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

# 自定义自学习分类任务类
class SelfLearningApp(AIBase):
    def __init__(self,kmodel_path,model_input_size=[224,224],confidence_threshold=0.5,rgb888p_size=[224,224],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
        # 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
        # 模型输出列表
        self.results=[]
        # features库
        self.embeddings=[]
        # features对应的标签
        self.embeddings_labels=[]
        # 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
            ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
            # 设置resize预处理
            self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
            # build预处理过程，参数为输入tensor的shape和输出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]])

    # 加载图片，将图片特征化后存入特征向量库
    def load_image(self,image_path,label):
        # 读取一张图片
        img=self.read_img(image_path)
        # 不同图片的宽高不同，因此每加载一张都要配置预处理过程
        self.config_preprocess([img.shape[2],img.shape[1]])
        # 预处理，推理，输出特征入库，特征标签入库
        tensor=self.preprocess(img)
        results=self.inference(tensor)
        self.embeddings.append(results[0][0])
        self.embeddings_labels.append(label)
        # 重置为视频流的预处理
        self.config_preprocess()
        gc.collect()

    # 从本地读入图片，并实现HWC转CHW
    def read_img(self,img_path):
        img_data = image.Image(img_path)
        img_data_rgb888=img_data.to_rgb888()
        img_hwc=img_data_rgb888.to_numpy_ref()
        shape=img_hwc.shape
        img_tmp = img_hwc.reshape((shape[0] * shape[1], shape[2]))
        img_tmp_trans = img_tmp.transpose()
        img_res=img_tmp_trans.copy()
        img_return=img_res.reshape((shape[2],shape[0],shape[1]))
        return img_return

    # 自学习任务推理流程
    def postprocess(self,results):
        with ScopedTiming("postprocess",self.debug_mode > 0):
            if len(self.embeddings)>0:
                # 计算特征向量和向量库中所有向量的最大相似度和相似向量的索引
                idx,score=self.compute_similar(results[0][0])
                gc.collect()
                # 返回分类标签和分数
                if len(self.embeddings_labels):
                    return self.embeddings_labels[idx],score
                else:
                    return "", 0.0
            else:
                return "Please add new category images...", 0.0

    # 绘制分类结果
    def draw_result(self,pl,res,score):
        with ScopedTiming("draw osd",self.debug_mode > 0):
            pl.osd_img.clear()
            mes=res+" "+str(round(score,3))
            pl.osd_img.draw_string_advanced(5,5,32,mes,color=(255,0,255,0))

    # 计算参数向量和向量库所有向量的相似度，并返回最大相似索引和对应的相似度分数
    def compute_similar(self,embedding):
        output = np.linalg.norm(embedding)
        embed_lib = np.linalg.norm(np.array(self.embeddings,dtype=np.float), axis=1)
        dot_products = np.dot(np.array(self.embeddings), embedding)
        similarities = dot_products / (embed_lib * output)
        most_similar_index=np.argmax(similarities)
        return most_similar_index,similarities[most_similar_index]



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
    # kmodel模型路径
    kmodel_path="/sdcard/examples/ai_test_kmodel/embedding.kmodel"
    # 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=[1280,720],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()
    # 自定义自学习分类器实例化
    cls=SelfLearningApp(kmodel_path,model_input_size=[224,224],rgb888p_size=[1280,720],display_size=display_size)
    # 配置预处理过程
    cls.config_preprocess()
    # 加载图片及其类别标签
    cls.load_image("/sdcard/examples/ai_test_utils/0.jpg","菠菜")
    cls.load_image("/sdcard/examples/ai_test_utils/1.jpg","菠菜")
    cls.load_image("/sdcard/examples/ai_test_utils/2.jpg","菠菜")

    cls.load_image("/sdcard/examples/ai_test_utils/3.jpg","长茄子")
    cls.load_image("/sdcard/examples/ai_test_utils/4.jpg","长茄子")
    cls.load_image("/sdcard/examples/ai_test_utils/5.jpg","长茄子")

    cls.load_image("/sdcard/examples/ai_test_utils/6.jpg","胡萝卜")
    cls.load_image("/sdcard/examples/ai_test_utils/7.jpg","胡萝卜")
    cls.load_image("/sdcard/examples/ai_test_utils/8.jpg","胡萝卜")
    while True:
        with ScopedTiming("total",1):
            # 获取当前帧
            img=pl.get_frame()
            # 获取分类结果和分数
            res,score=cls.run(img)
            # 绘制结果到PipeLine的osd图像
            cls.draw_result(pl,res,score)
            # 显示当前的绘制结果
            pl.show_image()
            gc.collect()
    cls.deinit()
    pl.destroy()

