def decode(image, threshold=True):
image = image.astype(np.uint8)
# threshold
if threshold:
_, image = cv2.threshold(image, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
image = crop_letter(image.astype(np.uint8))
# Re-size the letter image to 20x20 pixels to match training data
letter_image = resize_to_fit(image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
if "_upper" in letter:
letter = letter[0:1]
if letter in classifier.FONT_MAP:
return classifier.FONT_MAP[letter]
return letter
def solve_captcha(image_file, model_data):
(model, graph, lb) = model_data
letter_images = preprocess_image(image_file)
if not letter_images:
return ""
# Create an output image and a list to hold our predicted letters
predictions = []
# loop over the letters
for letter_image in letter_images :
# Re-size the letter image to 20x20 pixels to match training data
letter_image = resize_to_fit(letter_image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
with graph.as_default():
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# Print the captcha's text
captcha_text = "".join(predictions)
return captcha_text
def prediction(lb, model_made):
captcha_image_files = list(paths.list_images("generated_captcha_images"))
captcha_image_files = np.random.choice(captcha_image_files,
size=(10, ),
replace=False)
for image_file in captcha_image_files:
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.copyMakeBorder(image, 20, 20, 20, 20, cv2.BORDER_REPLICATE)
thresh = cv2.threshold(image, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if imutils.is_cv2() else contours[1]
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:
continue
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
output = cv2.merge([image] * 3)
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_made.predict(letter_image)
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
cv2.rectangle(output, (x - 2, y - 2), (x + w + 4, y + h + 4),
(0, 255, 0), 1)
cv2.putText(output, letter, (x - 5, y - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 2)
captcha_text = "".join(predictions)
print("CAPTCHA text is: {}".format(captcha_text))
plt.imshow(output, cmap='gray', interpolation='bicubic')
plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis
plt.show()
def load_data(
img_rows,
img_cols,
):
IMAGES_FOLDER = "dataset"
MODEL_LABELS_FILENAME = "model_labels.dat"
# Resize trainging
data = []
labels = []
# loop over the input images
for image_file in paths.list_images(IMAGES_FOLDER):
image = cv2.imread(image_file)
# # Resize the letter so it fits in a 80x80 pixel box
image = resize_to_fit(image, img_rows, img_cols)
# Grab the name of the letter based on the folder it was in
label = image_file.split(os.path.sep)[-2]
# Add the letter image and it's label to our training data
data.append(image)
labels.append(label)
# scale the raw pixel intensities to the range [0, 1] (this improves training)
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
# Split the training data into separate train and test sets
(X_train, X_valid, Y_train, Y_valid) = train_test_split(data,
labels,
test_size=0.25,
random_state=0)
# Convert the labels (letters) into one-hot encodings that Keras can work with
lb = LabelBinarizer().fit(Y_train)
Y_train = lb.transform(Y_train)
Y_valid = lb.transform(Y_valid)
# Save the label. Useful when predicting
with open(MODEL_LABELS_FILENAME, "wb") as f:
pickle.dump(lb, f)
return X_train, Y_train, X_valid, Y_valid
def solve(image_file):
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.copyMakeBorder(image, 20, 20, 20, 20, cv2.BORDER_REPLICATE)
thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if imutils.is_cv2() else contours[1]
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))
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
output = cv2.merge([image] * 3)
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)
print (prediction)
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
cv2.rectangle(output, (x - 2, y - 2), (x + w + 4, y + h + 4), (0, 255, 0), 1)
cv2.putText(output, letter, (x - 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 2)
captcha_text = "".join(predictions)
#print("CAPTCHA text is: {}".format(captcha_text))
#cv2.imshow("Output", output)
cv2.waitKey()
return (captcha_text)
def solve_captcha(image_file):
preprocess_image(image_file)
with open(MODEL_LABELS_FILENAME, "rb") as f:
lb = pickle.load(f)
model = load_model(MODEL_FILENAME)
image = cv2.imread("transformed.png", cv2.IMREAD_GRAYSCALE)
thresh = cv2.threshold(image, 200, 255,
cv2.THRESH_TRUNC | cv2.THRESH_OTSU)[1]
thresh = cv2.adaptiveThreshold(thresh, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 11, 3)
letter_image_regions = []
img_h, img_w = image.shape
grid_w = 20 # crop width
img_h = 40
padding = 11
for w in range(6):
bbox = (padding + w * grid_w, 7, padding + (w + 1) * (grid_w),
img_h - 2)
letter_image_regions.append(bbox)
if len(letter_image_regions) != 6:
return ""
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 = thresh[y:h, x - 1:w + 1]
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)
return "".join(predictions)
def tensor():
try:
tensor = np.load("tensor.npy")
labels = np.load("labels.npy")
except:
data = []
labels = []
for image_file in paths.list_images("extracted_letter_images"):
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = resize_to_fit(image, 20, 20)
image = np.expand_dims(image, axis=2)
label = image_file.split(os.path.sep)[-2]
data.append(image)
labels.append(label)
tensor = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
return tensor, labels
def get_accuracy(image_characters, model, lb):
"""
Tests a model on a few defined training sets from the greedy segmenter
:returns overallaccuracy plot, character_accuracy plot
"""
result = ""
for row in range(image_characters.shape[0]):
for col in range(image_characters.shape[1]):
image = image_characters[row][col].astype(np.uint8)
image = crop_letter(image.astype(np.uint8))
# Re-size the letter image to 20x20 pixels to match training data
letter_image = resize_to_fit(image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
if "_upper" in letter:
letter = letter[0:1]
if letter in classifier.FONT_MAP:
letter = classifier.FONT_MAP[letter]
result += letter
print(len(result))
print(len(EXPECTED))
letter_match = {}
total_matched = 0
for character in range(len(EXPECTED)):
if EXPECTED[character] not in letter_match:
letter_match[EXPECTED[character]] = 0
if EXPECTED[character] == result[character]:
total_matched += 1
letter_match[EXPECTED[character]] = letter_match[EXPECTED[character]] + 1
return float(total_matched) / float(len(EXPECTED)), letter_match
def ocr_image_to_label(text='0000'):
download_images_to_folder(text=text,images_folder='temp')
captcha_image_files = list(paths.list_images('temp'))
# loop over the image paths
# for image_file in captcha_image_files:
image_file = 'temp/'+text+'.jpeg'
img = Image.open(image_file)
img = two_value(img)
clearNoise(img, 0, 3, 1)
letter_image_regions = letter_split(img)
# [letter_image_regions[i].save(str(i+1))+'.jpeg' for i in range(4)]
[letter_image_regions[i].save(str(i + 1) + '.jpeg') for i in range(4)]
predictions = []
# loop over the lektters
for i in range(4):
# Grab the coordinates of the letter in the image
# x, y, w, h = letter_bounding_box
# Extract the letter from the original image with a 2-pixel margin around the edge
# letter_image = image[y - 2:y + h + 2, x - 2:x + w + 2]
letter_bounding_box = cv2.imread(str(i + 1) + '.jpeg')
letter_bounding_box = cv2.cvtColor(letter_bounding_box, cv2.COLOR_BGR2GRAY)
letter_image = resize_to_fit(letter_bounding_box, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# Print the captcha's text
captcha_text = "".join(predictions)
return captcha_text
def load_data():
# initialize the data and labels
data = []
labels = []
# loop over the input images
for image_file in paths.list_images(LETTER_IMAGES_FOLDER):
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
if type(image) == type(None):
print(f"{image_file} returned None from cv2.imread")
os.remove(image_file)
continue
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Resize the letter so it fits in a 20x20 pixel box
image = resize_to_fit(image, 20, 20)
# Add a third channel dimension to the image to make Keras happy
image = np.expand_dims(image, axis=2)
# Grab the name of the letter based on the folder it was in
label = image_file.split(os.path.sep)[-2]
# Add the letter image and it's label to our training data
data.append(image)
labels.append(label)
# scale the raw pixel intensities to the range [0, 1] (this improves training)
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
n_samples = len(data)
data = data.reshape((n_samples, -1))
# .squeeze() to remove unwanted dimension https://stackoverflow.com/a/25453912/5140672
return data.squeeze(), labels
def resizeImage(image_file):
global current
current = current + 1
print("[INFO] processing image {}/{}".format(current, row))
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = resize_to_fit(image, 20, 20)
label = image_file.split(os.path.sep)[-2]
save_path = os.path.join(LETTER_IMAGES_FOLDER_YZH, label)
filename = image_file.split(os.path.sep)[-1]
full_save_path = os.path.join(save_path, filename)
# print(save_path)
if not os.path.exists(save_path):
os.makedirs(save_path)
cv2.imwrite(full_save_path, image)
# characteristic before cropping
w = 22
#left, upper, right, lower = 14, 27, 38, 60
left, upper, right, lower = 5, 25, 45, 65
# loop over the letters
for i in range(5):
# Extract the letter from the original image with a 2-pixel margin around the edge
letter_image = image[upper:lower, left:right]
#cv2.imshow("letter_image", letter_image)
#cv2.waitKey()
# Re-size the letter image to 50x40 pixels to match training data
letter_image = resize_to_fit(letter_image, 40, 40)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# draw the prediction on the output image
cv2.rectangle(output, (left, upper), (right, lower), (0, 255, 0), 1)
cv2.putText(output, letter, (left - 5, upper - 5),
for sze_crp in size_crops:
LETTER_IMAGES_FOLDER = ''.join((letters_dir,'/',str(sze_crp),'/'))
# initialize the data and labels
data = []
labels = []
# loop over the input images
for image_file in paths.list_images(LETTER_IMAGES_FOLDER):
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Resize the letter so it fits in a 28x28 pixel box
image = resize_to_fit(image, 28, 28)
# Add a third channel dimension to the image to make Keras happy
image = np.expand_dims(image, axis=2)
# Grab the name of the letter based on the folder it was in
label = image_file.split(os.path.sep)[-2]
# Add the letter image and it's label to our training data
data.append(image)
labels.append(label)
# scale the raw pixel intensities to the range [0, 1] (this improves training)
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
# In[8]:
LETTER_IMAGES_FOLDER = letters_train_dir
# initialize the data and labels
data_l_train = []
labels_l_train = []
# loop over the input images
for image_file in paths.list_images(LETTER_IMAGES_FOLDER):
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Resize the letter so it fits in a 28x28 pixel box
image = resize_to_fit(image, 32, 32)
# Add a third channel dimension to the image to make Keras happy
image = np.expand_dims(image, axis=2)
# Grab the name of the letter based on the folder it was in
label = image_file.split(os.path.sep)[-1].split('.')[-2].split('-')[1]
# Add the letter image and it's label to our training data
data_l_train.append(image)
labels_l_train.append(label)
# In[9]:
LETTER_IMAGES_FOLDER = letters_test_dir
# Create a list to hold output images and our predicted letters
predictions = []
outputs = []
# loop over the lektters
for letter_bounding_box in letter_image_regions:
output = cv2.merge([gray] * 3)
# Grab the coordinates of the letter in the image
x, y, w, h = letter_bounding_box
# Extract the letter from the original image with a 2-pixel margin around the edge
letter_image = gray[y - 2:y + h + 2, x - 2:x + w + 2]
# Re-size the letter image to 20x20 pixels to match training data
letter_image = helpers.resize_to_fit(letter_image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# draw the prediction on the output image
cv2.rectangle(output, (x - 2, y - 2), (x + w + 4, y + h + 4),
(0, 255, 0), 1)
f for f in os.listdir(predict_image_directory)
if f.lower().endswith(('.png', '.jpg', '.jpeg'))
]
list_images_labels = []
for f in files_:
data = []
real_age = i
# img = image.load_img(os.path.join(predict_image_directory, f), target_size=(img_width, img_height))
image = cv2.imread(os.path.join(predict_image_directory, f))
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = resize_to_fit(image, image_size, image_size)
#image = np.expand_dims(image, axis=2)
#image = image / 255.0
#data = np.array(data, dtype="float") / 255.0
# x = image.img_to_array(img)
image = np.expand_dims(image, axis=2)
image = np.expand_dims(image, axis=0)
value = model.predict_classes(image)
#print(value)
#list_images_labels.append((f, real_age, value[0]))
def Solve(img_str):
# Load the image and convert it to grayscale
nparr = np.fromstring(img_str, np.uint8)
image = cv2.imdecode(nparr,
cv2.IMREAD_COLOR) # cv2.IMREAD_COLOR in OpenCV 3.1
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Add some extra padding around the image
image = cv2.copyMakeBorder(image,
20,
20,
20,
20,
cv2.BORDER_CONSTANT,
value=(255, 255, 255))
# threshold the image (convert it to pure black and white)
thresh = cv2.threshold(image, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# find the contours (continuous blobs of pixels) the image
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# Hack for compatibility with different OpenCV versions
contours = contours[0]
letter_image_regions = []
# Now we can loop through each of the four contours and extract the letter
# inside of each one
for contour in contours:
# Get the rectangle that contains the contour
(x, y, w, h) = cv2.boundingRect(contour)
# Compare the width and height of the contour to detect letters that
# are conjoined into one chunk
if w / h > 1.7:
# This contour is too wide to be a single letter!
# Split it in half into two letter regions!
return (False)
else:
# This is a normal letter by itself
letter_image_regions.append((x, y, w, h))
# If we found more or less than 4 letters in the captcha, our letter extraction
# didn't work correcly.Skip rather than wating 5 seconds
if len(letter_image_regions) != 5:
return (False)
# Sort the detected letter images based on the x coordinate to make sure
# we are processing them from left-to-right so we match the right image
# with the right letter
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
# Create an output image and a list to hold our predicted letters
predictions = []
# loop over the lektters
for letter_bounding_box in letter_image_regions:
# Grab the coordinates of the letter in the image
x, y, w, h = letter_bounding_box
# Extract the letter from the original image with a 2-pixel margin around the edge
letter_image = image[y - 6:y + h + 6, x - 2:x + w + 2]
# Re-size the letter image to 20x20 pixels to match training data
letter_image = resize_to_fit(letter_image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# Print the captcha's text
captcha_text = "".join(predictions)
return (captcha_text)
if len(single_letter_image) != 4:
continue
# Sort the letters w r t x coordinate, not to make mistakes when comparing the label
single_letter_image = sorted(single_letter_image, key=lambda x: x[0])
# Save each letter separately
output = cv2.merge([grayscale_image] * 3)
predictions = []
for letter_bounding_box in single_letter_image:
x, y, w, h = letter_bounding_box
extracted_letter_image = grayscale_image[y - 2:y + h + 2,
x - 2:x + w + 2]
# resize to 20x20 because it's the dimension of the input
extracted_letter_image = resize_to_fit(extracted_letter_image, 20, 20)
# we also need a 4d tensor
extracted_letter_image = np.expand_dims(extracted_letter_image, axis=2)
extracted_letter_image = np.expand_dims(extracted_letter_image, axis=0)
prediction = model.predict(extracted_letter_image)
# remember we used a label binarizer...
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
cv2.rectangle(output, (x - 2, y - 2), (x + w + 4, y + h + 4),
(0, 255, 0), 1)
cv2.putText(output, letter, (x - 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
0.55, (0, 255, 0), 2)
# Print result
captcha_text = "".join(predictions)
if captcha_correct_text != format(captcha_text):
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
# Create an output image and a list to hold our predicted letters
output = cv2.merge([image] * 3)
predictions = []
# loop over the lektters
for letter_bounding_box in letter_image_regions:
# Grab the coordinates of the letter in the image
x, y, w, h = letter_bounding_box
# Extract the letter from the original image with a 2-pixel margin around the edge
letter_image = image[y - 2:y + h + 2, x - 2:x + w + 2]
# Re-size the letter image to 20x20 pixels to match training data
letter_image = resize_to_fit(letter_image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# draw the prediction on the output image
cv2.rectangle(output, (x - 2, y - 2), (x + w + 4, y + h + 4), (0, 255, 0), 1)
cv2.putText(output, letter, (x - 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 2)
MODEL_FILENAME = "captcha_model.hdf5"
MODEL_LABELS_FILENAME = "model_labels.dat"
# initialize the data and labels
data = []
labels = []
# loop over the input images
for image_file in paths.list_images(
LETTER_IMAGES_FOLDER): #paths将该文件夹下的文件名变成列表
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #颜色空间转换函数,将BGR格式转换成灰度图片
# Resize the letter so it fits in a 30x30 pixel box
image = resize_to_fit(image, 30, 30)
# Add a third channel dimension to the image to make Keras happy#这个happy也太秀了
image = np.expand_dims(image, axis=2)
# Grab the name of the letter based on the folder it was in
label = image_file.split(os.path.sep)[-2] #os.path.sep路径分隔符，一般是"/"
# Add the letter image and it's label to our training data
data.append(image)
#print(data)
labels.append(label)
#print(labels)
# scale the raw pixel intensities to the range [0, 1] (this improves training)
data = np.array(data, dtype="float") / 255.0
from keras.layers.core import Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from helpers import resize_to_fit
SINGLE_LETTERS_FOLDER = "extracted_letter_images"
MODEL_FILENAME = "captcha_model.hdf5"
MODEL_LABELS_FILENAME = "model_labels.dat"
data = []
labels = []
for image_file in paths.list_images(SINGLE_LETTERS_FOLDER):
image = cv2.imread(image_file)
grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# set dimension for all images
grayscale_image = resize_to_fit(grayscale_image, 20, 20)
# need a third channel to work with keras
grayscale_image = np.expand_dims(grayscale_image, axis=2)
# add letter of image to labels
label = image_file.split(os.path.sep)[-2]
data.append(grayscale_image)
labels.append(label)
# try scaling to improve training, float have higher precision
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
(X_train, X_test, Y_train, Y_test) = train_test_split(data,
labels,
test_size=0.25,
LETTER_IMAGES_FOLDER = "extracted_letter_images"
MODEL_FILENAME = "captcha_model.hdf5"
MODEL_LABELS_FILENAME = "model_labels.dat"
# initialize the data and labels
data = []
labels = []
# loop over the input images
for image_file in paths.list_images(LETTER_IMAGES_FOLDER):
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Resize the letter so it fits in a 20x20 pixel box
image = resize_to_fit(image, 20, 20)
# Add a third channel dimension to the image to make Keras happy
image = np.expand_dims(image, axis=2)
# Grab the name of the letter based on the folder it was in
label = image_file.split(os.path.sep)[-2]
# Add the letter image and it's label to our training data
data.append(image)
labels.append(label)
# scale the raw pixel intensities to the range [0, 1] (this improves training)
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
def textpredict(image_file):
# loop over the image paths
# image_file = ""
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
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 (convert it to pure black and white)
thresh = cv2.threshold(image, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# find the contours (continuous blobs of pixels) the image
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# Hack for compatibility with different OpenCV versions
# contours = contours[0] if imutils.is_cv2() else contours[1]
contours = contours[1] if imutils.is_cv3() else contours[0]
letter_image_regions = []
# Now we can loop through each of the four contours and extract the letter
# inside of each one
for contour in contours:
# Get the rectangle that contains the contour
(x, y, w, h) = cv2.boundingRect(contour)
# Compare the width and height of the contour to detect letters that
# are conjoined into one chunk
if w / h > 1.25:
# This contour is too wide to be a single letter!
# Split it in half into two letter regions!
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:
# This is a normal letter by itself
letter_image_regions.append((x, y, w, h))
# Sort the detected letter images based on the x coordinate to make sure
# we are processing them from left-to-right so we match the right image
# with the right letter
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
# Create an output image and a list to hold our predicted letters
predictions = []
# loop over the lektters
for letter_bounding_box in letter_image_regions:
# Grab the coordinates of the letter in the image
x, y, w, h = letter_bounding_box
# Extract the letter from the original image with a 2-pixel margin around the edge
letter_image = image[y - 2:y + h + 2, x - 2:x + w + 2]
# Re-size the letter image to 20x20 pixels to match training data
letter_image = resize_to_fit(letter_image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# Print the captcha's text
captcha_text = "".join(predictions)
return captcha_text
MODEL_FILENAME = "stanford_models/model_large_renamed_test_6epoch.hdf5"
MODEL_LABELS_FILENAME = "stanford_models/model_large_renamed_test_6epoch.dat"
# initialize the data and labels
data = []
labels = []
# loop over the input images
for image_file in paths.list_images(LETTER_IMAGES_FOLDER):
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Resize the letter so it fits in a 40x40 pixel box
image = resize_to_fit(image, 40, 40)
# Add a third channel dimension to the image to make Keras happy
image = np.expand_dims(image, axis=2)
# Grab the name of the letter based on the folder it was in
label = image_file.split(os.path.sep)[-2]
# Add the letter image and it's label to our training data
data.append(image)
labels.append(label)
# scale the raw pixel intensities to the range [0, 1] (this improves training)
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
def decaptcha(filenames):
numChars = []
codes = []
m = 0
preprocessing = 0
testing = 0
tic = tm.perf_counter()
model = load_model('final_model.hdf5')
toc = tm.perf_counter()
print(toc - tic)
print(type(model))
temp1 = 0
temp2 = 0
temp3 = 0
temp4 = 0
j = 0
for i in filenames:
tic = tm.perf_counter()
img = cv2.imread(i)
img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img[img == img[0, 0]] = 255
kernel = np.ones((7, 7), np.uint8)
kernel1 = np.ones((4, 4), np.uint8)
img_d1 = cv2.dilate(img, kernel, iterations=1)
img = cv2.cvtColor(img_d1, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(img, (5, 5), 0)
ret3, th3 = cv2.threshold(blur, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img = cv2.bitwise_not(th3)
if j == 0:
cv2.imshow("imagee", img)
cv2.waitKey(0)
cv2.destroyAllWindow()
j = j + 1
toc3 = tm.perf_counter()
cols = np.sum(img, axis=0)
rows = np.sum(img, axis=1)
cnt = 0
flag = 0
thres = 255 * 5
tmp = 0
start = []
end = []
for i in range(len(cols)):
if cols[i] > thres and flag == 0:
flag = 1
tmp = i
if cols[i] <= thres and flag == 1:
if (i - tmp) > 5:
start.append(tmp)
end.append(i)
flag = 0
char = np.zeros((len(start), 140, 140))
toc4 = tm.perf_counter()
for i in range(len(start)):
l = end[i] - start[i]
char[i][:, 75 - (l // 2):75 + l - (l // 2)] = img[5:-5,
start[i]:end[i]]
toc5 = tm.perf_counter()
resized = np.zeros((len(start), 30, 30))
for i in range(len(start)):
char[i] = 255 * np.ones((140, 140)) - char[i]
resized[i] = resize_to_fit(char[i], 30, 30)
toc1 = tm.perf_counter()
temp1 = temp1 + (toc3 - tic)
temp2 = temp2 + (toc4 - toc3)
temp3 = temp3 + (toc5 - toc4)
temp4 = temp4 + (toc1 - toc5)
preprocessing = preprocessing + (toc1 - tic)
resized = resized[..., np.newaxis]
y_out = model.predict(resized)
label = ""
for i in range(len(start)):
label = label + chr(
ord('A') + np.where(y_out[i] == np.amax(y_out[i]))[0][0])
numChars.append(len(start))
codes.append(label)
m = m + 1
toc2 = tm.perf_counter()
testing = testing + (toc2 - toc1)
print(temp1)
print(temp2)
print(temp3)
print(temp4)
print(preprocessing)
print(testing)
return (numChars, codes)
MODEL_FILENAME = "captcha_model.hdf5"
MODEL_LABELS_FILENAME = "model_labels.dat"
# initialize the data and labels
data = []
labels = []
# loop over the input images
for image_file in paths.list_images(LETTER_IMAGES_FOLDER):
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Resize the letter so it fits in a 20x20 pixel box
image = resize_to_fit(image, 20, 20)
# Add a third channel dimension to the image to make Keras happy
image = np.expand_dims(image, axis=2)
# Grab the name of the letter based on the folder it was in
label = image_file.split(os.path.sep)[-2]
# Add the letter image and it's label to our training data
data.append(image)
labels.append(label)
# scale the raw pixel intensities to the range [0, 1] (this improves training)
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
def template_match_char(char_image, threshold, debug):
logging.info("[BEGIN] Executing TEMPLATE MATCHING")
# read character image
image = cv2.imread(char_image)
# convert to gray scale image
character_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if debug:
cv2.imshow("Char Gray", character_image)
# threshold the image, convert it to black/white
thresh, im_bw = cv2.threshold(character_image, threshold, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
im_bw = cv2.bitwise_not(im_bw)
# resize to fit then crop
crop_image = helpers.crop_letter(im_bw)
if debug:
cv2.imshow("Chara Cropped", crop_image)
resized_image = helpers.resize_to_fit(crop_image.astype(np.uint8), 20, 20)
if debug:
cv2.imshow("Char Resized", resized_image)
# directory full of texts -- to check if it's match
files = glob.glob('../neural_network/images/**.ttf/**/*.png')
text_dict = {}
for file in files:
text_dict[file] = {"path":file, "stats":None, "accuracy":None}
# reads templates
for i in text_dict:
logging.debug("Matching? " + i)
template_image = cv2.imread(i)
template_image_gray = cv2.cvtColor(template_image, cv2.COLOR_BGR2GRAY)
# convert to grayscale and to black and white
_, template_bw = cv2.threshold(template_image_gray, threshold, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# template_bw = cv2.bitwise_not(template_bw)
# resize to fit then crop
tm_crop_image = helpers.crop_letter(template_bw)
tm_resized_image = helpers.resize_to_fit(tm_crop_image.astype(np.uint8), 20, 20)
result = cv2.matchTemplate(tm_resized_image, resized_image, cv2.TM_CCOEFF_NORMED)
(minValue, maxValue, minLocation, maxLocation) = cv2.minMaxLoc(result)
# (x, y) = minLocation
# cv2.rectangle(character_image, (x, y), (x + template_image.shape[0], y + template_image.shape[1]), (0, 255, 0), 2)
# cv2.imshow("Source", character_image)
text_dict[i]["stats"] = (minValue, maxValue, minLocation, maxLocation)
text_dict[i]["accuracy"] = float(maxValue)
logging.debug("Result Accuracy: minValue({0}) maxValue({1}) File({2}) ".format(minValue, maxValue, i))
logging.debug("-----")
# highest_value = {"path":None, "stats":None, "accuracy":0}
# logging.info(highest_value["accuracy"])
# for i in text_dict:
# val = text_dict[i]["accuracy"]
# if int(val) > int(highest_value["accuracy"]):
# logging.info(text_dict[i])
# highest_value = text_dict[i]
# logging.info(highest_value)
sorted_dict = sorted(text_dict, key=lambda x: (text_dict[x]['accuracy']), reverse=True)
for i in sorted_dict:
logging.debug("{0} - {1}".format(text_dict[i]['accuracy'], i))
logging.info("Best Accuracy Result: {0} : {1} [File name: {2}]".format(text_dict[sorted_dict[0]]["accuracy"],
os.path.basename(sorted_dict[0]), sorted_dict[0]))
logging.info("Next Best 3 Accuracy Result...")
for i in range(1, 4):
logging.info("Accuracy Result #{0}: {1} : {2} [File name: {3}]".format(i+1, text_dict[sorted_dict[i]]["accuracy"],
os.path.basename(sorted_dict[i]),
sorted_dict[i]))
logging.info("[END] Finished TEMPLATE MATCHING")
if w / h > 1: # 1 - best value so far (hyperparam)
half_width = int(w / 2)
char_image_regions.append((x, y, half_width, h))
char_image_regions.append((x + half_width, y, half_width, h))
else:
char_image_regions.append((x, y, w, h))
char_image_regions = sorted(char_image_regions, key=lambda x: x[0])
predictions = []
for char_bounding_box in char_image_regions:
x, y, w, h = char_bounding_box
char_image = image[y:y + h, x:x + w]
char_image = resize_to_fit(char_image, 20, 20)
char_image = np.expand_dims(char_image, axis=2)
char_image = np.expand_dims(char_image, axis=0)
prediction = model.predict(char_image)
char = lb.inverse_transform(prediction)[0]
predictions.append(char)
captcha_text = "".join(predictions)
print(f"ACTUAL : {captcha_correct_text}")
print(f"CAPTCHA: {captcha_text}\n")
if captcha_text == captcha_correct_text:
num_correct += 1
def train_model():
LETTER_IMAGES_FOLDER = "extracted_letter_images"
MODEL_FILENAME = "captcha_model.hdf5"
MODEL_LABELS_FILENAME = "model_labels.dat"
# initialize the data and labels
data = []
labels = []
# loop over the input images
for image_file in paths.list_images(LETTER_IMAGES_FOLDER):
# Load the image and convert it to grayscale
image = cv2.imread(
image_file) # image_file: 'extracted_letter_images/K/000566.png'
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Resize the letter so it fits in a 20x20 pixel box
image = resize_to_fit(image, 20, 20)
# Add a third channel dimension to the image to make Keras happy
image = np.expand_dims(
image, axis=2
) # Before: image.shape: (20, 20). After: image.shape: (20, 20, 1).
# Grab the name of the letter based on the folder it was in
label = image_file.split(os.path.sep)[-2]
# Add the letter image and its label to our training data
data.append(image)
labels.append(label)
# scale the raw pixel intensities to the range [0, 1] (this improves training)
data = np.array(data,
dtype="float") / 255.0 # data.shape: (38744, 20, 20, 1)
labels = np.array(labels)
# Split the training data into separate train and test sets
(X_train, X_test, Y_train, Y_test) = train_test_split(data,
labels,
test_size=0.25,
random_state=0)
# Convert the labels (letters) into one-hot encodings that Keras can work with
lb = LabelBinarizer().fit(
labels
) # lb = LabelBinarizer().fit(Y_train) # NOTE: 严格来说应该用labels，但其实用Y_train就已经能够覆盖到所有的字母和数字了
Y_train = lb.transform(Y_train)
Y_test = lb.transform(Y_test)
# Save the mapping from labels to one-hot encodings.
# We'll need this later when we use the model to decode what its predictions mean
with open(MODEL_LABELS_FILENAME, "wb") as f:
pickle.dump(lb, f)
# Build the neural network!
model = Sequential()
# First convolutional layer with max pooling
model.add(
Conv2D(20, (5, 5),
padding="same",
input_shape=(20, 20, 1),
activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Second convolutional layer with max pooling
model.add(Conv2D(50, (5, 5), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Hidden layer with 500 nodes
model.add(Flatten())
model.add(Dense(500, activation="relu"))
# Output layer with 32 nodes (one for each possible letter/number we predict)
model.add(Dense(32, activation="softmax"))
# Ask Keras to build the TensorFlow model behind the scenes
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
# Train the neural network
model.fit(X_train,
Y_train,
validation_data=(X_test, Y_test),
batch_size=32,
epochs=10,
verbose=1)
# Save the trained model to disk
model.save(MODEL_FILENAME)
def solve_captcha_from_file(image_file):
MODEL_FILENAME = "captcha_model.hdf5"
MODEL_LABELS_FILENAME = "model_labels.dat"
NUM_LETTERS = 5
# Load up the model labels (so we can translate model predictions to actual letters)
with open(MODEL_LABELS_FILENAME, "rb") as f:
lb = pickle.load(f)
# Load the trained neural network
model = load_model(MODEL_FILENAME)
# Load the image and convert it to grayscale
image = cv2.imread(image_file)
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 (convert it to pure black and white)
thresh = cv2.threshold(image, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# find the contours (continuous blobs of pixels) the image
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# Hack for compatibility with different OpenCV versions
contours = contours[0] #if imutils.is_cv2() else contours[1]
letter_image_regions = []
# Now we can loop through each of the four contours and extract the letter
# inside of each one
for contour in contours:
# Get the rectangle that contains the contour
(x, y, w, h) = cv2.boundingRect(contour)
# Compare the width and height of the contour to detect letters that
# are conjoined into one chunk
if w / h > 1.25:
# This contour is too wide to be a single letter!
# Split it in half into two letter regions!
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:
# This is a normal letter by itself
letter_image_regions.append((x, y, w, h))
# If we found more or less than 4 letters in the captcha, our letter extraction
# didn't work correcly. Skip the image instead of saving bad training data!
if len(letter_image_regions) != NUM_LETTERS:
return '-1'
# Sort the detected letter images based on the x coordinate to make sure
# we are processing them from left-to-right so we match the right image
# with the right letter
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
# Create an output image and a list to hold our predicted letters
output = cv2.merge([image] * 3)
predictions = []
# loop over the letters
for letter_bounding_box in letter_image_regions:
# Grab the coordinates of the letter in the image
x, y, w, h = letter_bounding_box
# Extract the letter from the original image with a 2-pixel margin around the edge
letter_image = image[y - 2:y + h + 2, x - 2:x + w + 2]
# Re-size the letter image to 20x20 pixels to match training data
letter_image = resize_to_fit(letter_image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# Print the captcha's text
captcha_text = "".join(predictions)
return captcha_text
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
# Create an output image and a list to hold our predicted letters
output = cv2.merge([image] * 3)
predictions = []
# loop over the lektters
for letter_bounding_box in letter_image_regions:
# Grab the coordinates of the letter in the image
x, y, w, h = letter_bounding_box
# Extract the letter from the original image with a 2-pixel margin around the edge
letter_image = image[y - 2:y + h + 2, x - 2:x + w + 2]
# Re-size the letter image to 20x20 pixels to match training data
letter_image = resize_to_fit(letter_image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# draw the prediction on the output image
cv2.rectangle(output, (x - 2, y - 2), (x + w + 4, y + h + 4),
(0, 255, 0), 1)
def Solve(model,lb,text):
# Grab some random CAPTCHA images to test against.
# In the real world, you'd replace this section with code to grab a real
# CAPTCHA image from a live website.
#captcha_image_files = list(paths.list_images(CAPTCHA_IMAGE_FOLDER))
#captcha_image_files = np.random.choice(captcha_image_files, size=(10,), replace=False)
#image = io.imread("https://obis1.selcuk.edu.tr/CaptchaHandler.ashx")
# loop over the image paths
encoded_data = text.split(',')[1]
nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# Load the image and convert it to grayscale
#image = cv2.imread(image_file)
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 (convert it to pure black and white)
thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
for i in range(len(thresh)):
if i>51:
thresh[i-1] = thresh[i]
# find the contours (continuous blobs of pixels) the image
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Hack for compatibility with different OpenCV versions
contours = contours[1] if imutils.is_cv3() else contours[0]
letter_image_regions = []
# Now we can loop through each of the four contours and extract the letter
# inside of each one
for contour in contours:
# Get the rectangle that contains the contour
(x, y, w, h) = cv2.boundingRect(contour)
# Compare the width and height of the contour to detect letters that
# are conjoined into one chunk
if w / h > 1.25:
# This contour is too wide to be a single letter!
# Split it in half into two letter regions!
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:
# This is a normal letter by itself
letter_image_regions.append((x, y, w, h))
# If we found more or less than 4 letters in the captcha, our letter extraction
# didn't work correcly. Skip the image instead of saving bad training data!
#print(len(letter_image_regions))
if len(letter_image_regions) != 4:
return {"result":"-1"}
# Sort the detected letter images based on the x coordinate to make sure
# we are processing them from left-to-right so we match the right image
# with the right letter
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
# Create an output image and a list to hold our predicted letters
output = cv2.merge([image] * 3)
predictions = []
# loop over the lektters
for letter_bounding_box in letter_image_regions:
# Grab the coordinates of the letter in the image
x, y, w, h = letter_bounding_box
# Extract the letter from the original image with a 2-pixel margin around the edge
letter_image = image[y - 2:y + h + 2, x - 2:x + w + 2]
# Re-size the letter image to 20x20 pixels to match training data
letter_image = resize_to_fit(letter_image, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter_image = np.expand_dims(letter_image, axis=2)
letter_image = np.expand_dims(letter_image, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter_image)
# Convert the one-hot-encoded prediction back to a normal letter
letter = lb.inverse_transform(prediction)[0]
predictions.append(letter)
# draw the prediction on the output image
cv2.rectangle(output, (x - 2, y - 2), (x + w + 4, y + h + 4), (0, 255, 0), 1)
cv2.putText(output, letter, (x - 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 2)
# Print the captcha's text
captcha_text = "".join(predictions)
return {"result":"{}".format(captcha_text)}
