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"""
Command Line Interface for GreggRecognition
"""
import argparse
import os
import sys
from pathlib import Path
from typing import List
from .recognizer import GreggRecognition
def parse_args():
"""Parse command line arguments"""
parser = argparse.ArgumentParser(
description="Recognize Gregg shorthand from images",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"input",
help="Input image file or directory containing images"
)
parser.add_argument(
"--model",
choices=["image_to_text", "seq2seq"],
default="image_to_text",
help="Model type to use for recognition"
)
parser.add_argument(
"--model-path",
help="Path to custom model weights file"
)
parser.add_argument(
"--output",
help="Output file to save results (default: print to stdout)"
)
parser.add_argument(
"--device",
choices=["auto", "cpu", "cuda"],
default="auto",
help="Device to use for inference"
)
parser.add_argument(
"--batch-size",
type=int,
default=8,
help="Batch size for processing multiple images"
)
parser.add_argument(
"--beam-size",
type=int,
default=1,
help="Beam size for beam search (image_to_text model only)"
)
parser.add_argument(
"--temperature",
type=float,
default=1.0,
help="Temperature for sampling (seq2seq model only)"
)
parser.add_argument(
"--extensions",
nargs="+",
default=[".jpg", ".jpeg", ".png", ".bmp", ".tiff"],
help="Image file extensions to process when input is a directory"
)
parser.add_argument(
"--verbose",
action="store_true",
help="Enable verbose output"
)
return parser.parse_args()
def find_image_files(input_path: str, extensions: List[str]) -> List[str]:
"""Find all image files in a directory"""
input_path = Path(input_path)
if input_path.is_file():
return [str(input_path)]
elif input_path.is_dir():
image_files = []
for ext in extensions:
pattern = f"*{ext.lower()}"
image_files.extend(input_path.glob(pattern))
pattern = f"*{ext.upper()}"
image_files.extend(input_path.glob(pattern))
return [str(f) for f in sorted(set(image_files))]
else:
raise FileNotFoundError(f"Input path does not exist: {input_path}")
def main():
"""Main CLI function"""
args = parse_args()
try:
# Find input files
image_files = find_image_files(args.input, args.extensions)
if not image_files:
print(f"No image files found in: {args.input}")
sys.exit(1)
if args.verbose:
print(f"Found {len(image_files)} image file(s)")
print(f"Using model: {args.model}")
print(f"Device: {args.device}")
# Initialize recognizer
recognizer = GreggRecognition(
model_type=args.model,
device=args.device,
model_path=args.model_path
)
if args.verbose:
model_info = recognizer.get_model_info()
print(f"Model parameters: {model_info['num_parameters']:,}")
# Process images
if len(image_files) == 1:
# Single image
result = recognizer.recognize(
image_files[0],
beam_size=args.beam_size,
temperature=args.temperature
)
results = [(image_files[0], result)]
else:
# Multiple images
if args.verbose:
print(f"Processing {len(image_files)} images...")
recognized_texts = recognizer.batch_recognize(
image_files,
batch_size=args.batch_size,
beam_size=args.beam_size,
temperature=args.temperature
)
results = list(zip(image_files, recognized_texts))
# Output results
if args.output:
# Write to file
with open(args.output, 'w', encoding='utf-8') as f:
for image_path, text in results:
f.write(f"{image_path}\t{text}\n")
if args.verbose:
print(f"Results saved to: {args.output}")
else:
# Print to stdout
for image_path, text in results:
if len(image_files) == 1:
print(text)
else:
print(f"{os.path.basename(image_path)}: {text}")
except Exception as e:
print(f"Error: {str(e)}", file=sys.stderr)
sys.exit(1)
if __name__ == "__main__":
main()
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