diff --git a/.gitignore b/.gitignore
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+++ b/.gitignore
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+*.pyc 
+.DS_Store
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diff --git a/README.md b/README.md
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+# Document Scanner
+
+### An interactive document scanner built in Python using OpenCV
+
+The scanner takes a poorly scanned image, finds the corners of the document, applies the perspective transformation to get a top-down view of the document, sharpens the image, and applies an adaptive color threshold to clean up the image.
+
+On my test dataset of 280 images, the program correctly detected the corners of the document 92.8% of the time.
+
+This project makes use of the transform and imutils modules from pyimagesearch (which can be accessed [here](http://www.pyimagesearch.com/2014/09/01/build-kick-ass-mobile-document-scanner-just-5-minutes/)). The UI code for the interactive mode is adapted from `poly_editor.py` from [here](https://matplotlib.org/examples/event_handling/poly_editor.html).
+
+* You can manually click and drag the corners of the document to be perspective transformed:
+![Example of interactive GUI](https://github.com/andrewdcampbell/doc_scanner/blob/master/ui.gif)
+
+* The scanner can also process an entire directory of images automatically and save the output in an output directory:
+![Image Directory of images to be processed](https://github.com/andrewdcampbell/doc_scanner/blob/master/before_after.gif)
+
+
+### Usage
+```
+python scan.py (--images <IMG_DIR> | --image <IMG_PATH>) [-i]
+```
+* For example, to scan a single image with interactive mode:
+```
+python scan.py --image images/page.jpg -i
+```
+* To scan all images in a directory automatically:
+```
+python scan.py --images images
+```
\ No newline at end of file
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diff --git a/polygon_interacter.py b/polygon_interacter.py
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index 0000000..af5a638
--- /dev/null
+++ b/polygon_interacter.py
@@ -0,0 +1,107 @@
+import numpy as np
+from matplotlib.lines import Line2D
+from matplotlib.artist import Artist
+from matplotlib.mlab import dist_point_to_segment
+
+
+class PolygonInteractor(object):
+    """
+    An polygon editor
+    """
+
+    showverts = True
+    epsilon = 5  # max pixel distance to count as a vertex hit
+
+    def __init__(self, ax, poly):
+        if poly.figure is None:
+            raise RuntimeError('You must first add the polygon to a figure or canvas before defining the interactor')
+        self.ax = ax
+        canvas = poly.figure.canvas
+        self.poly = poly
+
+        x, y = zip(*self.poly.xy)
+        self.line = Line2D(x, y, marker='o', markerfacecolor='r', animated=True)
+        self.ax.add_line(self.line)
+
+        cid = self.poly.add_callback(self.poly_changed)
+        self._ind = None  # the active vert
+
+        canvas.mpl_connect('draw_event', self.draw_callback)
+        canvas.mpl_connect('button_press_event', self.button_press_callback)
+        canvas.mpl_connect('button_release_event', self.button_release_callback)
+        canvas.mpl_connect('motion_notify_event', self.motion_notify_callback)
+        self.canvas = canvas
+
+    def get_poly_points(self):
+        return np.asarray(self.poly.xy)
+
+    def draw_callback(self, event):
+        self.background = self.canvas.copy_from_bbox(self.ax.bbox)
+        self.ax.draw_artist(self.poly)
+        self.ax.draw_artist(self.line)
+        self.canvas.blit(self.ax.bbox)
+
+    def poly_changed(self, poly):
+        'this method is called whenever the polygon object is called'
+        # only copy the artist props to the line (except visibility)
+        vis = self.line.get_visible()
+        Artist.update_from(self.line, poly)
+        self.line.set_visible(vis)  # don't use the poly visibility state
+
+    def get_ind_under_point(self, event):
+        'get the index of the vertex under point if within epsilon tolerance'
+
+        # display coords
+        xy = np.asarray(self.poly.xy)
+        xyt = self.poly.get_transform().transform(xy)
+        xt, yt = xyt[:, 0], xyt[:, 1]
+        d = np.sqrt((xt - event.x)**2 + (yt - event.y)**2)
+        indseq = np.nonzero(np.equal(d, np.amin(d)))[0]
+        ind = indseq[0]
+
+        if d[ind] >= self.epsilon:
+            ind = None
+
+        return ind
+
+    def button_press_callback(self, event):
+        'whenever a mouse button is pressed'
+        if not self.showverts:
+            return
+        if event.inaxes is None:
+            return
+        if event.button != 1:
+            return
+        self._ind = self.get_ind_under_point(event)
+
+    def button_release_callback(self, event):
+        'whenever a mouse button is released'
+        if not self.showverts:
+            return
+        if event.button != 1:
+            return
+        self._ind = None
+
+    def motion_notify_callback(self, event):
+        'on mouse movement'
+        if not self.showverts:
+            return
+        if self._ind is None:
+            return
+        if event.inaxes is None:
+            return
+        if event.button != 1:
+            return
+        x, y = event.xdata, event.ydata
+
+        self.poly.xy[self._ind] = x, y
+        if self._ind == 0:
+            self.poly.xy[-1] = x, y
+        elif self._ind == len(self.poly.xy) - 1:
+            self.poly.xy[0] = x, y
+        self.line.set_data(zip(*self.poly.xy))
+
+        self.canvas.restore_region(self.background)
+        self.ax.draw_artist(self.poly)
+        self.ax.draw_artist(self.line)
+        self.canvas.blit(self.ax.bbox)
diff --git a/pyimagesearch/__init__.py b/pyimagesearch/__init__.py
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index 0000000..e69de29
diff --git a/pyimagesearch/imutils.py b/pyimagesearch/imutils.py
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+++ b/pyimagesearch/imutils.py
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+# Import the necessary packages
+import numpy as np
+import cv2
+
+def translate(image, x, y):
+	# Define the translation matrix and perform the translation
+	M = np.float32([[1, 0, x], [0, 1, y]])
+	shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
+
+	# Return the translated image
+	return shifted
+
+def rotate(image, angle, center = None, scale = 1.0):
+	# Grab the dimensions of the image
+	(h, w) = image.shape[:2]
+
+	# If the center is None, initialize it as the center of
+	# the image
+	if center is None:
+		center = (w / 2, h / 2)
+
+	# Perform the rotation
+	M = cv2.getRotationMatrix2D(center, angle, scale)
+	rotated = cv2.warpAffine(image, M, (w, h))
+
+	# Return the rotated image
+	return rotated
+
+def resize(image, width = None, height = None, inter = cv2.INTER_AREA):
+	# initialize the dimensions of the image to be resized and
+	# grab the image size
+	dim = None
+	(h, w) = image.shape[:2]
+
+	# if both the width and height are None, then return the
+	# original image
+	if width is None and height is None:
+		return image
+
+	# check to see if the width is None
+	if width is None:
+		# calculate the ratio of the height and construct the
+		# dimensions
+		r = height / float(h)
+		dim = (int(w * r), height)
+
+	# otherwise, the height is None
+	else:
+		# calculate the ratio of the width and construct the
+		# dimensions
+		r = width / float(w)
+		dim = (width, int(h * r))
+
+	# resize the image
+	resized = cv2.resize(image, dim, interpolation = inter)
+
+	# return the resized image
+	return resized
\ No newline at end of file
diff --git a/pyimagesearch/transform.py b/pyimagesearch/transform.py
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+++ b/pyimagesearch/transform.py
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+# import the necessary packages
+from scipy.spatial import distance as dist
+import numpy as np
+import cv2
+
+def order_points(pts):
+    # sort the points based on their x-coordinates
+    xSorted = pts[np.argsort(pts[:, 0]), :]
+ 
+    # grab the left-most and right-most points from the sorted
+    # x-roodinate points
+    leftMost = xSorted[:2, :]
+    rightMost = xSorted[2:, :]
+ 
+    # now, sort the left-most coordinates according to their
+    # y-coordinates so we can grab the top-left and bottom-left
+    # points, respectively
+    leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+    (tl, bl) = leftMost
+ 
+    # now that we have the top-left coordinate, use it as an
+    # anchor to calculate the Euclidean distance between the
+    # top-left and right-most points; by the Pythagorean
+    # theorem, the point with the largest distance will be
+    # our bottom-right point
+    D = dist.cdist(tl[np.newaxis], rightMost, "euclidean")[0]
+    (br, tr) = rightMost[np.argsort(D)[::-1], :]
+ 
+    # return the coordinates in top-left, top-right,
+    # bottom-right, and bottom-left order
+    return np.array([tl, tr, br, bl], dtype = "float32")
+
+def four_point_transform(image, pts):
+    # obtain a consistent order of the points and unpack them
+    # individually
+    rect = order_points(pts)
+    (tl, tr, br, bl) = rect
+
+    # compute the width of the new image, which will be the
+    # maximum distance between bottom-right and bottom-left
+    # x-coordiates or the top-right and top-left x-coordinates
+    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
+    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
+    maxWidth = max(int(widthA), int(widthB))
+
+    # compute the height of the new image, which will be the
+    # maximum distance between the top-right and bottom-right
+    # y-coordinates or the top-left and bottom-left y-coordinates
+    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
+    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
+    maxHeight = max(int(heightA), int(heightB))
+
+    # now that we have the dimensions of the new image, construct
+    # the set of destination points to obtain a "birds eye view",
+    # (i.e. top-down view) of the image, again specifying points
+    # in the top-left, top-right, bottom-right, and bottom-left
+    # order
+    dst = np.array([
+        [0, 0],
+        [maxWidth - 1, 0],
+        [maxWidth - 1, maxHeight - 1],
+        [0, maxHeight - 1]], dtype = "float32")
+
+    # compute the perspective transform matrix and then apply it
+    M = cv2.getPerspectiveTransform(rect, dst)
+    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
+
+    # return the warped image
+    return warped
\ No newline at end of file
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diff --git a/scan.py b/scan.py
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+++ b/scan.py
@@ -0,0 +1,335 @@
+# USAGE:
+# python scan.py (--images <IMG_DIR> | --image <IMG_PATH>) [-i]
+# For example, to scan a single image with interactive mode:
+# python scan.py --image sample_images/desk.JPG -i
+# To scan all images in a directory automatically:
+# python scan.py --images sample_images
+
+# Scanned images will be output to directory named 'output'
+
+from pyimagesearch import transform
+from pyimagesearch import imutils
+from scipy.spatial import distance as dist
+from matplotlib.patches import Polygon
+import polygon_interacter as poly_i
+import numpy as np
+import matplotlib.pyplot as plt
+import itertools
+import math
+import cv2
+
+import argparse
+import os
+
+class DocScanner(object):
+    """An image scanner"""
+
+    def __init__(self, interactive=False, MIN_QUAD_AREA_RATIO=0.25, MAX_QUAD_ANGLE_RANGE=40):
+        """
+        Args:
+            interactive (boolean): If True, user can adjust screen contour before
+                transformation occurs in interactive pyplot window.
+            MIN_QUAD_AREA_RATIO (float): A contour will be rejected if its corners 
+                do not form a quadrilateral that covers at least MIN_QUAD_AREA_RATIO 
+                of the original image. Defaults to 0.25.
+            MAX_QUAD_ANGLE_RANGE (int):  A contour will also be rejected if the range 
+                of its interior angles exceeds MAX_QUAD_ANGLE_RANGE. Defaults to 40.
+        """        
+        self.interactive = interactive
+        self.MIN_QUAD_AREA_RATIO = MIN_QUAD_AREA_RATIO
+        self.MAX_QUAD_ANGLE_RANGE = MAX_QUAD_ANGLE_RANGE        
+
+    def filter_corners(self, corners, min_dist=20):
+        """Filters corners that are within min_dist of others"""
+        def predicate(representatives, corner):
+            return all(dist.euclidean(representative, corner) >= min_dist
+                       for representative in representatives)
+
+        filtered_corners = []
+        for c in corners:
+            if predicate(filtered_corners, c):
+                filtered_corners.append(c)
+        return filtered_corners
+
+    def angle_between_vectors_degrees(self, u, v):
+        """Returns the angle between two vectors in degrees"""
+        return np.degrees(
+            math.acos(np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v))))
+
+    def get_angle(self, p1, p2, p3):
+        """
+        Returns the angle between the line segment from p2 to p1 
+        and the line segment from p2 to p3 in degrees
+        """
+        a = np.radians(np.array(p1))
+        b = np.radians(np.array(p2))
+        c = np.radians(np.array(p3))
+
+        avec = a - b
+        cvec = c - b
+
+        return self.angle_between_vectors_degrees(avec, cvec)
+
+    def angle_range(self, quad):
+        """
+        Returns the range between max and min interior angles of quadrilateral.
+        The input quadrilateral must be a numpy array with vertices ordered clockwise
+        starting with the top left vertex.
+        """
+        tl, tr, br, bl = quad
+        ura = self.get_angle(tl[0], tr[0], br[0])
+        ula = self.get_angle(bl[0], tl[0], tr[0])
+        lra = self.get_angle(tr[0], br[0], bl[0])
+        lla = self.get_angle(br[0], bl[0], tl[0])
+
+        angles = [ura, ula, lra, lla]
+        return np.ptp(angles)          
+
+    def get_corners(self, img):
+        """
+        Returns a list of corners ((x, y) tuples) found in the input image. With proper
+        pre-processing and filtering, it should output at most 10 potential corners.
+        This is a utility function used by get_contours. The input image is expected 
+        to be rescaled and Canny filtered prior to be passed in.
+        """
+        lsd = cv2.createLineSegmentDetector()
+        lines = lsd.detect(img)[0]
+
+        # massages the output from LSD
+        # LSD operates on edges. One "line" has 2 edges, and so we need to combine the edges back into lines
+        # 1. separate out the lines into horizontal and vertical lines.
+        # 2. Draw the horizontal lines back onto a canvas, but slightly thicker and longer.
+        # 3. Run connected-components on the new canvas
+        # 4. Get the bounding box for each component, and the bounding box is final line.
+        # 5. The ends of each line is a corner
+        # 6. Repeat for vertical lines
+        # 7. Draw all the final lines onto another canvas. Where the lines overlap are also corners
+
+        corners = []
+        if lines is not None:
+            # separate out the horizontal and vertical lines, and draw them back onto separate canvases
+            lines = lines.squeeze().astype(np.int32).tolist()
+            horizontal_lines_canvas = np.zeros(img.shape, dtype=np.uint8)
+            vertical_lines_canvas = np.zeros(img.shape, dtype=np.uint8)
+            for line in lines:
+                x1, y1, x2, y2 = line
+                if abs(x2 - x1) > abs(y2 - y1):
+                    (x1, y1), (x2, y2) = sorted(((x1, y1), (x2, y2)), key=lambda pt: pt[0])
+                    cv2.line(horizontal_lines_canvas, (max(x1 - 5, 0), y1), (min(x2 + 5, img.shape[1] - 1), y2), 255, 2)
+                else:
+                    (x1, y1), (x2, y2) = sorted(((x1, y1), (x2, y2)), key=lambda pt: pt[1])
+                    cv2.line(vertical_lines_canvas, (x1, max(y1 - 5, 0)), (x2, min(y2 + 5, img.shape[0] - 1)), 255, 2)
+
+            lines = []
+
+            # find the horizontal lines (connected-components -> bounding boxes -> final lines)
+            contours = cv2.findContours(horizontal_lines_canvas, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+            contours = contours[1]
+            contours = sorted(contours, key=lambda c: cv2.arcLength(c, True), reverse=True)[:2]
+            horizontal_lines_canvas = np.zeros(img.shape, dtype=np.uint8)
+            for contour in contours:
+                contour = contour.reshape((contour.shape[0], contour.shape[2]))
+                min_x = np.amin(contour[:, 0], axis=0) + 2
+                max_x = np.amax(contour[:, 0], axis=0) - 2
+                left_y = int(np.average(contour[contour[:, 0] == min_x][:, 1]))
+                right_y = int(np.average(contour[contour[:, 0] == max_x][:, 1]))
+                lines.append((min_x, left_y, max_x, right_y))
+                cv2.line(horizontal_lines_canvas, (min_x, left_y), (max_x, right_y), 1, 1)
+                corners.append((min_x, left_y))
+                corners.append((max_x, right_y))
+
+            # find the vertical lines (connected-components -> bounding boxes -> final lines)
+            contours = cv2.findContours(vertical_lines_canvas, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+            contours = contours[1]
+            contours = sorted(contours, key=lambda c: cv2.arcLength(c, True), reverse=True)[:2]
+            vertical_lines_canvas = np.zeros(img.shape, dtype=np.uint8)
+            for contour in contours:
+                contour = contour.reshape((contour.shape[0], contour.shape[2]))
+                min_y = np.amin(contour[:, 1], axis=0) + 2
+                max_y = np.amax(contour[:, 1], axis=0) - 2
+                top_x = int(np.average(contour[contour[:, 1] == min_y][:, 0]))
+                bottom_x = int(np.average(contour[contour[:, 1] == max_y][:, 0]))
+                lines.append((top_x, min_y, bottom_x, max_y))
+                cv2.line(vertical_lines_canvas, (top_x, min_y), (bottom_x, max_y), 1, 1)
+                corners.append((top_x, min_y))
+                corners.append((bottom_x, max_y))
+
+            # find the corners
+            corners_y, corners_x = np.where(horizontal_lines_canvas + vertical_lines_canvas == 2)
+            corners += zip(corners_x, corners_y)
+
+        # remove corners in close proximity
+        corners = self.filter_corners(corners)
+        return corners
+
+    def is_valid_contour(self, cnt, IM_WIDTH, IM_HEIGHT):
+        """Returns True if the contour satisfies all requirements set at instantitation"""
+
+        return (len(cnt) == 4 and cv2.contourArea(cnt) > IM_WIDTH * IM_HEIGHT * self.MIN_QUAD_AREA_RATIO 
+            and self.angle_range(cnt) < self.MAX_QUAD_ANGLE_RANGE)
+
+
+    def get_contour(self, rescaled_image):
+        """
+        Returns a numpy array of shape (4, 2) containing the vertices of the four corners
+        of the document in the image. It considers the corners returned from get_corners()
+        and uses heuristics to choose the four corners that most likely represent
+        the corners of the document. If no corners were found, or the four corners represent
+        a quadrilateral that is too small or convex, it returns the original four corners.
+        """
+
+        # these constants are carefully chosen
+        MORPH = 9
+        CANNY = 84
+        HOUGH = 25
+
+        IM_HEIGHT, IM_WIDTH, _ = rescaled_image.shape
+
+        # convert the image to grayscale and blur it slightly
+        gray = cv2.cvtColor(rescaled_image, cv2.COLOR_BGR2GRAY)
+        gray = cv2.GaussianBlur(gray, (7,7), 0)
+
+        # dilate helps to remove potential holes between edge segments
+        kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(MORPH,MORPH))
+        dilated = cv2.dilate(gray, kernel)
+
+        # find edges and mark them in the output map using the Canny algorithm
+        edged = cv2.Canny(dilated, 0, CANNY)
+        test_corners = self.get_corners(edged)
+
+        approx_contours = []
+
+        if len(test_corners) >= 4:
+            quads = []
+
+            for quad in itertools.combinations(test_corners, 4):
+                points = np.array(quad)
+                points = transform.order_points(points)
+                points = np.array([[p] for p in points], dtype = "int32")
+                quads.append(points)
+
+            # get top five quadrilaterals by area
+            quads = sorted(quads, key=cv2.contourArea, reverse=True)[:5]
+            # sort candidate quadrilaterals by their angle range, which helps remove outliers
+            quads = sorted(quads, key=self.angle_range)
+
+            approx = quads[0]
+            if self.is_valid_contour(approx, IM_WIDTH, IM_HEIGHT):
+                approx_contours.append(approx)
+
+            # for debugging: uncomment the code below to draw the corners and countour found 
+            # by get_corners() and overlay it on the image
+
+            # cv2.drawContours(rescaled_image, [approx], -1, (20, 20, 255), 2)
+            # plt.scatter(*zip(*test_corners))
+            # plt.imshow(rescaled_image)
+            # plt.show()
+
+        # also attempt to find contours directly from the edged image, which occasionally 
+        # produces better results
+        (_, cnts, hierarchy) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
+
+        # loop over the contours
+        for c in cnts:
+            # approximate the contour
+            approx = cv2.approxPolyDP(c, 80, True)
+            if self.is_valid_contour(approx, IM_WIDTH, IM_HEIGHT):
+                approx_contours.append(approx)
+                break
+
+        # If we did not find any valid contours, just use the whole image
+        if not approx_contours:
+            TOP_RIGHT = (IM_WIDTH, 0)
+            BOTTOM_RIGHT = (IM_WIDTH, IM_HEIGHT)
+            BOTTOM_LEFT = (0, IM_HEIGHT)
+            TOP_LEFT = (0, 0)
+            screenCnt = np.array([[TOP_RIGHT], [BOTTOM_RIGHT], [BOTTOM_LEFT], [TOP_LEFT]])
+
+        else:
+            screenCnt = max(approx_contours, key=cv2.contourArea)
+            
+        return screenCnt.reshape(4, 2)
+
+    def interactive_get_contour(self, screenCnt, rescaled_image):
+        poly = Polygon(screenCnt, animated=True, fill=False, color="yellow", linewidth=5)
+        fig, ax = plt.subplots()
+        ax.add_patch(poly)
+        ax.set_title(('Drag the corners of the box to the corners of the document. \n'
+            'Close the window when finished.'))
+        p = poly_i.PolygonInteractor(ax, poly)
+        plt.imshow(rescaled_image)
+        plt.show()
+
+        new_points = p.get_poly_points()[:4]
+        new_points = np.array([[p] for p in new_points], dtype = "int32")
+        return new_points.reshape(4, 2)
+
+    def scan(self, image_path):
+
+        RESCALED_HEIGHT = 500.0
+        OUTPUT_DIR = 'output'
+
+        # load the image and compute the ratio of the old height
+        # to the new height, clone it, and resize it
+        image = cv2.imread(image_path)
+
+        assert(image is not None)
+
+        ratio = image.shape[0] / RESCALED_HEIGHT
+        orig = image.copy()
+        rescaled_image = imutils.resize(image, height = int(RESCALED_HEIGHT))
+
+        # get the contour of the document
+        screenCnt = self.get_contour(rescaled_image)
+
+        if self.interactive:
+            screenCnt = self.interactive_get_contour(screenCnt, rescaled_image)
+
+        # apply the perspective transformation
+        warped = transform.four_point_transform(orig, screenCnt * ratio)
+
+        # convert the warped image to grayscale
+        gray = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
+
+        # sharpen image
+        sharpen = cv2.GaussianBlur(gray, (0,0), 3)
+        sharpen = cv2.addWeighted(gray, 1.5, sharpen, -0.5, 0)
+
+        # apply adaptive threshold to get black and white effect
+        thresh = cv2.adaptiveThreshold(sharpen, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 15)
+
+        # save the transformed image
+        basename = os.path.basename(image_path)
+        cv2.imwrite(OUTPUT_DIR + '/' + basename, thresh)
+        print("Proccessed " + basename)
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    group = ap.add_mutually_exclusive_group(required=True)
+    group.add_argument("--images", help="Directory of images to be scanned")
+    group.add_argument("--image", help="Path to single image to be scanned")
+    ap.add_argument("-i", action='store_true',
+        help = "Flag for manually verifying and/or setting document corners")
+
+    args = vars(ap.parse_args())
+    im_dir = args["images"]
+    im_file_path = args["image"]
+    interactive_mode = args["i"]
+
+    scanner = DocScanner(interactive_mode)
+
+    valid_formats = [".jpg", ".jpeg", ".jp2", ".png", ".bmp", ".tiff", ".tif"]
+
+    get_ext = lambda f: os.path.splitext(f)[1].lower()
+
+    # Scan single image specified by command line argument --image <IMAGE_PATH>
+    if im_file_path:
+        scanner.scan(im_file_path)
+
+    # Scan all valid images in directory specified by command line argument --images <IMAGE_DIR>
+    else:
+        im_files = [f for f in os.listdir(im_dir) if get_ext(f) in valid_formats]
+        for im in im_files:
+            scanner.scan(im_dir + '/' + im)
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