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README.md

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 # CNN-Breaking-CAPTCHA-Security-Codes
 > CNNs are a type of neural network that allow greater extraction of features from captured images. Unlike classical models, CNNs take image data, train the model, and then classify the features automatically for healthier classification. The image data was fit to the CNN model with 80% in the training set and 20% in the testing set. Since the technology that was used to fit the image data to the CNN did not allow for the label to be a string, the 32 individual characters strings of the CAPTCHA images were converted to their corresponding ASCII codes, as illustrated in the following steps
 
+
+# Image Processing
+
+The Captcha image consists of four characters that are either a string
+of letters or a combination of letters with numbers. In some of the
+combinations, the captcha characters contain all letters but they are
+not aligned cleanly. Some of the letters are joined together or
+diagonally aligned, therefore correctly separating them in order to make
+them easily recognised as letters is one of the tasks at hand. In the
+instances of Captcha characters that contain both numbers and letters,
+the position of the number is not guaranteed and the letters are
+slanted.
+
+The expected outcome is to correctly identify each of the Captcha
+characters from the image. We are doing this inorder to measure the
+efficiency of the neural networks to correctly identify the Captcha
+characters. The label variable is the letter or number from the image of
+the separated Captcha characters.
+
+## read in images
+
+``` {.python}
+def read_image(image_file_path):
+    """Read in an image file."""
+    bgr_img = cv2.imread(image_file_path)
+    b,g,r = cv2.split(bgr_img)       # get b,g,r
+    rgb_img = cv2.merge([r,g,b])     # switch it to rgb
+    
+    return rgb_img 
+images = []
+labels = []
+
+for image_file_path in imutils.paths.list_images(image_file_Path):
+    image_file = read_image(image_file_path)
+    label = image_file_path.split('/')[7]
+    images.append(image_file)
+    labels.append(label)
+   
+for label in labels:
+    labelHere = label.split('.')[0]
+    newLabels.append(labelHere)
+    
+images = np.array(images)
+#images4Plot = np.array(images, dtype="float") / 255.0
+labels = np.array(newLabels)
+```
+A dataset of 9,955 of unique CAPTCHA images each with its label as the
+filename was used for this research. However, machine learning
+classification requires a one-to-many relationship between a label and
+in this context the CAPTCHA images. Therefore, uniqueness of the CAPTCHA
+images is problematic for a machine learning process.
+
+``` {.python}
+some_digit = images[300]
+#Some_digit_image = some_digit.reshape(24, 72, 3)
+plt.imshow(some_digit, cmap = mpl.cm.binary,
+           interpolation="nearest")
+plt.axis("off")
+
+
+plt.show()
+```
+![alt text](https://github.com/Kovenda/randomForest-Breaking-CAPTCHA-Security-Codes/blob/main/images-and-plots/d0de6d4167aae20e07f8c9576a487b463410295a.png?raw=true)
+
+## Padding and Thresholding: Coverting images to Black and White (non-gray scale)
+
+``` {.python}
+def pureBlackWhiteConversionThreshold(image):
+    # Add some extra padding around the image
+    imagePadded = cv2.copyMakeBorder(image, 8, 8, 8, 8, cv2.BORDER_REPLICATE)
+    gray = cv2.cvtColor(imagePadded, cv2.COLOR_RGB2GRAY)
+    # threshold the image (convert it to pure black and white)
+    imagethresholded = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]    
+    return imagethresholded 
+    
+def pureBlackWhiteConversionOGImage(image):
+    # Add some extra padding around the image
+    imagePadded = cv2.copyMakeBorder(image, 8, 8, 8, 8, cv2.BORDER_REPLICATE)
+    gray = cv2.cvtColor(imagePadded, cv2.COLOR_RGB2GRAY)
+    
+padded_ThreshImage300 = pureBlackWhiteConversionThreshold(images[300])
+
+some_digit = padded_ThreshImage300
+plt.imshow(some_digit, cmap = mpl.cm.binary,
+           interpolation="nearest")
+plt.axis("off")
+plt.show()
+```
+![alt text](https://github.com/Kovenda/randomForest-Breaking-CAPTCHA-Security-Codes/blob/main/images-and-plots/8995982a1771d8fa3139a964a1f0de4b7efcd475.png?raw=true)
+
+## Seperate Characters into individual images 
+
+> To deal with the uniqueness problem of the dataset, the solution was to separate the CAPTCHA images into the individual 4 characters that make up the CHAPTCHA image. This was to make each character into its own image. The resulting dataset has 39,754 images with one character per image. The new dataset satisfies the one-to-many relationship between the images and the following 32 characters labels {\'2\', \'3\', \'4\',\'5\', \'6\', \'7\', \'8\', \'9\', \'A\', \'B\', \'C\', \'D\', \'E\',
+\'F\', \'G\', \'H\', \'J\', \'K\', \'L\', \'M\', \'N\', \'P\', \'Q\',\'R\', \'S\', \'T\', \'U\', \'V\', \'W\', \'X\', \'Y\', \'Z\'}
+
+
+``` {.python}
+def regionsOfLetters(image):
+    
+     # find the contours (continuous blobs of pixels) the image
+    contours = cv2.findContours(image, 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) != 4:
+       # continue
+
+    # 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])
+    
+    return letter_image_regions 
+    
+letter_image_regions = regionsOfLetters(padded_ThreshImage300)
+letter_image_regions
+
+def extractLetters(letter_image_regions, image):
+    # Save out each letter as a single image
+    letter_images =[]
+    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]
+        #image_file1 = read_image(letter_image)
+        letter_images.append(letter_image)
+    return letter_images 
+    
+grayScaleImage = pureBlackWhiteConversionOGImage(images[300])
+letter_image_List = extractLetters(letter_image_regions,grayScaleImage)
+
+checkImage = letter_image_List[3]
+
+some_digit = checkImage
+#Some_digit_image = some_digit.reshape(24, 72, 3)
+plt.imshow(some_digit, cmap = mpl.cm.binary,
+           interpolation="nearest")
+plt.axis("off")
+plt.show()
+```
+![alt text](https://github.com/Kovenda/randomForest-Breaking-CAPTCHA-Security-Codes/blob/main/images-and-plots/2492e78e7724e2c4241d4148b15603de796bd78a.png?raw=true)
+
+# New Image Dataset
+``` {.python}
+def plot_digits(instances, images_per_row=10, **options):
+    size = 28
+    images_per_row = min(len(instances), images_per_row)
+    images = [instance.reshape(size,size) for instance in instances]
+    n_rows = (len(instances) - 1) // images_per_row + 1
+    row_images = []
+    n_empty = n_rows * images_per_row - len(instances)
+    images.append(np.zeros((size, size * n_empty)))
+    for row in range(n_rows):
+        rimages = images[row * images_per_row : (row + 1) * images_per_row]
+        row_images.append(np.concatenate(rimages, axis=1))
+    image = np.concatenate(row_images, axis=0)
+    plt.imshow(image, cmap = mpl.cm.binary, **options)
+    plt.axis("off")
+    
+index, = np.where(labels == 'F')
+
+plt.figure(figsize=(15, 15))
+example_images = np.r_[images[[14,39,51,39702,39752]], 
+                       images[[7,56,61,39703,39714]],
+                       images[[45,   198,   352,39705, 39719]], 
+                       images[[2,    12,    52, 39698, 39712]], 
+                       images[[3,    26,    87, 39612, 39619]]]
+
+example_images
+plot_digits(example_images, images_per_row=5)
+#save_fig("more_digits_plot")
+#plt.show()
+
+```
+![alt text](https://github.com/Kovenda/randomForest-Breaking-CAPTCHA-Security-Codes/blob/main/images-and-plots/deb47268075c5456f794f88455f6f8a4307c4491.png?raw=true)
+
 ## Data
 The images data used for this model went through some image processing and was flattened to a csv
 > See imageProcessing.py in this repo to see how that was done