



'''simple docstring'''



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]			)		->						str:
				snake_case__      :   Optional[int]      =    fname.split(os.path.sep			)[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''   ,				UpperCamelCase__			).groups()[0]






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Tuple    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Any=None    ,			__UpperCamelCase		:						int=None			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   Dict      =    file_names
							snake_case__      :   Tuple      =    image_transform
							snake_case__      :   Optional[Any]      =    label_to_id



			def __len__(     self		:						str			)      ->      Dict:
							"""simple docstring"""





							return len(self.file_names			)







			def __getitem__(     self		:						Any    ,			__UpperCamelCase		:						str			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   str      =    self.file_names[idx]
							snake_case__      :   Tuple      =    PIL.Image.open(__UpperCamelCase			)
							snake_case__      :   Any      =    raw_image.convert('''RGB'''			)
							if self.image_transform is not None:
											snake_case__      :   int      =    self.image_transform(__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    extract_label(__UpperCamelCase			)
							if self.label_to_id is not None:
											snake_case__      :   Optional[int]      =    self.label_to_id[label]
							return {"image": image, "label": label}



def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Optional[Any]			)		->						int:
				# Initialize accelerator
				snake_case__      :   Union[str, Any]      =    Accelerator(cpu=args.cpu   ,				mixed_precision=args.mixed_precision			)

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				snake_case__      :   Any      =    config['''lr''']
				snake_case__      :   Dict      =    int(config['''num_epochs''']			)
				snake_case__      :   str      =    int(config['''seed''']			)
				snake_case__      :   Union[str, Any]      =    int(config['''batch_size''']			)
				snake_case__      :   int      =    config['''image_size''']
				if not isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    (image_size, image_size)

				# Grab all the image filenames
				snake_case__      :   str      =    [os.path.join(args.data_dir   ,				UpperCamelCase__			) for fname in os.listdir(args.data_dir			) if fname.endswith('''.jpg'''			)]

				# Build the label correspondences
				snake_case__      :   int      =    [extract_label(UpperCamelCase__			) for fname in file_names]
				snake_case__      :   Dict      =    list(set(UpperCamelCase__			)			)
				id_to_label.sort()
				snake_case__      :   Optional[int]      =    {lbl: i for i, lbl in enumerate(UpperCamelCase__			)}

				# Set the seed before splitting the data.
				np.random.seed(UpperCamelCase__			)
				torch.manual_seed(UpperCamelCase__			)
				torch.cuda.manual_seed_all(UpperCamelCase__			)

				# Split our filenames between train and validation
				snake_case__      :   Optional[int]      =    np.random.permutation(len(UpperCamelCase__			)			)
				snake_case__      :   Tuple      =    int(0.8 * len(UpperCamelCase__			)			)
				snake_case__      :   Tuple      =    random_perm[:cut]
				snake_case__      :   int      =    random_perm[cut:]

				# For training we use a simple RandomResizedCrop
				snake_case__      :   Dict      =    Compose([RandomResizedCrop(UpperCamelCase__   ,				scale=(0.5, 1.0)			), ToTensor()]			)
				snake_case__      :   Union[str, Any]      =    PetsDataset(
				    [file_names[i] for i in train_split]   ,				image_transform=UpperCamelCase__   ,				label_to_id=UpperCamelCase__			)

				# For evaluation, we use a deterministic Resize
				snake_case__      :   Optional[Any]      =    Compose([Resize(UpperCamelCase__			), ToTensor()]			)
				snake_case__      :   Optional[int]      =    PetsDataset([file_names[i] for i in eval_split]   ,				image_transform=UpperCamelCase__   ,				label_to_id=UpperCamelCase__			)

				# Instantiate dataloaders.
				snake_case__      :   int      =    DataLoader(UpperCamelCase__   ,				shuffle=UpperCamelCase__   ,				batch_size=UpperCamelCase__   ,				num_workers=4			)
				snake_case__      :   Dict      =    DataLoader(UpperCamelCase__   ,				shuffle=UpperCamelCase__   ,				batch_size=UpperCamelCase__   ,				num_workers=4			)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case__      :   Dict      =    create_model('''resnet50d'''   ,				pretrained=UpperCamelCase__   ,				num_classes=len(UpperCamelCase__			)			)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				snake_case__      :   Optional[Any]      =    model.to(accelerator.device			)

				# Freezing the base model
				for param in model.parameters():
								snake_case__      :   List[Any]      =    False
				for param in model.get_classifier().parameters():
								snake_case__      :   str      =    True

				# We normalize the batches of images to be a bit faster.
				snake_case__      :   List[Any]      =    torch.tensor(model.default_cfg['''mean''']			)[None, :, None, None].to(accelerator.device			)
				snake_case__      :   Tuple      =    torch.tensor(model.default_cfg['''std''']			)[None, :, None, None].to(accelerator.device			)

				# Instantiate optimizer
				snake_case__      :   Dict      =    torch.optim.Adam(params=model.parameters()   ,				lr=lr / 25			)

				# Instantiate learning rate scheduler
				snake_case__      :   List[str]      =    OneCycleLR(optimizer=UpperCamelCase__   ,				max_lr=UpperCamelCase__   ,				epochs=UpperCamelCase__   ,				steps_per_epoch=len(UpperCamelCase__			)			)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__      :   int      =    accelerator.prepare(
				    UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)

				# Now we train the model
				for epoch in range(UpperCamelCase__			):
								model.train()
								for step, batch in enumerate(UpperCamelCase__			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												snake_case__      :   Optional[int]      =    {k: v.to(accelerator.device			) for k, v in batch.items()}
												snake_case__      :   Optional[Any]      =    (batch['''image'''] - mean) / std
												snake_case__      :   Optional[Any]      =    model(UpperCamelCase__			)
												snake_case__      :   str      =    torch.nn.functional.cross_entropy(UpperCamelCase__   ,				batch['''label''']			)
												accelerator.backward(UpperCamelCase__			)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

								model.eval()
								snake_case__      :   List[Any]      =    0
								snake_case__      :   List[Any]      =    0
								for _, batch in enumerate(UpperCamelCase__			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												snake_case__      :   int      =    {k: v.to(accelerator.device			) for k, v in batch.items()}
												snake_case__      :   int      =    (batch['''image'''] - mean) / std
												with torch.no_grad():
																snake_case__      :   Optional[int]      =    model(UpperCamelCase__			)
												snake_case__      :   Dict      =    outputs.argmax(dim=-1			)
												snake_case__  ,      snake_case__      :   Tuple      =    accelerator.gather_for_metrics((predictions, batch['''label'''])			)
												snake_case__      :   Tuple      =    predictions == references
												num_elems += accurate_preds.shape[0]
												accurate += accurate_preds.long().sum()

								snake_case__      :   str      =    accurate.item() / num_elems
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''			)


def 							__UpperCAmelCase				(				)		->						Optional[int]:
				snake_case__      :   List[str]      =    argparse.ArgumentParser(description='''Simple example of training script.'''			)
				parser.add_argument('''--data_dir'''   ,				required=UpperCamelCase__   ,				help='''The data folder on disk.'''			)
				parser.add_argument(
				    '''--mixed_precision'''   ,				type=UpperCamelCase__   ,				default=UpperCamelCase__   ,				choices=['''no''', '''fp16''', '''bf16''', '''fp8''']   ,				help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''   ,				)
				parser.add_argument(
				    '''--checkpointing_steps'''   ,				type=UpperCamelCase__   ,				default=UpperCamelCase__   ,				help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''   ,				)
				parser.add_argument('''--cpu'''   ,				action='''store_true'''   ,				help='''If passed, will train on the CPU.'''			)
				snake_case__      :   Union[str, Any]      =    parser.parse_args()
				snake_case__      :   str      =    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(UpperCamelCase__   ,				UpperCamelCase__			)


if __name__ == "__main__":
				main()






import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_UpperCamelCase:					Tuple     =180



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  _lowerCAmelCase						=		k_size // 2
  _lowerCAmelCase	,       _lowerCAmelCase						=		np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						=		(
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(__SCREAMING_SNAKE_CASE    ) + np.square(__SCREAMING_SNAKE_CASE    )) / (2 * np.square(__SCREAMING_SNAKE_CASE    ))    )
  )
  return g



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Optional[Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		np.zeros(__SCREAMING_SNAKE_CASE    )

  for row in range(1		,				image_shape[0] - 1    ):
    for col in range(1		,				image_shape[1] - 1    ):
      _lowerCAmelCase						=		gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row, col - 1]
        _lowerCAmelCase						=		sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          _lowerCAmelCase						=		sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row + 1, col - 1]
        _lowerCAmelCase						=		sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          _lowerCAmelCase						=		sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row - 1, col]
        _lowerCAmelCase						=		sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          _lowerCAmelCase						=		sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row - 1, col - 1]
        _lowerCAmelCase						=		sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          _lowerCAmelCase						=		sobel_grad[row, col]

  return destination



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """simple docstring"""




  for row in range(1		,				image_shape[0] - 1    ):
    for col in range(1		,				image_shape[1] - 1    ):
      if destination[row, col] >= threshold_high:
        _lowerCAmelCase						=		strong
      elif destination[row, col] <= threshold_low:
        _lowerCAmelCase						=		0
      else:
        _lowerCAmelCase						=		weak



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  for row in range(1		,				image_shape[0]    ):
    for col in range(1		,				image_shape[1]    ):
      if destination[row, col] == weak:
        if 255 in (
            destination[row, col + 1],
            destination[row, col - 1],
            destination[row - 1, col],
            destination[row + 1, col],
            destination[row - 1, col - 1],
            destination[row + 1, col - 1],
            destination[row - 1, col + 1],
            destination[row + 1, col + 1],
        ):
          _lowerCAmelCase						=		strong
        else:
          _lowerCAmelCase						=		0



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  int=15		,				__SCREAMING_SNAKE_CASE					:  int=30		,				__SCREAMING_SNAKE_CASE					:  Tuple=128		,				__SCREAMING_SNAKE_CASE					:  List[Any]=255    ):






  """simple docstring"""




  _lowerCAmelCase						=		img_convolve(__SCREAMING_SNAKE_CASE		,				gen_gaussian_kernel(9		,				sigma=1.4    )    )
  # get the gradient and degree by sobel_filter
  _lowerCAmelCase	,       _lowerCAmelCase						=		sobel_filter(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		PI + np.radadeg(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		suppress_non_maximum(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  detect_high_low_threshold(
      image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  track_edge(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  return destination


if __name__ == "__main__":
       # read original image in gray mode
       _UpperCamelCase:					Optional[Any]     =cva.imread(R'../image_data/lena.jpg', 0)
       # canny edge detection
       _UpperCamelCase:					Tuple     =canny(lena)
       cva.imshow('canny', canny_destination)
       cva.waitKey(0)
