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

# 自定义手掌检测任务类
class HandDetApp(AIBase):
    def __init__(self,kmodel_path,labels,model_input_size,anchors,confidence_threshold=0.2,nms_threshold=0.5,nms_option=False, strides=[8,16,32],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
        self.labels=labels
        # 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
        # 锚框,目标检测任务使用
        self.anchors=anchors
        # 特征下采样倍数
        self.strides = strides
        # NMS选项，如果为True做类间NMS,如果为False做类内NMS
        self.nms_option = nms_option
        # 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
        # Ai2d实例用于实现预处理
        self.ai2d=Ai2d(debug_mode)
        # 设置ai2d的输入输出的格式和数据类型
        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参数并应用pad操作，以确保输入图像尺寸与模型输入尺寸匹配
            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, [114, 114, 114])
            # 使用双线性插值进行resize操作，调整图像尺寸以符合模型输入要求
            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]])

    # 自定义当前任务的后处理，用于处理模型输出结果，这里使用了aicube库的anchorbasedet_post_process接口
    def postprocess(self,results):
        with ScopedTiming("postprocess",self.debug_mode > 0):
            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)
            # 返回手掌检测结果
            return dets

# 自定义手势关键点检测任务类
class HandKPDetApp(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
        # 检测模型输入分辨率
        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]]
        self.crop_params=[]
        # debug模式
        self.debug_mode=debug_mode
        # Ai2d实例用于实现预处理
        self.ai2d=Ai2d(debug_mode)
        # 设置ai2d的输入输出的格式和数据类型
        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crop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,det,input_image_size=None):
        with ScopedTiming("set preprocess config",self.debug_mode > 0):
            ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
            self.crop_params = self.get_crop_param(det)
            self.ai2d.crop(self.crop_params[0],self.crop_params[1],self.crop_params[2],self.crop_params[3])
            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]])

    # 自定义后处理，results是模型输出的array列表
    def postprocess(self,results):
        with ScopedTiming("postprocess",self.debug_mode > 0):
            results=results[0].reshape(results[0].shape[0]*results[0].shape[1])
            results_show = np.zeros(results.shape,dtype=np.int16)
            results_show[0::2] = results[0::2] * self.crop_params[3] + self.crop_params[0]
            results_show[1::2] = results[1::2] * self.crop_params[2] + self.crop_params[1]
            results_show[0::2] = results_show[0::2] * (self.display_size[0] / self.rgb888p_size[0])
            results_show[1::2] = results_show[1::2] * (self.display_size[1] / self.rgb888p_size[1])
            return results_show

    # 计算crop参数
    def get_crop_param(self,det_box):
        x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5]
        w,h= int(x2 - x1),int(y2 - y1)
        w_det = int(float(x2 - x1) * self.display_size[0] // self.rgb888p_size[0])
        h_det = int(float(y2 - y1) * self.display_size[1] // self.rgb888p_size[1])
        x_det = int(x1*self.display_size[0] // self.rgb888p_size[0])
        y_det = int(y1*self.display_size[1] // self.rgb888p_size[1])
        length = max(w, h)/2
        cx = (x1+x2)/2
        cy = (y1+y2)/2
        ratio_num = 1.26*length
        x1_kp = int(max(0,cx-ratio_num))
        y1_kp = int(max(0,cy-ratio_num))
        x2_kp = int(min(self.rgb888p_size[0]-1, cx+ratio_num))
        y2_kp = int(min(self.rgb888p_size[1]-1, cy+ratio_num))
        w_kp = int(x2_kp - x1_kp + 1)
        h_kp = int(y2_kp - y1_kp + 1)
        return [x1_kp, y1_kp, w_kp, h_kp]

# 手掌关键点检测任务
class HandKeyPointDet:
    def __init__(self,hand_det_kmodel,hand_kp_kmodel,det_input_size,kp_input_size,labels,anchors,confidence_threshold=0.25,nms_threshold=0.3,nms_option=False,strides=[8,16,32],rgb888p_size=[1280,720],display_size=[1920,1080],debug_mode=0):
        # 手掌检测模型路径
        self.hand_det_kmodel=hand_det_kmodel
        # 手掌关键点模型路径
        self.hand_kp_kmodel=hand_kp_kmodel
        # 手掌检测模型输入分辨率
        self.det_input_size=det_input_size
        # 手掌关键点模型输入分辨率
        self.kp_input_size=kp_input_size
        self.labels=labels
        # anchors
        self.anchors=anchors
        # 置信度阈值
        self.confidence_threshold=confidence_threshold
        # nms阈值
        self.nms_threshold=nms_threshold
        # nms选项
        self.nms_option=nms_option
        # 特征图对于输入的下采样倍数
        self.strides=strides
        # 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.hand_det=HandDetApp(self.hand_det_kmodel,self.labels,model_input_size=self.det_input_size,anchors=self.anchors,confidence_threshold=self.confidence_threshold,nms_threshold=self.nms_threshold,nms_option=self.nms_option,strides=self.strides,rgb888p_size=self.rgb888p_size,display_size=self.display_size,debug_mode=0)
        self.hand_kp=HandKPDetApp(self.hand_kp_kmodel,model_input_size=self.kp_input_size,rgb888p_size=self.rgb888p_size,display_size=self.display_size)
        self.hand_det.config_preprocess()

    # run函数
    def run(self,input_np):
        # 手掌检测
        det_boxes=self.hand_det.run(input_np)
        hand_res=[]
        boxes=[]
        for det_box in det_boxes:
            # 对检测到的每个手掌执行手势关键点识别
            x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5]
            w,h= int(x2 - x1),int(y2 - y1)
            # 丢弃不合理的框
            if (h<(0.1*self.rgb888p_size[1])):
                continue
            if (w<(0.25*self.rgb888p_size[0]) and ((x1<(0.03*self.rgb888p_size[0])) or (x2>(0.97*self.rgb888p_size[0])))):
                continue
            if (w<(0.15*self.rgb888p_size[0]) and ((x1<(0.01*self.rgb888p_size[0])) or (x2>(0.99*self.rgb888p_size[0])))):
                continue
            self.hand_kp.config_preprocess(det_box)
            results_show=self.hand_kp.run(input_np)
            boxes.append(det_box)
            hand_res.append(results_show)
        return boxes,hand_res

    # 绘制效果，绘制手掌关键点、检测框
    def draw_result(self,pl,dets,hand_res):
        pl.osd_img.clear()
        if dets:
            for k in range(len(dets)):
                det_box=dets[k]
                x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5]
                w,h= int(x2 - x1),int(y2 - y1)
                w_det = int(float(x2 - x1) * self.display_size[0] // self.rgb888p_size[0])
                h_det = int(float(y2 - y1) * self.display_size[1] // self.rgb888p_size[1])
                x_det = int(x1*self.display_size[0] // self.rgb888p_size[0])
                y_det = int(y1*self.display_size[1] // self.rgb888p_size[1])
                pl.osd_img.draw_rectangle(x_det, y_det, w_det, h_det, color=(255, 0, 255, 0), thickness = 2)

                results_show=hand_res[k]
                for i in range(len(results_show)/2):
                    pl.osd_img.draw_circle(results_show[i*2], results_show[i*2+1], 1, color=(255, 0, 255, 0),fill=False)
                for i in range(5):
                    j = i*8
                    if i==0:
                        R = 255; G = 0; B = 0
                    if i==1:
                        R = 255; G = 0; B = 255
                    if i==2:
                        R = 255; G = 255; B = 0
                    if i==3:
                        R = 0; G = 255; B = 0
                    if i==4:
                        R = 0; G = 0; B = 255
                    pl.osd_img.draw_line(results_show[0], results_show[1], results_show[j+2], results_show[j+3], color=(255,R,G,B), thickness = 3)
                    pl.osd_img.draw_line(results_show[j+2], results_show[j+3], results_show[j+4], results_show[j+5], color=(255,R,G,B), thickness = 3)
                    pl.osd_img.draw_line(results_show[j+4], results_show[j+5], results_show[j+6], results_show[j+7], color=(255,R,G,B), thickness = 3)
                    pl.osd_img.draw_line(results_show[j+6], results_show[j+7], results_show[j+8], results_show[j+9], color=(255,R,G,B), thickness = 3)



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]
    # 手掌检测模型路径
    hand_det_kmodel_path="/sdcard/examples/kmodel/hand_det.kmodel"
    # 手部关键点模型路径
    hand_kp_kmodel_path="/sdcard/examples/kmodel/handkp_det.kmodel"
    # 其它参数
    anchors_path="/sdcard/examples/utils/prior_data_320.bin"
    hand_det_input_size=[512,512]
    hand_kp_input_size=[256,256]
    confidence_threshold=0.2
    nms_threshold=0.5
    labels=["hand"]
    anchors = [26,27, 53,52, 75,71, 80,99, 106,82, 99,134, 140,113, 161,172, 245,276]

    # 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()
    hkd=HandKeyPointDet(hand_det_kmodel_path,hand_kp_kmodel_path,det_input_size=hand_det_input_size,kp_input_size=hand_kp_input_size,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)
    while True:
        with ScopedTiming("total",1):
            img=pl.get_frame()                      # 获取当前帧
            det_boxes,hand_res=hkd.run(img)         # 推理当前帧
            hkd.draw_result(pl,det_boxes,hand_res)  # 绘制推理结果
            pl.show_image()                         # 展示推理结果
            gc.collect()
    hkd.hand_det.deinit()
    hkd.hand_kp.deinit()
    pl.destroy()

