,1080]
    face_det_input_size=[320,320]
    face_pose_input_size=[120,120]
    confidence_threshold=0.5
    nms_threshold=0.2
    anchor_len=4200
    det_dim=4
    anchors = np.fromfile(anchors_path, dtype=np.float)
    anchors = anchors.reshape((anchor_len,det_dim))

    # 初始化PipeLine，只关注传给AI的图像分辨率，显示的分辨率
    pl=PipeLine(rgb888p_size=rgb888p_size,display_size=display_size,display_mode=display_mode)
    pl.create()
    fp=FacePose(face_det_kmodel_path,face_pose_kmodel_path,det_input_size=face_det_input_size,pose_input_size=face_pose_input_size,anchors=anchors,confidence_threshold=confidence_threshold,nms_threshold=nms_threshold,rgb888p_size=rgb888p_size,display_size=display_size)
    try:
        while True:
            os.exitpoint()
            with ScopedTiming("total",1):
                img=pl.get_frame()                      # 获取当前帧
                det_boxes,pose_res=fp.run(img)          # 推理当前帧
                fp.draw_result(pl,det_boxes,pose_res)   # 绘制推理效果
                pl.show_image()                         # 展示推理效果
                gc.collect()
    except Exception as e:
        sys.print_exception(e)
    finally:
        fp.face_det.deinit()
        fp.face_pose.deinit()
        pl.destroy()

