对于pytorch中dataloader PIL与opencv总结

我不是女神ヾ 2023-03-06 03:57 50阅读 0赞

## 1.PIL ##

pytorch中dataloader使用的是PIL读取数据,有用的就是下面几句:

from PIL import Image, ImageOps
    def read_image(file_name, format=None):
        image = Image.open(file_name)
        image = np.asarray(image)  #转为numpy的数组,只有这样才能读出这里面的数据
        image = Image.fromarray(image) #转PIL,如果要使用PIL的库做图像处理,那么必须转为PIL
    
        return image

以resize为例,看看PIL怎么处理的:

from PIL import Image, ImageOps
    import torchvision.transforms.transforms as transforms
    def read_image(file_name, , image_height, image_width):
        image = Image.open(file_name)
        image = np.asarray(image)
        image = Image.fromarray(image)
        image = transforms.Resize((image_width, image_height), Image.BICUBIC)(image)
        image = np.asarray(image)
        image = image.astype("float32").transpose(2, 0, 1)[np.newaxis]  # (1, 3, h, w)
        
        return image

## 2.opencv ##

我们自己做推理的时候,既可以使用PIL又可以使用opencv,但是还是opencv最常用,不论在python中还是c++等.支持更好.

opencv读图,转为为RGB(因为PIL也是RGB):

import cv2
    def preprocess(image_path):
        original_image = cv2.imread(image_path)
        # the model expects RGB inputs
        original_image = original_image[:, :, ::-1]
    
        return original_image

与PIL对比,resize opencv怎么做的:

def preprocess(image_path, image_height, image_width):
        original_image = cv2.imread(image_path)
        # the model expects RGB inputs
        original_image = original_image[:, :, ::-1]
    
        # Apply pre-processing to image.
        image = cv2.resize(original_image, (image_width, image_height), interpolation=cv2.INTER_CUBIC)
        image = image.astype("float32").transpose(2, 0, 1)[np.newaxis]  # (1, 3, h, w)
        
        return image

## 3.总结 ##

以fast-reid为例,两种读取方式分别做训练与推理,虽然这两种读取方式有差异,但是这个差异对最后结果的影响还是较小的.([https://github.com/michuanhaohao/reid-strong-baseline/issues/107][https_github.com_michuanhaohao_reid-strong-baseline_issues_107]).

## 参考 ##

[1.pytorch模型转为caffe模型之后mAP下降10个点][https_github.com_michuanhaohao_reid-strong-baseline_issues_107]

[https_github.com_michuanhaohao_reid-strong-baseline_issues_107]: https://github.com/michuanhaohao/reid-strong-baseline/issues/107