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

# 自定义人体关键点检测类
class PersonKeyPointApp(AIBase):
    def __init__(self,kmodel_path,model_input_size,confidence_threshold=0.2,nms_threshold=0.5,rgb888p_size=[1280,720],display_size=[1920,1080],debug_mode=0):
        super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
        self.kmodel_path=kmodel_path
        # 模型输入分辨率
        self.model_input_size=model_input_size
        # 置信度阈值设置
        self.confidence_threshold=confidence_threshold
        # nms阈值设置
        self.nms_threshold=nms_threshold
        # sensor给到AI的图像分辨率
        self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
        # 显示分辨率
        self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
        self.debug_mode=debug_mode
        #骨骼信息
        self.SKELETON = [(16, 14),(14, 12),(17, 15),(15, 13),(12, 13),(6,  12),(7,  13),(6,  7),(6,  8),(7,  9),(8,  10),(9,  11),(2,  3),(1,  2),(1,  3),(2,  4),(3,  5),(4,  6),(5,  7)]
        #肢体颜色
        self.LIMB_COLORS = [(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 255, 51,  255),(255, 255, 51,  255),(255, 255, 51,  255),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0)]
        #关键点颜色，共17个
        self.KPS_COLORS = [(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 0,   255, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255),(255, 51,  153, 255)]

        # Ai2d实例，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，您可以通过设置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
            top,bottom,left,right,_=center_pad_param(self.rgb888p_size,self.model_input_size)
            self.ai2d.pad([0,0,0,0,top,bottom,left,right], 0, [0,0,0])
            self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
            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]])

    def preprocess(self,input_np):
        with ScopedTiming("preprocess",self.debug_mode > 0):
            return [nn.from_numpy(input_np)]

    # 自定义当前任务的后处理
    def postprocess(self,results):
        with ScopedTiming("postprocess",self.debug_mode > 0):
            # 这里使用了aidemo库的person_kp_postprocess接口
            results = aidemo.person_kp_postprocess(results[0],[self.rgb888p_size[1],self.rgb888p_size[0]],self.model_input_size,self.confidence_threshold,self.nms_threshold)
            return results

    #绘制结果，绘制人体关键点
    def draw_result(self,pl,res):
        with ScopedTiming("display_draw",self.debug_mode >0):
            if res[0]:
                pl.osd_img.clear()
                kpses = res[1]
                for i in range(len(res[0])):
                    for k in range(17+2):
                        if (k < 17):
                            kps_x,kps_y,kps_s = round(kpses[i][k][0]),round(kpses[i][k][1]),kpses[i][k][2]
                            kps_x1 = int(float(kps_x) * self.display_size[0] // self.rgb888p_size[0])
                            kps_y1 = int(float(kps_y) * self.display_size[1] // self.rgb888p_size[1])
                            if (kps_s > 0):
                                pl.osd_img.draw_circle(kps_x1,kps_y1,5,self.KPS_COLORS[k],4)
                        ske = self.SKELETON[k]
                        pos1_x,pos1_y= round(kpses[i][ske[0]-1][0]),round(kpses[i][ske[0]-1][1])
                        pos1_x_ = int(float(pos1_x) * self.display_size[0] // self.rgb888p_size[0])
                        pos1_y_ = int(float(pos1_y) * self.display_size[1] // self.rgb888p_size[1])

                        pos2_x,pos2_y = round(kpses[i][(ske[1] -1)][0]),round(kpses[i][(ske[1] -1)][1])
                        pos2_x_ = int(float(pos2_x) * self.display_size[0] // self.rgb888p_size[0])
                        pos2_y_ = int(float(pos2_y) * self.display_size[1] // self.rgb888p_size[1])

                        pos1_s,pos2_s = kpses[i][(ske[0] -1)][2],kpses[i][(ske[1] -1)][2]
                        if (pos1_s > 0.0 and pos2_s >0.0):
                            pl.osd_img.draw_line(pos1_x_,pos1_y_,pos2_x_,pos2_y_,self.LIMB_COLORS[k],4)
                    gc.collect()
            else:
                pl.osd_img.clear()

if __name__=="__main__":
    # 添加显示模式，默认hdmi，可选hdmi/lcd/lt9611/st7701/hx8399/nt35516/nt35532/gc9503/aml020t/jd9852/ili9806/virt；其中hdmi默认对应lt9611，lcd默认对应st7701
    display_mode="hdmi"
    # 显示分辨率，None表示使用当前显示屏默认分辨率；使用virt时可在这里手动设置，例如[800, 480]
    display_size=None
    # k230保持不变，k230d可调整为[640,360]
    rgb888p_size = [320, 320]
    # 模型路径
    kmodel_path="/sdcard/examples/kmodel/yolov8n-pose.kmodel"
    # 其它参数设置
    confidence_threshold = 0.2
    nms_threshold = 0.5
    # 初始化PipeLine，rgb888p_size为传给AI的图像分辨率，display_size为显示分辨率
    pl=PipeLine(rgb888p_size=rgb888p_size,display_mode=display_mode, display_size=display_size)
    # 创建PipeLine，可按需传入sensor_id选择摄像头，例如pl.create(sensor_id=2)
    pl.create()
    display_size=pl.get_display_size()
    # 初始化自定义人体关键点检测实例
    person_kp=PersonKeyPointApp(kmodel_path,model_input_size=[320,320],confidence_threshold=confidence_threshold,nms_threshold=nms_threshold,rgb888p_size=rgb888p_size,display_size=display_size,debug_mode=0)
    person_kp.config_preprocess()
    while True:
        with ScopedTiming("total",1):
            # 获取当前帧数据
            img=pl.get_frame()
            # 推理当前帧
            res=person_kp.run(img)
            # 绘制结果到PipeLine的osd图像
            person_kp.draw_result(pl,res)
            # 显示当前的绘制结果
            pl.show_image()
            gc.collect()
    person_kp.deinit()
    pl.destroy()

