from pathlib import Path
import sys
import torch
import numpy
from PIL import Image, ImageFile
import matplotlib.pyplot as plt
from mnist import CNN

sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
import gpu_utils


class PredictResult:

    possibilities: torch.Tensor

    def __init__(self, possibilities: torch.Tensor):
        self.possibilities = possibilities

    def chosen_number(self) -> int:
        """获取最终选定的数字"""
        # 依然是找最大的那个index
        _, prediction = self.possibilities.max(1)
        return prediction.item()

    def number_possibilities(self) -> list[float]:
        """获取每个数字出现的概率"""
        return list(self.possibilities[0][i].item() for i in range(10))

class Predictor:
    device: torch.device
    cnn: CNN

    def __init__(self):
        self.device = gpu_utils.get_gpu_device()
        self.cnn = CNN().to(self.device)

        # 加载保存好的模型参数
        file_path = Path(__file__).resolve().parent.parent / 'models' / 'cnn.pth'
        self.cnn.load_state_dict(torch.load(file_path))

    def predict_sketchpad(self, image: list[list[bool]]) -> PredictResult:
        input = torch.Tensor(image).float().to(self.device)
        assert(input.dim() == 2)
        assert(input.size(0) == 28)
        assert(input.size(1) == 28)

        # 为了满足要求，要在第一维度挤出2下
        # 一次是灰度通道，一次是批次。
        # 相当于batch size = 1的计算
        input = input.unsqueeze(0).unsqueeze(0)
       
        # 预测
        with torch.no_grad():
            output = self.cnn(input)
            return PredictResult(output)

    def predict_image(self, image: ImageFile.ImageFile) -> PredictResult:
        # 确保图像为灰度图像，然后转换为numpy数组。
        # 注意这里的numpy数组是只读的，所以要先拷贝一份
        grayscale_image = image.convert('L')
        numpy_data = numpy.reshape(grayscale_image, (28, 28), copy=True)
        # 转换到Tensor，设置dtype并传到GPU上
        data = torch.from_numpy(numpy_data).float().to(self.device)
        # 归一化到255，又因为图像输入是白底黑字，需要做转换。
        data.div_(255.0).sub_(1).mul_(-1)

        # 同理，挤出维度并预测
        input = data.unsqueeze(0).unsqueeze(0)
        with torch.no_grad():
            output = self.cnn(input)
            return PredictResult(output)

def main():
    predictor = Predictor()

    # 遍历测试目录中的所有图片，并处理。
    test_dir = Path(__file__).resolve().parent.parent / 'test_images'
    for image_path in test_dir.glob('*.png'):
        if image_path.is_file():
            print(f'Predicting {image_path} ...')
            image = Image.open(image_path)
            rv = predictor.predict_image(image)

            print(f'Predict digit: {rv.chosen_number()}')
            plt.figure(f'Image - {image_path}')
            plt.imshow(image)
            plt.axis('on')
            plt.title(f'Predict digit: {rv.chosen_number()}')
            plt.show()


if __name__ == "__main__":
    main()