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 aicube

# 自定义人体检测类
class PersonDetectionApp(AIBase):
    def __init__(self,kmodel_path,model_input_size,labels,anchors,confidence_threshold=0.2,nms_threshold=0.5,nms_option=False,strides=[8,16,32],rgb888p_size=[224,224],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
        # 标签
        self.labels=labels
        # 检测anchors设置
        self.anchors=anchors
        # 特征图降采样倍数
        self.strides=strides
        # 置信度阈值设置
        self.confidence_threshold=confidence_threshold
        # nms阈值设置
        self.nms_threshold=nms_threshold
        self.nms_option=nms_option
        # 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
        # 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]])

    # 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aicube模型的后处理接口anchorbasedet_post_preocess
            dets = aicube.anchorbasedet_post_process(results[0], results[1], results[2], self.model_input_size, self.rgb888p_size, self.strides, len(self.labels), self.confidence_threshold, self.nms_threshold, self.anchors, self.nms_option)
            return dets

    # 绘制结果
    def draw_result(self,pl,dets):
        with ScopedTiming("display_draw",self.debug_mode >0):
            if dets:
                pl.osd_img.clear()
                for det_box in dets:
                    x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5]
                    w = float(x2 - x1) * self.display_size[0] // self.rgb888p_size[0]
                    h = float(y2 - y1) * self.display_size[1] // self.rgb888p_size[1]
                    x1 = int(x1 * self.display_size[0] // self.rgb888p_size[0])
                    y1 = int(y1 * self.display_size[1] // self.rgb888p_size[1])
                    x2 = int(x2 * self.display_size[0] // self.rgb888p_size[0])
                    y2 = int(y2 * self.display_size[1] // self.rgb888p_size[1])
                    if (h<(0.1*self.display_size[0])):
                        continue
                    if (w<(0.25*self.display_size[0]) and ((x1<(0.03*self.display_size[0])) or (x2>(0.97*self.display_size[0])))):
                        continue
                    if (w<(0.15*self.display_size[0]) and ((x1<(0.01*self.display_size[0])) or (x2>(0.99*self.display_size[0])))):
                        continue
                    pl.osd_img.draw_rectangle(x1 , y1 , int(w) , int(h), color=(255, 0, 255, 0), thickness = 2)
                    pl.osd_img.draw_string_advanced( x1 , y1-50,32, " " + self.labels[det_box[0]] + " " + str(round(det_box[1],2)), color=(255,0, 255, 0))
            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 = [1280, 720]
    # 模型路径
    kmodel_path="/sdcard/examples/kmodel/person_detect_yolov5n.kmodel"
    # 其它参数设置
    confidence_threshold = 0.2
    nms_threshold = 0.6
    labels = ["person"]
    anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]

    # 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()
    # 初始化自定义人体检测实例
    person_det=PersonDetectionApp(kmodel_path,model_input_size=[640,640],labels=labels,anchors=anchors,confidence_threshold=confidence_threshold,nms_threshold=nms_threshold,nms_option=False,strides=[8,16,32],rgb888p_size=rgb888p_size,display_size=display_size,debug_mode=0)
    person_det.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_det.run(img)
            # 绘制结果到PipeLine的osd图像
            person_det.draw_result(pl,res)
            # 显示当前的绘制结果
            pl.show_image()
            gc.collect()
    person_det.deinit()
    pl.destroy()

