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import torch
from PIL import Image
import numpy as np
from io import BytesIO
from huggingface_hub import hf_hub_download
from pathlib import Path
from src.preprocess import read_xray, enhance_exposure, unsharp_masking, apply_clahe, resize_pil_image, increase_brightness
from src.network.model import RealESRGAN
from src.app.exceptions import InputError, ModelLoadError, PreprocessingError, InferenceError,PostprocessingError
class ModelLoadError(Exception):
pass
class InferencePipeline:
def __init__(self, config):
"""
Initialize the inference pipeline using configuration.
Args:
config: Configuration dictionary.
"""
self.config = config
preferred_device = config["model"].get("device", "cuda")
if preferred_device == "cuda" and not torch.cuda.is_available():
print("[Warning] CUDA requested but not available. Falling back to CPU.")
self.device = "cpu"
else:
self.device = preferred_device
self.scale = config["model"].get("scale", 4)
model_source = config["model"].get("source", "local")
self.model = RealESRGAN(self.device, scale=self.scale)
print(f"Using device: {self.device}")
try:
if model_source == "huggingface":
repo_id = config["model"]["repo_id"]
filename = config["model"]["filename"]
local_path = hf_hub_download(repo_id=repo_id, filename=filename)
self.load_weights(local_path)
else:
local_path = config["model"]["weights"]
self.load_weights(local_path)
except Exception as e:
raise ModelLoadError(f"Failed to load the model: {str(e)}")
def load_weights(self, model_weights):
"""
Load the model weights.
Args:
model_weights: Path to the model weights file.
"""
try:
self.model.load_weights(model_weights)
except FileNotFoundError:
raise ModelLoadError(f"Model weights not found at '{model_weights}'.")
except Exception as e:
raise ModelLoadError(f"Error loading weights: {str(e)}")
def preprocess(self, image_path_or_bytes, apply_pre_contrast_adjustment=True, is_dicom=False):
"""
Preprocess the input image.
Args:
image_path: Path to the input image file.
is_dicom: Boolean indicating if the input is a DICOM file.
Returns:
PIL Image: Preprocessed image.
"""
try:
if is_dicom:
img = read_xray(image_path_or_bytes)
else:
img = Image.open(image_path_or_bytes)
if apply_pre_contrast_adjustment:
img = enhance_exposure(np.array(img))
if isinstance(img,np.ndarray):
img = Image.fromarray(((img / np.max(img))*255).astype(np.uint8))
if img.mode not in ['RGB']:
img = img.convert('RGB')
img = unsharp_masking(
img,
self.config["preprocessing"]["unsharping_mask"].get("kernel_size", 7),
self.config["preprocessing"]["unsharping_mask"].get("strength", 2)
)
img = increase_brightness(
img,
self.config["preprocessing"]["brightness"].get("factor", 1.2),
)
if img.mode not in ['RGB']:
img = img.convert('RGB')
return img, img.size
except Exception as e:
raise PreprocessingError(f"Error during preprocessing: {str(e)}")
def postprocess(self, image_array):
"""
Postprocess the output from the model.
Args:
image_array: PIL.Image output from the model.
Returns:
PIL Image: Postprocessed image.
"""
try:
return apply_clahe(
image_array,
self.config["postprocessing"]["clahe"].get("clipLimit", 2.0),
tuple(self.config["postprocessing"]["clahe"].get("tileGridSize", [16, 16]))
)
except Exception as e:
raise PostprocessingError(f"Error during postprocessing: {str(e)}")
def is_dicom(self, file_path_or_bytes):
"""
Check if the input file is a DICOM file.
Args:
file_path_or_bytes (str or bytes or BytesIO): Path to the file, byte content, or BytesIO object.
Returns:
bool: True if the file is a DICOM file, False otherwise.
"""
try:
if isinstance(file_path_or_bytes, str):
# Check the file extension
file_extension = Path(file_path_or_bytes).suffix.lower()
if file_extension in ['.dcm', '.dicom']:
return True
# Open the file and check the header
with open(file_path_or_bytes, 'rb') as file:
header = file.read(132)
return header[-4:] == b'DICM'
elif isinstance(file_path_or_bytes, BytesIO):
file_path_or_bytes.seek(0)
header = file_path_or_bytes.read(132)
file_path_or_bytes.seek(0) # Reset the stream position
return header[-4:] == b'DICM'
elif isinstance(file_path_or_bytes, bytes):
header = file_path_or_bytes[:132]
return header[-4:] == b'DICM'
except Exception as e:
print(f"Error during DICOM validation: {e}")
return False
return False
def validate_input(self, input_data):
"""
Validate the input data to ensure it is suitable for processing.
Args:
input_data: Path to the input file, bytes content, or BytesIO object.
Returns:
bool: True if the input is valid, raises InputError otherwise.
"""
if isinstance(input_data, str):
# Check if the file exists
if not Path(input_data).exists():
raise InputError(f"Input file '{input_data}' does not exist.")
# Check if the file type is supported
file_extension = Path(input_data).suffix.lower()
if file_extension not in ['.png', '.jpeg', '.jpg', '.dcm', '.dicom']:
raise InputError(f"Unsupported file type '{file_extension}'. Supported types are PNG, JPEG, and DICOM.")
elif isinstance(input_data, BytesIO):
# Check if BytesIO data is not empty
if input_data.getbuffer().nbytes == 0:
raise InputError("Input BytesIO data is empty.")
else:
raise InputError("Unsupported input type. Must be a file path, byte content, or BytesIO object.")
return True
def infer(self, input_image):
"""
Perform inference on a single image.
Args:
input_image: PIL Image to be processed.
Returns:
PIL Image: Super-resolved image.
"""
try:
# Perform inference
input_array = np.array(input_image)
sr_array = self.model.predict(input_array)
return sr_array
except Exception as e:
raise InferenceError(f"Error during inference: {str(e)}")
def run(self, input_path, apply_pre_contrast_adjustment = True, apply_clahe_postprocess=False, return_original_size = True):
"""
Process a single image and save the output.
Args:
input_path: Path to the input image file.
is_dicom: Boolean indicating if the input is a DICOM file.
apply_clahe_postprocess: Boolean indicating if CLAHE should be applied post-processing.
"""
# Validate the input
self.validate_input(input_path)
is_dicom =self.is_dicom(input_path)
img, original_size = self.preprocess(input_path, is_dicom=is_dicom, apply_pre_contrast_adjustment = apply_pre_contrast_adjustment)
if img is None:
raise InputError(f"Invalid Input")
sr_image = self.infer(img)
if apply_clahe_postprocess:
sr_image = self.postprocess(sr_image)
if return_original_size:
sr_image = resize_pil_image(sr_image, target_shape = original_size)
return sr_image |