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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/xinntao/Real-ESRGAN/llms.txt

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PrefetchReader

A threaded image prefetch reader for efficient batch processing. 
from realesrgan.utils import PrefetchReader

reader = PrefetchReader(img_list, num_prefetch_queue=3)
for img in reader:
    # Process image
    pass

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Parameters

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img_list
list[str]
required
A list of image file paths to be read
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num_prefetch_queue
int
required
Number of images to prefetch in the queue

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Methods

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run()

Starts the prefetch thread. Reads images from the list and puts them in the queue.

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next()

Returns the next prefetched image from the queue. Returns: numpy.ndarray - The next image in BGR format Raises: StopIteration when no more images are available

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iter()

Returns the iterator object (self).

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Usage Example

import glob
from realesrgan.utils import PrefetchReader

# Get list of image paths
img_paths = sorted(glob.glob('inputs/*.png'))

# Create prefetch reader with queue size of 3
reader = PrefetchReader(img_paths, num_prefetch_queue=3)
reader.start()  # Start the prefetch thread

# Iterate through prefetched images
for idx, img in enumerate(reader):
    print(f'Processing image {idx}: {img.shape}')
    # Your processing logic here
The PrefetchReader uses threading to read images asynchronously, which can significantly improve performance when processing large batches of images by overlapping I/O and computation.

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IOConsumer

A threaded consumer for asynchronous image writing operations. 
from realesrgan.utils import IOConsumer
import queue

write_queue = queue.Queue()
consumer = IOConsumer(opt, write_queue, qid=0)
consumer.start()

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Parameters

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opt
object
required
Options object containing configuration parameters
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que
queue.Queue
required
Queue object for receiving write tasks
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qid
int
required
Worker ID for the consumer thread

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Methods

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run()

Runs the consumer loop. Continuously reads messages from the queue and writes images to disk. Message format: 
{
    'output': numpy.ndarray,  # Image to write
    'save_path': str          # Destination path
}
To stop the consumer, send the string 'quit' to the queue.

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Usage Example

import queue
import cv2
from realesrgan.utils import IOConsumer

# Create write queue and consumer
write_queue = queue.Queue(maxsize=10)
consumer = IOConsumer(opt, write_queue, qid=0)
consumer.start()

# Process images and queue for async writing
for img_path in image_paths:
    img = cv2.imread(img_path)
    output = process_image(img)  # Your processing
    
    # Queue the write operation
    write_queue.put({
        'output': output,
        'save_path': f'results/{img_path}'
    })

# Signal completion
write_queue.put('quit')
consumer.join()
The IOConsumer allows you to offload disk I/O operations to a separate thread, preventing write operations from blocking your main processing pipeline. This is especially useful when processing large batches of high-resolution images.

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Constants

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ROOT_DIR

The root directory of the Real-ESRGAN package. 
from realesrgan.utils import ROOT_DIR

model_path = os.path.join(ROOT_DIR, 'weights', 'model.pth')
Type: str Value: The directory containing the Real-ESRGAN package (parent directory of realesrgan/)

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