import glob
import os
import random
import albumentations
from albumentations.pytorch.transforms import ToTensorV2
import cv2
import numpy as np
import pandas as pd
import PIL
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from matplotlib import pyplot as plt
from PIL import Image
from torchvision import transforms

import dataPreprocessing
import typing
from typing import List


class WheatDataset(torch.utils.data.Dataset):
    """Global wheat dataset for dataloader.

    Attributes:
        image_ids: list of file names, ['fe133ccb4', ...]
        image_dir: data path, data/train/ or data/test/
        transforms: preprocessing for images from torchvision.
        target_transforms: transform target

    Returns:
        image: PIL RGB format.
        bboxes: [N, 4] x1, y1, x2, y2
        labels: [N, ] [1, ..., 1]
        areas: [N, ] areas float
    """

    def __init__(self, image_ids, image_dir, transforms=None, target_transforms=None):
        super(WheatDataset, self).__init__()
        self.image_ids = image_ids
        self.transforms = transforms
        self.target_transforms = target_transforms
        self.img_dir = image_dir
        meta_data = dataPreprocessing.dataPreprocess(image_dir).data_df
        meta_data['x2'] = meta_data.x + meta_data.w
        meta_data['y2'] = meta_data.y + meta_data.h
        meta_data['area'] = meta_data.w * meta_data.h
        self.dataframe = meta_data.groupby('image_id')

    def __getitem__(self, index):
        image_id = self.image_ids[index]
        data = self.dataframe.get_group(
            image_id).loc[:, ['x', 'y', 'x2', 'y2', 'source', 'area']].values
        bboxes, labels, areas = data[:, :4], data[:, -2], data[:, -1]

        # bboxes = torch.from_numpy(bboxes.astype(np.float32))
        bboxes = bboxes.astype(np.int64)
        labels = torch.ones(labels.shape, dtype=torch.int64)

        # image
        img_path = os.path.join(self.img_dir, 'train', image_id + '.jpg')
        image = cv2.imread(img_path, cv2.COLOR_BGR2RGB).astype(np.float32)
        # THE TRICK
        image /= 255.0
        # The image is in HWC, we need to convert to CHW.
        if self.transforms:
            sample = {
                'image': image,
                'bboxes': bboxes,
                'labels': labels}
            sample = self.transforms(**sample)
            image = sample['image']
            bboxes = torch.stack(
                tuple(map(torch.FloatTensor, zip(*sample['bboxes'])))).permute(1, 0)
        if self.target_transforms:
            labels = self.target_transforms(labels)
        return image, bboxes, labels, image_id

    def __len__(self):
        return len(self.image_ids)


class WheatDatasetTest(torch.utils.data.Dataset):
    """Some Information about WheatDatasetTest  """

    def __init__(self, test_dir):
        super(WheatDatasetTest, self).__init__()
        # glob all the test data
        self.image_ids = glob.glob(os.path.join(test_dir, "*.jpg"))

    def __getitem__(self, index):
        img = PIL.Image.open(self.image_ids[index]).convert('RGB')
        img = torchvision.transforms.ToTensor()(img)
        return img, self.image_ids[index]

    def __len__(self):
        return len(self.image_ids)


def collate_fn(batch):
    images, bboxes, labels, imageid = tuple(zip(*batch))
    targets = []
    for box, label in zip(bboxes, labels):
        targets.append({'boxes': box, 'labels': label})
    return torch.stack(images), targets, imageid


if __name__ == "__main__":
    data_path = 'data'
    train_ids, val_ids = dataPreprocessing.dataPreprocess(
        data_path).random_split_dataset()

    # Test dataloader
    tsfm = albumentations.Compose([
        albumentations.Resize(800, 800),
        albumentations.Flip(0.5),
        ToTensorV2(p=1.0)
    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})

    dataset = WheatDataset(
        val_ids, data_path, transforms=tsfm)
    image, bbox, label, imageid = dataset[random.randint(
        0, len(dataset))]
    print(image, bbox, label)

    image = image.permute(1, 2, 0).cpu().numpy()
    bbox = bbox.numpy().astype(int)
    for box in bbox:
        cv2.rectangle(image, (box[0], box[1]),
                      (box[2], box[3]), (255, 0, 0), 3)
    plt.imshow(image.astype(int))

02650ed58dc7d4db2d15dbc69fcd679c5a52608f
<mask token>


class WheatDataset(torch.utils.data.Dataset):
    <mask token>

    def __init__(self, image_ids, image_dir, transforms=None,
        target_transforms=None):
        super(WheatDataset, self).__init__()
        self.image_ids = image_ids
        self.transforms = transforms
        self.target_transforms = target_transforms
        self.img_dir = image_dir
        meta_data = dataPreprocessing.dataPreprocess(image_dir).data_df
        meta_data['x2'] = meta_data.x + meta_data.w
        meta_data['y2'] = meta_data.y + meta_data.h
        meta_data['area'] = meta_data.w * meta_data.h
        self.dataframe = meta_data.groupby('image_id')

    def __getitem__(self, index):
        image_id = self.image_ids[index]
        data = self.dataframe.get_group(image_id).loc[:, ['x', 'y', 'x2',
            'y2', 'source', 'area']].values
        bboxes, labels, areas = data[:, :4], data[:, -2], data[:, -1]
        bboxes = bboxes.astype(np.int64)
        labels = torch.ones(labels.shape, dtype=torch.int64)
        img_path = os.path.join(self.img_dir, 'train', image_id + '.jpg')
        image = cv2.imread(img_path, cv2.COLOR_BGR2RGB).astype(np.float32)
        image /= 255.0
        if self.transforms:
            sample = {'image': image, 'bboxes': bboxes, 'labels': labels}
            sample = self.transforms(**sample)
            image = sample['image']
            bboxes = torch.stack(tuple(map(torch.FloatTensor, zip(*sample[
                'bboxes'])))).permute(1, 0)
        if self.target_transforms:
            labels = self.target_transforms(labels)
        return image, bboxes, labels, image_id

    def __len__(self):
        return len(self.image_ids)


class WheatDatasetTest(torch.utils.data.Dataset):
    """Some Information about WheatDatasetTest  """

    def __init__(self, test_dir):
        super(WheatDatasetTest, self).__init__()
        self.image_ids = glob.glob(os.path.join(test_dir, '*.jpg'))

    def __getitem__(self, index):
        img = PIL.Image.open(self.image_ids[index]).convert('RGB')
        img = torchvision.transforms.ToTensor()(img)
        return img, self.image_ids[index]

    def __len__(self):
        return len(self.image_ids)


<mask token>

<mask token>


class WheatDataset(torch.utils.data.Dataset):
    """Global wheat dataset for dataloader.

    Attributes:
        image_ids: list of file names, ['fe133ccb4', ...]
        image_dir: data path, data/train/ or data/test/
        transforms: preprocessing for images from torchvision.
        target_transforms: transform target

    Returns:
        image: PIL RGB format.
        bboxes: [N, 4] x1, y1, x2, y2
        labels: [N, ] [1, ..., 1]
        areas: [N, ] areas float
    """

    def __init__(self, image_ids, image_dir, transforms=None,
        target_transforms=None):
        super(WheatDataset, self).__init__()
        self.image_ids = image_ids
        self.transforms = transforms
        self.target_transforms = target_transforms
        self.img_dir = image_dir
        meta_data = dataPreprocessing.dataPreprocess(image_dir).data_df
        meta_data['x2'] = meta_data.x + meta_data.w
        meta_data['y2'] = meta_data.y + meta_data.h
        meta_data['area'] = meta_data.w * meta_data.h
        self.dataframe = meta_data.groupby('image_id')

    def __getitem__(self, index):
        image_id = self.image_ids[index]
        data = self.dataframe.get_group(image_id).loc[:, ['x', 'y', 'x2',
            'y2', 'source', 'area']].values
        bboxes, labels, areas = data[:, :4], data[:, -2], data[:, -1]
        bboxes = bboxes.astype(np.int64)
        labels = torch.ones(labels.shape, dtype=torch.int64)
        img_path = os.path.join(self.img_dir, 'train', image_id + '.jpg')
        image = cv2.imread(img_path, cv2.COLOR_BGR2RGB).astype(np.float32)
        image /= 255.0
        if self.transforms:
            sample = {'image': image, 'bboxes': bboxes, 'labels': labels}
            sample = self.transforms(**sample)
            image = sample['image']
            bboxes = torch.stack(tuple(map(torch.FloatTensor, zip(*sample[
                'bboxes'])))).permute(1, 0)
        if self.target_transforms:
            labels = self.target_transforms(labels)
        return image, bboxes, labels, image_id

    def __len__(self):
        return len(self.image_ids)


class WheatDatasetTest(torch.utils.data.Dataset):
    """Some Information about WheatDatasetTest  """

    def __init__(self, test_dir):
        super(WheatDatasetTest, self).__init__()
        self.image_ids = glob.glob(os.path.join(test_dir, '*.jpg'))

    def __getitem__(self, index):
        img = PIL.Image.open(self.image_ids[index]).convert('RGB')
        img = torchvision.transforms.ToTensor()(img)
        return img, self.image_ids[index]

    def __len__(self):
        return len(self.image_ids)


<mask token>

<mask token>


class WheatDataset(torch.utils.data.Dataset):
    """Global wheat dataset for dataloader.

    Attributes:
        image_ids: list of file names, ['fe133ccb4', ...]
        image_dir: data path, data/train/ or data/test/
        transforms: preprocessing for images from torchvision.
        target_transforms: transform target

    Returns:
        image: PIL RGB format.
        bboxes: [N, 4] x1, y1, x2, y2
        labels: [N, ] [1, ..., 1]
        areas: [N, ] areas float
    """

    def __init__(self, image_ids, image_dir, transforms=None,
        target_transforms=None):
        super(WheatDataset, self).__init__()
        self.image_ids = image_ids
        self.transforms = transforms
        self.target_transforms = target_transforms
        self.img_dir = image_dir
        meta_data = dataPreprocessing.dataPreprocess(image_dir).data_df
        meta_data['x2'] = meta_data.x + meta_data.w
        meta_data['y2'] = meta_data.y + meta_data.h
        meta_data['area'] = meta_data.w * meta_data.h
        self.dataframe = meta_data.groupby('image_id')

    def __getitem__(self, index):
        image_id = self.image_ids[index]
        data = self.dataframe.get_group(image_id).loc[:, ['x', 'y', 'x2',
            'y2', 'source', 'area']].values
        bboxes, labels, areas = data[:, :4], data[:, -2], data[:, -1]
        bboxes = bboxes.astype(np.int64)
        labels = torch.ones(labels.shape, dtype=torch.int64)
        img_path = os.path.join(self.img_dir, 'train', image_id + '.jpg')
        image = cv2.imread(img_path, cv2.COLOR_BGR2RGB).astype(np.float32)
        image /= 255.0
        if self.transforms:
            sample = {'image': image, 'bboxes': bboxes, 'labels': labels}
            sample = self.transforms(**sample)
            image = sample['image']
            bboxes = torch.stack(tuple(map(torch.FloatTensor, zip(*sample[
                'bboxes'])))).permute(1, 0)
        if self.target_transforms:
            labels = self.target_transforms(labels)
        return image, bboxes, labels, image_id

    def __len__(self):
        return len(self.image_ids)


class WheatDatasetTest(torch.utils.data.Dataset):
    """Some Information about WheatDatasetTest  """

    def __init__(self, test_dir):
        super(WheatDatasetTest, self).__init__()
        self.image_ids = glob.glob(os.path.join(test_dir, '*.jpg'))

    def __getitem__(self, index):
        img = PIL.Image.open(self.image_ids[index]).convert('RGB')
        img = torchvision.transforms.ToTensor()(img)
        return img, self.image_ids[index]

    def __len__(self):
        return len(self.image_ids)


def collate_fn(batch):
    images, bboxes, labels, imageid = tuple(zip(*batch))
    targets = []
    for box, label in zip(bboxes, labels):
        targets.append({'boxes': box, 'labels': label})
    return torch.stack(images), targets, imageid


<mask token>

import glob
import os
import random
import albumentations
from albumentations.pytorch.transforms import ToTensorV2
import cv2
import numpy as np
import pandas as pd
import PIL
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from matplotlib import pyplot as plt
from PIL import Image
from torchvision import transforms
import dataPreprocessing
import typing
from typing import List


class WheatDataset(torch.utils.data.Dataset):
    """Global wheat dataset for dataloader.

    Attributes:
        image_ids: list of file names, ['fe133ccb4', ...]
        image_dir: data path, data/train/ or data/test/
        transforms: preprocessing for images from torchvision.
        target_transforms: transform target

    Returns:
        image: PIL RGB format.
        bboxes: [N, 4] x1, y1, x2, y2
        labels: [N, ] [1, ..., 1]
        areas: [N, ] areas float
    """

    def __init__(self, image_ids, image_dir, transforms=None,
        target_transforms=None):
        super(WheatDataset, self).__init__()
        self.image_ids = image_ids
        self.transforms = transforms
        self.target_transforms = target_transforms
        self.img_dir = image_dir
        meta_data = dataPreprocessing.dataPreprocess(image_dir).data_df
        meta_data['x2'] = meta_data.x + meta_data.w
        meta_data['y2'] = meta_data.y + meta_data.h
        meta_data['area'] = meta_data.w * meta_data.h
        self.dataframe = meta_data.groupby('image_id')

    def __getitem__(self, index):
        image_id = self.image_ids[index]
        data = self.dataframe.get_group(image_id).loc[:, ['x', 'y', 'x2',
            'y2', 'source', 'area']].values
        bboxes, labels, areas = data[:, :4], data[:, -2], data[:, -1]
        bboxes = bboxes.astype(np.int64)
        labels = torch.ones(labels.shape, dtype=torch.int64)
        img_path = os.path.join(self.img_dir, 'train', image_id + '.jpg')
        image = cv2.imread(img_path, cv2.COLOR_BGR2RGB).astype(np.float32)
        image /= 255.0
        if self.transforms:
            sample = {'image': image, 'bboxes': bboxes, 'labels': labels}
            sample = self.transforms(**sample)
            image = sample['image']
            bboxes = torch.stack(tuple(map(torch.FloatTensor, zip(*sample[
                'bboxes'])))).permute(1, 0)
        if self.target_transforms:
            labels = self.target_transforms(labels)
        return image, bboxes, labels, image_id

    def __len__(self):
        return len(self.image_ids)


class WheatDatasetTest(torch.utils.data.Dataset):
    """Some Information about WheatDatasetTest  """

    def __init__(self, test_dir):
        super(WheatDatasetTest, self).__init__()
        self.image_ids = glob.glob(os.path.join(test_dir, '*.jpg'))

    def __getitem__(self, index):
        img = PIL.Image.open(self.image_ids[index]).convert('RGB')
        img = torchvision.transforms.ToTensor()(img)
        return img, self.image_ids[index]

    def __len__(self):
        return len(self.image_ids)


def collate_fn(batch):
    images, bboxes, labels, imageid = tuple(zip(*batch))
    targets = []
    for box, label in zip(bboxes, labels):
        targets.append({'boxes': box, 'labels': label})
    return torch.stack(images), targets, imageid


if __name__ == '__main__':
    data_path = 'data'
    train_ids, val_ids = dataPreprocessing.dataPreprocess(data_path
        ).random_split_dataset()
    tsfm = albumentations.Compose([albumentations.Resize(800, 800),
        albumentations.Flip(0.5), ToTensorV2(p=1.0)], bbox_params={'format':
        'pascal_voc', 'label_fields': ['labels']})
    dataset = WheatDataset(val_ids, data_path, transforms=tsfm)
    image, bbox, label, imageid = dataset[random.randint(0, len(dataset))]
    print(image, bbox, label)
    image = image.permute(1, 2, 0).cpu().numpy()
    bbox = bbox.numpy().astype(int)
    for box in bbox:
        cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 3
            )
    plt.imshow(image.astype(int))

import glob
import os
import random
import albumentations
from albumentations.pytorch.transforms import ToTensorV2
import cv2
import numpy as np
import pandas as pd
import PIL
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from matplotlib import pyplot as plt
from PIL import Image
from torchvision import transforms

import dataPreprocessing
import typing
from typing import List


class WheatDataset(torch.utils.data.Dataset):
    """Global wheat dataset for dataloader.

    Attributes:
        image_ids: list of file names, ['fe133ccb4', ...]
        image_dir: data path, data/train/ or data/test/
        transforms: preprocessing for images from torchvision.
        target_transforms: transform target

    Returns:
        image: PIL RGB format.
        bboxes: [N, 4] x1, y1, x2, y2
        labels: [N, ] [1, ..., 1]
        areas: [N, ] areas float
    """

    def __init__(self, image_ids, image_dir, transforms=None, target_transforms=None):
        super(WheatDataset, self).__init__()
        self.image_ids = image_ids
        self.transforms = transforms
        self.target_transforms = target_transforms
        self.img_dir = image_dir
        meta_data = dataPreprocessing.dataPreprocess(image_dir).data_df
        meta_data['x2'] = meta_data.x + meta_data.w
        meta_data['y2'] = meta_data.y + meta_data.h
        meta_data['area'] = meta_data.w * meta_data.h
        self.dataframe = meta_data.groupby('image_id')

    def __getitem__(self, index):
        image_id = self.image_ids[index]
        data = self.dataframe.get_group(
            image_id).loc[:, ['x', 'y', 'x2', 'y2', 'source', 'area']].values
        bboxes, labels, areas = data[:, :4], data[:, -2], data[:, -1]

        # bboxes = torch.from_numpy(bboxes.astype(np.float32))
        bboxes = bboxes.astype(np.int64)
        labels = torch.ones(labels.shape, dtype=torch.int64)

        # image
        img_path = os.path.join(self.img_dir, 'train', image_id + '.jpg')
        image = cv2.imread(img_path, cv2.COLOR_BGR2RGB).astype(np.float32)
        # THE TRICK
        image /= 255.0
        # The image is in HWC, we need to convert to CHW.
        if self.transforms:
            sample = {
                'image': image,
                'bboxes': bboxes,
                'labels': labels}
            sample = self.transforms(**sample)
            image = sample['image']
            bboxes = torch.stack(
                tuple(map(torch.FloatTensor, zip(*sample['bboxes'])))).permute(1, 0)
        if self.target_transforms:
            labels = self.target_transforms(labels)
        return image, bboxes, labels, image_id

    def __len__(self):
        return len(self.image_ids)


class WheatDatasetTest(torch.utils.data.Dataset):
    """Some Information about WheatDatasetTest  """

    def __init__(self, test_dir):
        super(WheatDatasetTest, self).__init__()
        # glob all the test data
        self.image_ids = glob.glob(os.path.join(test_dir, "*.jpg"))

    def __getitem__(self, index):
        img = PIL.Image.open(self.image_ids[index]).convert('RGB')
        img = torchvision.transforms.ToTensor()(img)
        return img, self.image_ids[index]

    def __len__(self):
        return len(self.image_ids)


def collate_fn(batch):
    images, bboxes, labels, imageid = tuple(zip(*batch))
    targets = []
    for box, label in zip(bboxes, labels):
        targets.append({'boxes': box, 'labels': label})
    return torch.stack(images), targets, imageid


if __name__ == "__main__":
    data_path = 'data'
    train_ids, val_ids = dataPreprocessing.dataPreprocess(
        data_path).random_split_dataset()

    # Test dataloader
    tsfm = albumentations.Compose([
        albumentations.Resize(800, 800),
        albumentations.Flip(0.5),
        ToTensorV2(p=1.0)
    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})

    dataset = WheatDataset(
        val_ids, data_path, transforms=tsfm)
    image, bbox, label, imageid = dataset[random.randint(
        0, len(dataset))]
    print(image, bbox, label)

    image = image.permute(1, 2, 0).cpu().numpy()
    bbox = bbox.numpy().astype(int)
    for box in bbox:
        cv2.rectangle(image, (box[0], box[1]),
                      (box[2], box[3]), (255, 0, 0), 3)
    plt.imshow(image.astype(int))
