import numpy as np
import cv2
import os

class SimpleDatasetLoader:
    def __init__(self, preprocessors = []):
        self.preprocessors = preprocessors

    def load(self, image_paths,verbose=-1):
        data=[]
        labels=[]

        for(i,image_path) in enumerate(image_paths):
            image=cv2.imread(image_path)
            label=image_path.split(os.path.sep)[-2]

            for p in self.preprocessors:
                image=p.preprocess(image)

            data.append(image)
            labels.append(label)  

            if verbose>0 and i>0 and (i+1) % verbose ==0:
                print('[INFO] processed {}/{}'.format(i+1,len(image_paths)))
        
        return (np.array(data),np.array(labels))          
311746cd5475150b3c1e7e75a021f3aba219ce38
<mask token>


class SimpleDatasetLoader:
    <mask token>
    <mask token>

<mask token>


class SimpleDatasetLoader:
    <mask token>

    def load(self, image_paths, verbose=-1):
        data = []
        labels = []
        for i, image_path in enumerate(image_paths):
            image = cv2.imread(image_path)
            label = image_path.split(os.path.sep)[-2]
            for p in self.preprocessors:
                image = p.preprocess(image)
            data.append(image)
            labels.append(label)
            if verbose > 0 and i > 0 and (i + 1) % verbose == 0:
                print('[INFO] processed {}/{}'.format(i + 1, len(image_paths)))
        return np.array(data), np.array(labels)

<mask token>


class SimpleDatasetLoader:

    def __init__(self, preprocessors=[]):
        self.preprocessors = preprocessors

    def load(self, image_paths, verbose=-1):
        data = []
        labels = []
        for i, image_path in enumerate(image_paths):
            image = cv2.imread(image_path)
            label = image_path.split(os.path.sep)[-2]
            for p in self.preprocessors:
                image = p.preprocess(image)
            data.append(image)
            labels.append(label)
            if verbose > 0 and i > 0 and (i + 1) % verbose == 0:
                print('[INFO] processed {}/{}'.format(i + 1, len(image_paths)))
        return np.array(data), np.array(labels)

import numpy as np
import cv2
import os


class SimpleDatasetLoader:

    def __init__(self, preprocessors=[]):
        self.preprocessors = preprocessors

    def load(self, image_paths, verbose=-1):
        data = []
        labels = []
        for i, image_path in enumerate(image_paths):
            image = cv2.imread(image_path)
            label = image_path.split(os.path.sep)[-2]
            for p in self.preprocessors:
                image = p.preprocess(image)
            data.append(image)
            labels.append(label)
            if verbose > 0 and i > 0 and (i + 1) % verbose == 0:
                print('[INFO] processed {}/{}'.format(i + 1, len(image_paths)))
        return np.array(data), np.array(labels)

import numpy as np
import cv2
import os

class SimpleDatasetLoader:
    def __init__(self, preprocessors = []):
        self.preprocessors = preprocessors

    def load(self, image_paths,verbose=-1):
        data=[]
        labels=[]

        for(i,image_path) in enumerate(image_paths):
            image=cv2.imread(image_path)
            label=image_path.split(os.path.sep)[-2]

            for p in self.preprocessors:
                image=p.preprocess(image)

            data.append(image)
            labels.append(label)  

            if verbose>0 and i>0 and (i+1) % verbose ==0:
                print('[INFO] processed {}/{}'.format(i+1,len(image_paths)))
        
        return (np.array(data),np.array(labels))          