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import json
from PIL import Image
import pytesseract
import pandas as pd
import os
from pathlib import Path
import logging
# Configurar logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('hawb_processing.log'),
logging.StreamHandler()
]
)
logger = logging.getLogger(__name__)
def load_field_areas(coordinates_json):
"""Carga y procesa las coordenadas desde el archivo JSON"""
with open(coordinates_json, 'r') as f:
data = json.load(f)
field_areas = {}
for box in data['boxes']:
x = float(box['x'])
y = float(box['y'])
width = float(box['width'])
height = float(box['height'])
field_areas[box['label']] = {
"x1": int(x - width/2),
"y1": int(y - height/2),
"x2": int(x + width/2),
"y2": int(y + height/2)
}
return field_areas, data['width'], data['height']
def extract_text_from_area(image, area, margin=10):
"""Extrae texto de un área específica de la imagen con margen de tolerancia"""
x1 = max(0, area["x1"] - margin)
y1 = max(0, area["y1"] - margin)
x2 = min(image.width, area["x2"] + margin)
y2 = min(image.height, area["y2"] + margin)
crop = image.crop((x1, y1, x2, y2))
custom_config = r'--oem 3 --psm 6'
text = pytesseract.image_to_string(crop, lang='eng', config=custom_config).strip()
return text
def process_hawb(image_path, coordinates_json, margin=5):
"""Procesa una imagen de HAWB y extrae los campos principales"""
logger.info(f"Procesando HAWB: {image_path}")
image = Image.open(image_path)
field_areas, img_width, img_height = load_field_areas(coordinates_json)
# Ajustar imagen si es necesario
if image.size != (img_width, img_height):
image = image.resize((img_width, img_height))
# Extraer campos principales
campos = ["Shipper_name", "hawb_number", "hawb_date", "number_pieces", "gross_weight", "kg_lb"]
extracted = {"filename": os.path.basename(image_path)}
for campo in campos:
if campo in field_areas:
extracted[campo] = extract_text_from_area(image, field_areas[campo], margin)
else:
extracted[campo] = ""
return extracted
def process_hawb_batch(image_paths, coordinates_json):
"""Procesa un lote de imágenes de HAWB"""
logger.info(f"Iniciando procesamiento de {len(image_paths)} HAWB")
results = []
for image_path in image_paths:
try:
result = process_hawb(image_path, coordinates_json)
results.append(result)
except Exception as e:
logger.error(f"Error procesando {image_path}: {str(e)}")
results.append({'filename': os.path.basename(image_path)})
df = pd.DataFrame(results)
return df
def main(hawb_dir="./hawb", data_dir="./data", coordinates_json="./coordinates_HAWB.json"):
Path(data_dir).mkdir(parents=True, exist_ok=True)
image_paths = [
os.path.join(hawb_dir, f)
for f in os.listdir(hawb_dir)
if f.lower().endswith(('.jpg', '.jpeg', '.png'))
]
df = process_hawb_batch(image_paths, coordinates_json)
return df
if __name__ == "__main__":
main()
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