Captcha_solver / captcha_solver_hf.py
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import numpy as np
import cv2
import pickle
from keras.models import load_model
from helpers import resize_to_fit
# Ensure these files are in the same directory as your script
MODEL_FILENAME = "captcha_model.hdf5"
MODEL_LABELS_FILENAME = "model_labels.dat"
# Load the model and labels
with open(MODEL_LABELS_FILENAME, "rb") as f:
lb = pickle.load(f)
model = load_model(MODEL_FILENAME)
def solve_captcha(image):
# Convert the image to grayscale
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Add some extra padding around the image
image = cv2.copyMakeBorder(image, 20, 20, 20, 20, cv2.BORDER_REPLICATE)
# Threshold the image
thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# Find contours
contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
letter_image_regions = []
for contour in contours:
(x, y, w, h) = cv2.boundingRect(contour)
if w / h > 1.25:
half_width = int(w / 2)
letter_image_regions.append((x, y, half_width, h))
letter_image_regions.append((x + half_width, y, half_width, h))
else:
letter_image_regions.append((x, y, w, h))
if len(letter_image_regions) != 4:
return "Error: Could not identify 4 letters in the CAPTCHA"
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
predictions = []
for letter_bounding_box in letter_image_regions:
x, y, w, h = letter_bounding_box
letter_image = image[y - 2:y + h + 2, x - 2:x + w + 2]
letter_image = resize_to_fit(letter_image, 20, 20)
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
prediction = model.predict(letter_image)
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
captcha_text = "".join(predictions)
return captcha_text
# We don't need a test function here anymore, as testing will be done through the Gradio interface