def find_box(img, draw=True):
ratio = 1.5
orig = img.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (3, 3), 0)
gray = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 61, 10)
#gray = cv2.morphologyEx(gray, cv2.MORPH_OPEN, np.ones((3, 3)), iterations=1)
#gray = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, np.ones((9, 9)), iterations=1)
#gray = cv2.morphologyEx(gray, cv2.MORPH_OPEN, np.ones((3, 3)), iterations=2)
#_, gray = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY_INV)
gray = cv2.dilate(gray, np.ones((5, 5), np.uint8), iterations=2)
edged = cv2.Canny(gray, 75, 200)
if draw:
pass
cv2.imshow("orig", imutils.resize(gray, height=500))
cv2.imshow("equ", imutils.resize(edged, height=500))
cv2.waitKey(0)
_, cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:15]
possiable_pages = []
approx_conts = []
for c in cnts:
epsilon = 0.1 * cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, epsilon, True)
approx_conts.append(approx)
if len(approx) == 4:
possiable_pages.append(approx)
possiable_pages = sorted(possiable_pages,
key=lambda c: cv2.boundingRect(c)[1],
reverse=False)
out_page = []
for i in range(len(possiable_pages) - 1):
if cv2.boundingRect(possiable_pages[i + 1])[1] - cv2.boundingRect(
possiable_pages[i])[1] > 15:
out_page.append(possiable_pages[i])
out_page.append(possiable_pages[-1])
possiable_pages = out_page
print "pages: {}, approx: {}".format(len(possiable_pages),
len(approx_conts))
if draw:
cv2.drawContours(img, approx_conts, -1, (0, 255, 0), 2)
cv2.drawContours(img, possiable_pages, -1, (0, 255, 0), 2)
cv2.imshow("equ_conts", imutils.resize(img, height=500))
cv2.waitKey(0)
#cv2.imshow("cropped", ret[0])
#cv2.imshow("cropped", imutils.resize(cropped, height=500))
#plt.hist(orig.ravel(), 256, [0, 256])
#plt.show()
cv2.waitKey(0)
ret = [crop(orig, x) for x in possiable_pages]
ret2 = [x.tolist() for x in possiable_pages]
return zip(ret, ret2)
def scan_image(image):
# image = cv2.imread(img)
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
print "STEP 1: Edge Detection"
# cv2.imshow("Image", image)
# cv2.imshow("Edged", edged)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
_, cnts, hierarchy = cv2.findContours(edged, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
print(len(approx))
if len(approx) == 4:
screenCnt = approx
break
else:
return
print "STEP 2: Find contours of paper"
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
# cv2.imshow("Outline", image)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
warped = threshold_adaptive(warped, 251, offset=10)
warped = warped.astype("uint8") * 255
print "STEP 3: Apply perspective transform"
# cv2.imshow("Original", imutils.resize(orig, height = 650))
cv2.imshow("Scanned", imutils.resize(warped, height=650))
cv2.waitKey(0)
cv2.destroyAllWindows()
def find_page(img, draw=True):
ratio = img.shape[0] / 500.0
orig = img.copy()
img = imutils.resize(img, height=500)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#gray = cv2.GaussianBlur(gray, (19, 19), 0)
edged = cv2.Canny(gray, 75, 200)
if draw:
cv2.imshow("Img", edged)
cv2.waitKey(0)
_, cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
possiable_pages = []
approx_conts = []
for c in cnts:
epsilon = 0.1 * cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, epsilon, True)
approx_conts.append(approx)
if len(approx) == 4:
possiable_pages.append(approx)
break
if draw:
cv2.drawContours(img, cnts, -1, (0, 255, 0), 2)
cv2.imshow("Img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
return four_point_transform(orig, possiable_pages[0].reshape(4, 2) * ratio)
def thresh(self, image):
ratio = image.shape[0] / 500.0
orig = image.copy()
warped = imutils.resize(image, height=500)
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
warped = threshold_adaptive(warped, 251, offset=10)
warped = warped.astype("uint8") * 255
gray = cv2.GaussianBlur(warped, (5, 5), 0)
# show the original and scanned images
# print ("STEP 3: Apply perspective transform")
print("hello")
dst = cv2.fastNlMeansDenoising(warped, None, 10, 5, 21)
im = Image.fromarray(imutils.resize(dst, height=650))
# im.show()
return (dst)
def get_images_and_labels(path):
# Append all the absolute image paths in a list image_paths
# We will not read the image with the .sad extension in the training set
# Rather, we will use them to test our accuracy of the training
image_paths = [os.path.join(path, f) for f in os.listdir(path) if not f.endswith('.sad')]
# images will contains face images
images = []
# labels will contains the label that is assigned to the image
labels = []
for image_path in image_paths:
# Read the image and convert to grayscale
image_pil = cv2.imread(image_path)
resized = imutils.resize(image_pil, width = 300)
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
# Convert the image format into numpy array
image = np.array(gray, 'uint8')
# Get the label of the image
nbr = int(os.path.split(image_path)[1].split(".")[0].replace("subject", ""))
# Detect the face in the image
faces = faceCascade.detectMultiScale(image)
# If face is detected, append the face to images and the label to labels
for (x, y, w, h) in faces:
images.append(image[y: y + h, x: x + w])
labels.append(nbr)
cv2.imshow("Adding faces to traning set...", image[y: y + h, x: x + w])
cv2.waitKey(50)
# return the images list and labels list
return images, labels
def resize_image(image, desired_height):
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
ratio = image.shape[0] / desired_height
orig = image.copy()
image = imutils.resize(image, height=desired_height)
return image, orig, ratio
def processImage(self):
image = cv2.imread(self.imagePath)
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height=500)
print "STEP 1: Edge Detection"
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(image, (5, 5), 0)
edged = cv2.Canny(gray, 55, 200)
# cv2.imshow("Image", image)
# cv2.imshow("Edged", edged)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
print "STEP 2: Find contours of paper"
# find the contours in the edged image, keeping only the
# largest ones, and initialize the screen contour
(_, cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
# loop over the contours
screenCnt = []
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# if our approximated contour has four points, then we
# can assume that we have found our screen
if len(approx) == 4:
screenCnt = approx
break
if len(screenCnt) != 4:
raise ContourNotFoundError('not find contour')
# cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
# cv2.imshow("Outline", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# apply the four point transform to obtain a top-down
# view of the original image
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
# convert the warped image to grayscale, then threshold it
# to give it that 'black and white' paper effect
# warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
# warped = threshold_adaptive(warped, 251, offset=10)
# warped = warped.astype("uint8") * 255
height, width = warped.shape[:2]
ratio1 = float(width) / float(height)
ratio2 = float(height) / float(width)
# if (ratio1 > 0.80 or ratio1 < 0.60) and (ratio2 > 0.80 or ratio2 < 0.60):
# raise NotA4Error('Cropped Image is not a A4 paper: height: ' + str(height) + ' width: ' + str(width))
cv2.imwrite(self.outputPath, warped)
print "Finished Transformation"
return self.outputPath
def readReceipt(number):
# Read from specificed path.
image = cv2.imread(receipts[number])
# Resize as the images are hi-res.
ratio = image.shape[0] / 500.0
image = imutils.resize(image, height = 500)
# Return image.
return image
def center_extent(image, size):
(eW, eH) = size
if image.shape[1] > image.shape[0]:
image = imutils.resize(image, width=eW)
else:
image = imutils.resize(image, height=eH)
extent = np.zeros((eH, eW), dtype='uint8')
offsetX = (eW - image.shape[1]) // 2
offsetY = (eH - image.shape[0]) // 2
extent[offsetY:offsetY + image.shape[0],
offsetX:offsetX + image.shape[1]] = image
CM = mahotas.center_of_mass(extent)
(cY, cX) = np.round(CM).astype('int32')
(dX, dY) = ((size[0] // 2) - cX, (size[1] // 2) - cY)
M = np.float32([[1, 0, dX], [0, 1, dY]])
extent = cv2.warpAffine(extent, M, size)
return extent
def scan(cls, filepath):
print("Starting scan")
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
image = cv2.imread(filepath)
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height=500)
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# find the contours in the edged image, keeping only the
# largest ones, and initialize the screen contour
cnts, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
screenCnt = None
# loop over the contours
for c in cnts:
# approximate contours
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# if our approximated contour has four points, then we
# can assume that we have found our screen
if len(approx) == 4:
screenCnt = approx
break
# Check if we found a 4 point contour. If not, we create our own bounding box
# with the largest contour
if screenCnt is None:
height, width, channels = image.shape
imageBounds = np.array([[1, 1], [width, 1], [width, height], [1, height]])
screenCnt = imutils.get_bounding_box(imageBounds)
# apply the four point transform to obtain a top-down
# view of the original image
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
# convert the warped image to grayscale, then threshold it
# to give it that 'black and white' paper effect
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
warped = threshold_adaptive(warped, 250, offset=10)
warped = warped.astype("uint8") * 255
# Write out image to tmp file
filename = "tmp/tmp-result.png"
cv2.imwrite(filename, warped)
print("Finished scan")
return filename
def run(self):
# if a video path was not supplied, grab the reference
# to the gray
if not args.get("video", False):
camera = cv2.VideoCapture(0)
# otherwise, load the video
else:
camera = cv2.VideoCapture(args["video"])
# hand training data
hand_cascade = cv2.CascadeClassifier('hand_1.xml')
# keep looping over the frames in the video
while (camera.isOpened()):
# grab the current frame
(grabbed, frame) = camera.read()
frame = cv2.flip(frame, 1)
# if we are viewing a video and we did not grab a
# frame, then we have reached the end of the video
if args.get("video") and not grabbed:
break
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
frame = imutils.resize(frame, width=600)
skin = self.skindetection(frame)
frameGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
skinGray = cv2.cvtColor(skin, cv2.COLOR_BGR2GRAY)
# hand harrcascade part ----------------------------
hand = hand_cascade.detectMultiScale(skinGray, 1.3, 5)
for (x, y, w, h) in hand:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 4)
# ---------------------------------------------------------
ret, skinGray = cv2.threshold(skinGray, 120, 255,
cv2.THRESH_BINARY_INV)
# show the skin in the image along with the mask
cv2.imshow("images", np.hstack([frame, skin]))
cv2.imshow('gray', skinGray)
# if the 'q' key is pressed, stop the loop
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()
def deskew(image, width):
(h, w) = image.shape[:2]
moments = cv2.moments(image)
skew = moments['mu11'] / moments['mu02']
M = np.float32([[1, skew, -0.5 * w * skew], [0, 1, 0]])
image = cv2.warpAffine(image,
M, (w, h),
flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
image = imutils.resize(image, width=width)
return image
def draw_circle(event, x, y, flags, param):
global mouseX, mouseY, color
if event == cv2.EVENT_LBUTTONDBLCLK:
mouseX, mouseY = x, y
image = cv2.imread(dir_initials + "initial_" + letters[l] + "-1.png")
initial = imutils.resize(image, width=400, height=300)
cv2.imwrite("frame.png", initial)
color = initial[y, x]
print("My color is " + format(color))
if event == cv2.EVENT_RBUTTONDBLCLK:
mouseX, mouseY = x, y
image = cv2.imread("frame.png")
image = imutils.resize(image, width=400)
color_clicked = image[y, x]
print("Color clicked is " + format(color_clicked))
if color_clicked[2] < (color[2] + 40) and color_clicked[2] > (
color[2] - 40
) and color_clicked[1] < (color[1] + 50) and color_clicked[1] > (
color[1] - 50) and color_clicked[0] < (
color[0] + 50) and color_clicked[0] > (color[0] - 50):
print("Skin detected")
def get_image():
ap = argparse.ArgumentParser()
ap.add_argument("-i",
"--image",
required=True,
help="Path to the image to be scanned")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
ratio = image.shape[0] / 500.0
orig_img = image.copy()
# TODO: Check image orientation and rotate if neccessary with game_map = np.rot90(game_map, k=3)
resized_img = imutils.resize(image, height=500)
return (orig_img, resized_img, ratio)
def getSkinColor(pathToImage, outpath_onlySkin, outpath_Kmean, outpath_AGC):
# define the upper and lower boundaries of the HSV pixel
# intensities to be considered 'skin'
# lower = np.array([0, 48, 80], dtype = "uint8")
lower = np.array([0, 5, 40], dtype="uint8")
upper = np.array([55, 255, 255], dtype = "uint8")
# get the image from the file path
frame = cv2.imread(pathToImage)
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
# the speicifed upper and lower boundaries
frame = imutils.resize(frame, width = 400)
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
skinMask = cv2.inRange(converted, lower, upper)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
skinMask = cv2.erode(skinMask, kernel, iterations = 2)
skinMask = cv2.dilate(skinMask, kernel, iterations = 2)
# blur the mask to help remove noise, then apply the
# mask to the frame
skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
skin = cv2.bitwise_and(frame, frame, mask = skinMask)
# show the skin in the image along with the mask
cv2.imwrite(outpath_onlySkin, skin)
# do the action money shot!
quant = cluster_and_categorize(skin)
# save outputs of k-mean clustering
cv2.imwrite(outpath_Kmean, quant)
# get solid 150x150 image of the detected skin color
k_color = np.zeros((150, 150, 3), np.uint8)
k_color[:] = get_skin_color_from_hist(quant)
cv2.imwrite(outpath_AGC, k_color)
skin_type = categorize_skin_color(k_color[0:1,0:1])
print pathToImage + "\n---------------"
print "Your skin color is: "
print k_color[0,0]
print "Your skin type is: "
print skin_type
print ""
def detectFace(camera, fd):
(grabbed, frame) = camera.read()
frame = imutils.resize(frame, width = 300)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faceRects = fd.detect(gray, scaleFactor = 1.1, minNeighbors = 5,
minSize = (30, 30))
# frameClone = frame.copy()
#
# for (fX, fY, fW, fH) in faceRects:
# cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH), (0, 255, 0), 2)
#
# cv2.imshow("Face", frameClone)
return len(faceRects)
def scan(self, image_path):
RESCALED_HEIGHT = 500.0
OUTPUT_DIR = os.path.join(os.getcwd(), "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)
if os.path.exists(OUTPUT_DIR):
cv2.imwrite(OUTPUT_DIR + '/' + basename, thresh)
print("Proccessed " + basename)
else:
os.mkdir(OUTPUT_DIR)
cv2.imwrite(OUTPUT_DIR + '/' + basename, thresh)
print("Proccessed " + basename)
def detectFace(camera, fd):
(grabbed, frame) = camera.read()
frame = imutils.resize(frame, width=300)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faceRects = fd.detect(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
# frameClone = frame.copy()
#
# for (fX, fY, fW, fH) in faceRects:
# cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH), (0, 255, 0), 2)
#
# cv2.imshow("Face", frameClone)
return len(faceRects)
def processImage(self):
image = cv2.imread(self.imagePath)
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height=500)
print "STEP 1: Edge Detection"
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
print "STEP 2: Find contours of paper"
# find the contours in the edged image, keeping only the
# largest ones, and initialize the screen contour
(_, cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
# loop over the contours
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# if our approximated contour has four points, then we
# can assume that we have found our screen
if len(approx) == 4:
screenCnt = approx
break
# apply the four point transform to obtain a top-down
# view of the original image
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
# convert the warped image to grayscale, then threshold it
# to give it that 'black and white' paper effect
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
warped = threshold_adaptive(warped, 251, offset=10)
warped = warped.astype("uint8") * 255
cv2.imwrite(self.outputPath, warped)
print "Finished"
return self.outputPath
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_path = "/home/user4/Downloads/DATASET/white_images/1.jpeg"
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)
screenCnt = self.interactive_get_contour(screenCnt, rescaled_image)
# apply the perspective transformation
warped = transform.four_point_transform(orig, screenCnt * ratio)
warped = cv2.resize(warped, (2400, 1100))
# # 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(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 6)
# thresh = cv2.adaptiveThreshold(sharpen, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 6)
# #0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU
# save the transformed image
basename = os.path.basename(image_path)
image21 = cv2.imwrite(OUTPUT_DIR + '/' + basename, warped)
print("Proccessed " + basename)
def has_hand(image, image_path="result.JPG"):
# define the upper and lower boundaries of the HSV pixel
# intensities to be considered 'skin'
lower = np.array([0, 48, 80], dtype="uint8")
upper = np.array([20, 255, 255], dtype="uint8")
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
# the speicifed upper and lower boundaries
frame = imutils.resize(image, width=400)
converted = cv2.cvtColor(frame, cv2.COLOR_RGB2HSV)
skinMask = cv2.inRange(converted, lower, upper)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10))
skinMask = cv2.erode(skinMask, kernel, iterations=2)
skinMask = cv2.dilate(skinMask, kernel, iterations=2)
# blur the mask to help remove noise, then apply the
# mask to the frame
skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
skin = cv2.bitwise_and(frame, frame, mask=skinMask)
# show the skin in the image along with the mask
cv2.imwrite(image_path[:-4] + "_2.JPG", np.hstack([frame, skin]))
count = 0
for array in skinMask:
for elm in array:
if elm == 255:
# print("has skin")
count += 1
print(count)
if count > 1500:
print("has skin")
return True
else:
return False
def scan(image):
screenCnt = None
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height=500)
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (9, 9), 0)
# gray = threshold_adaptive(gray, 251, offset=5)
# warped = warped.astype("uint8") * 255
edged = cv2.Canny(gray, 75, 200)
# find the contours in the edged image, keeping only the
# largest ones, and initialize the screen contour
(_, cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # opencv3
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
# loop over the contours
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# if our approximated contour has four points, then we
# can assume that we have found our screen
if len(approx) == 4:
screenCnt = approx
break
# apply the four point transform to obtain a top-down
# view of the original image
if screenCnt is None:
return None
else:
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
wraped_2 = cv2.resize(cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY), (1000, 1366))
return wraped_2
def show_camera(self):
# capture frames from the camera
for f in self.camera.capture_continuous(self.rawCapture,
format="bgr",
use_video_port=True):
# grab the raw NumPy array representing the image
self.frame = f.array
# resize the frame and convert it to grayscale
self.frame = imutils.resize(self.frame, width=300)
self.gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
# detect faces in the image and then clone the frame
# so that we can draw on it
self.faceRects = self.fd.detect(self.gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
self.frameClone = self.frame.copy()
# loop over the face bounding boxes and draw them
for (fX, fY, fW, fH) in self.faceRects:
cv2.rectangle(self.frameClone, (fX, fY), (fX + fW, fY + fH),
(0, 255, 0), 2)
# Desactivo el laser si detecto una cara
if len(self.faceRects) > 0:
GPIO.output(laser, 0)
else:
GPIO.output(laser, 1)
# show our detected faces, then clear the frame in
# preparation for the next frame
#cv2.imshow("Face", self.frameClone)
self.frame2 = self.rot180(numpy.rot90(self.frameClone))
self.frame2 = pygame.surfarray.make_surface(self.frame2)
screen.blit(self.frame2, (400, 10))
pygame.display.update()
#pygame.display.flip()
self.rawCapture.truncate(0)
def show_camera(self):
# capture frames from the camera
for f in self.camera.capture_continuous(self.rawCapture,
format="bgr",
use_video_port=True):
# grab the raw NumPy array representing the image
self.frame = f.array
# resize the frame and convert it to grayscale
self.frame = self.rot180(imutils.resize(self.frame, width=300))
self.gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
# detect faces in the image and then clone the frame
# so that we can draw on it
self.faceRects = self.fd.detect(self.gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
self.frameClone = self.frame.copy()
# loop over the face bounding boxes and draw them
for (fX, fY, fW, fH) in self.faceRects:
cv2.rectangle(self.frameClone, (fX, fY), (fX + fW, fY + fH),
(0, 255, 0), 2)
# Desactivo el laser si detecto una cara
if len(self.faceRects) > 0:
GPIO.output(laser, 0)
else:
GPIO.output(laser, 1)
# show our detected faces, then clear the frame in
# preparation for the next frame
cv2.imshow("Robopot", self.frameClone)
self.rawCapture.truncate(0)
# if the 'q' key is pressed, stop the loop
if cv2.waitKey(1) & 0xFF == ord("q"):
break
def detect_skin(frame, img_width):
# Source: http://www.pyimagesearch.com/2014/08/18/skin-detection-step-step-example-using-python-opencv/
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
# the speicifed upper and lower boundaries
frame = imutils.resize(frame, width=img_width)
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
skinMask = cv2.inRange(converted, lower, upper)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
skinMask = cv2.erode(skinMask, kernel, iterations=2)
skinMask = cv2.dilate(skinMask, kernel, iterations=2)
# blur the mask to help remove noise, then apply the
# mask to the frame
skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
skin = cv2.bitwise_and(frame, frame, mask=skinMask)
# Return the frame
return skin
def getSkinColor(pathToImage, outpath_onlySkin, outpath_acg):
# define the upper and lower boundaries of the HSV pixel
# intensities to be considered 'skin'
#lower = np.array([0, 48, 80], dtype = "uint8")
lower = np.array([0, 5, 75], dtype = "uint8")
upper = np.array([20, 255, 255], dtype = "uint8")
frame = cv2.imread(pathToImage)
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
# the speicifed upper and lower boundaries
frame = imutils.resize(frame, width = 400)
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
skinMask = cv2.inRange(converted, lower, upper)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
skinMask = cv2.erode(skinMask, kernel, iterations = 2)
skinMask = cv2.dilate(skinMask, kernel, iterations = 2)
# blur the mask to help remove noise, then apply the
# mask to the frame
skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
skin = cv2.bitwise_and(frame, frame, mask = skinMask)
# show the skin in the image along with the mask
cv2.imwrite(outpath_onlySkin, skin)
#average_color = computeAvg(skin, outpath_acg)
#print average_color
#average_color = findNearestBucket([(40, 1.2, 98.04),(70,2,99),(49.09,34.38,87.84),(37.5,46.64,87.45),(13.91,68.32,39.61),(272.73,25.58,16.86)], average_color)
#average_color = bucketsavg(skin, outpath_acg, [(250,249,247),(251,252,246),(224,210,147),(223,184,119),(101,48,32),(38,32,43)])
#average_color = bucketsavg(skin, outpath_acg, [(40, 1.2, 98.04),(70,2,99),(49.09,34.38,87.84),(37.5,46.64,87.45),(13.91,68.32,39.61),(272.73,25.58,16.86)])
#average_color = bucketsavg(skin, outpath_acg, [(280, 3, 245), (40,3,236), (40,3,250),(54,23,253),(41,23,253),(43,25,254),(5,11,250),(21,14,243),(32,10,244),(67,8,252),(44,15,252),(43,29,254),(48,30,255),(48,30,255),(46,48,241),(48,70,239),(49,88,224),(49,66,242),(43,82,235),(49,111,235),(45,139,227),(43,135,225),(42,114,223),(37,118,222),(38,127,199),(35,122,188),(])
#print "The skin color in HSV scale is: "
#print average_color
return average_color
def transform_file(file):
image = cv2.imdecode(
np.fromstring(file.read(), np.uint8),
cv2.IMREAD_UNCHANGED) # make img from request to np array
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
print("STEP 1: Edge Detection")
(_, cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
# loop over the contours
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# if our approximated contour has four points, then we
# can assume that we have found our screen
if len(approx) == 4:
screenCnt = approx
break
print("STEP 2: Find contours of paper")
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
# convert the warped image to grayscale, then threshold it
# to give it that 'black and white' paper effect
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
warped = threshold_adaptive(warped, 251, offset=10)
warped = warped.astype("uint8") * 255
# would be nicer to leave the writing to another function? not sure
cv2.imwrite(os.path.join(app.config['UPLOAD_FOLDER'], file.filename),
warped)
import sys, cv2
from pyimagesearch import imutils
# The program accepts one command line parameter, specifying the file to read.
img = cv2.imread(sys.argv[1])
ratio = img.shape[0] / 500.0
orig = img.copy()
img = imutils.resize(img, height = 500)
## CROPPING AND DESKEWING
#gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#blurred = cv2.GaussianBlur(gray, (9,9), 0)
edges = cv2.Canny(img, 0, 150, apertureSize=3)
cv2.imshow("Image", edges)
cv2.waitKey(3000)
cv2.destroyAllWindows()
morphKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 1))
edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, morphKernel)
cv2.imshow("Image", edges)
cv2.waitKey(3000)
cv2.destroyAllWindows()
morphKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 1))
edges = cv2.morphologyEx(edges, cv2.MORPH_OPEN, morphKernel)
camera = cv2.VideoCapture(args["video"])
# keep looping over the frames in the video
while True:
# grab the current frame
(grabbed, frame) = camera.read() #grabbed = boolean for sucessful reading, frame = image frame
# if we are viewing a video and we did not grab a
# frame, then we have reached the end of the video
if args.get("video") and not grabbed:
break
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
# the speicifed upper and lower boundaries
frame = imutils.resize(frame, width = 400)
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) #Convert to HSV
skinMask = cv2.inRange(converted, lower, upper) #Check if within boundaries
# apply a series of erosions and dilations to the mask
# using an elliptical kernel to remove small false-positive skin regions
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
skinMask = cv2.erode(skinMask, kernel, iterations = 2)
skinMask = cv2.dilate(skinMask, kernel, iterations = 2)
# blur the mask to help remove noise, then apply the
# mask to the frame
skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
#Apply mask
skin = cv2.bitwise_and(frame, frame, mask = skinMask)
import numpy as np
import argparse
import pyimagesearch.imutils as imutils #pyimagesearch #.imutils
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--puzzle", required = True, help = "Path to the puzzle image")
ap.add_argument("-w", "--waldo", required = True, help = "Path to the waldo image")
args = vars(ap.parse_args())
# load the puzzle and waldo images
puzzle = cv2.imread(args["puzzle"])
waldo = cv2.imread(args["waldo"])
(waldoHeight, waldoWidth) = waldo.shape[:2]
# find the waldo in the puzzle
result = cv2.matchTemplate(puzzle, waldo, cv2.TM_CCOEFF)
(_, _, minLoc, maxLoc) = cv2.minMaxLoc(result)
# the puzzle image
topLeft = maxLoc
botRight = (topLeft[0] + waldoWidth, topLeft[1] + waldoHeight)
roi = puzzle[topLeft[1]:botRight[1], topLeft[0]:botRight[0]]
# construct a darkened transparent 'layer' to darken everything
# in the puzzle except for waldo
mask = np.zeros(puzzle.shape, dtype = "uint8")
puzzle = cv2.addWeighted(puzzle, 0.25, mask, 0.75, 0)
# put the original waldo back in the image so that he is
# 'brighter' than the rest of the image
puzzle[topLeft[1]:botRight[1], topLeft[0]:botRight[0]] = roi
# display the images
cv2.imshow("Puzzle", imutils.resize(puzzle, height = 650))
cv2.imshow("Waldo", waldo)
cv2.waitKey(0)
lower = np.array([0, 48, 80], dtype="uint8")
upper = np.array([20, 255, 255], dtype="uint8")
cap = cv2.VideoCapture(0)
while True:
# grab the current frame
grabbed, frame = cap.read()
# if we are viewing a video and we did not grab a
# frame, then we have reached the end of the video
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
# the speicifed upper and lower boundaries
frame = imutils.resize(frame, width=400)
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
skinMask = cv2.inRange(converted, lower, upper)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
# skinMask = cv2.erode(skinMask, kernel, iterations = 2)
# skinMask = cv2.dilate(skinMask, kernel, iterations = 2)
# blur the mask to help remove noise, then apply the
# mask to the frame
skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
skin = cv2.bitwise_and(frame, frame, mask=skinMask)
# show the skin in the image along with the mask
#python scan.py --image unnamed.jpg
# import the necessary packages
from pyimagesearch.transform import four_point_transform
from pyimagesearch import imutils
from skimage.filters import threshold_adaptive
import numpy as np
#import argparse
import cv2
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
image = cv2.imread("unnamed4.jpg")
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height=500)
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# show the original image and the edge detected image
print "STEP 1: Edge Detection"
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()
# find the contours in the edged image, keeping only the
def run(self):
# if a video path was not supplied, grab the reference
# to the gray
if not args.get("video", False):
camera = cv2.VideoCapture(0)
# otherwise, load the video
else:
camera = cv2.VideoCapture(args["video"])
# hand training data
#hand_cascade = cv2.CascadeClassifier('hand_1.xml')
# keep looping over the frames in the video
while (camera.isOpened()):
# grab the current frame
(grabbed, frame) = camera.read()
frame = cv2.flip(frame, 1)
# if we are viewing a video and we did not grab a
# frame, then we have reached the end of the video
if args.get("video") and not grabbed:
break
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
frame = imutils.resize(frame, width=900)
# define our roi region
x1, x2 = 10, 210
y1, y2 = 600, 800
roi_img = frame[x1:x2, y1:y2]
# extract skin color from the background
skin = self.extraction(roi_img, self.skinLower, self.skinUpper)
frameGray = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
skinGray = cv2.cvtColor(skin, cv2.COLOR_BGR2GRAY)
# hand harrcascade part ----------------------------
'''
hand = hand_cascade.detectMultiScale(skinGray, 1.3, 5)
for (x,y,w,h) in hand:
cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),4)
'''
# ---------------------------------------------------------
ret, skinGray = cv2.threshold(skinGray, 120, 255,
cv2.THRESH_BINARY_INV)
# find contour
image, contours, hierarchy = cv2.findContours(skinGray.copy(), \
cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# find the dominant hand contour
max_area = -1
for i in range(len(contours)):
cnt = contours[i]
area = cv2.contourArea(cnt)
if (area > max_area):
max_area = area
ci = i
cnt = contours[ci]
# draw contonur
hull = cv2.convexHull(cnt)
drawing = np.zeros(roi_img.shape, np.uint8)
cv2.drawContours(drawing, [cnt], 0, (0, 255, 0), 2)
cv2.drawContours(drawing, [hull], 0, (0, 0, 255), 2)
hull = cv2.convexHull(cnt, returnPoints=False)
# check if defect angle smaller than 90 degree
defects = cv2.convexityDefects(cnt, hull)
count_defects = 0
if defects != None:
for i in range(defects.shape[0]):
s, e, f, d = defects[i, 0]
start = tuple(cnt[s][0])
end = tuple(cnt[e][0])
far = tuple(cnt[f][0])
a = math.sqrt((end[0] - start[0])**2 +
(end[1] - start[1])**2)
b = math.sqrt((far[0] - start[0])**2 +
(far[1] - start[1])**2)
c = math.sqrt((end[0] - far[0])**2 + (end[1] - far[1])**2)
angle = math.acos((b**2 + c**2 - a**2) / (2 * b * c)) * 57
if angle <= 90:
count_defects += 1
cv2.circle(roi_img, far, 1, [0, 0, 255], 3)
cv2.line(roi_img, start, end, [0, 255, 0], 3)
print count_defects
# draw the ROI region
cv2.rectangle(frame, (y2, x2), (y1, x1), (0, 255, 0), 3)
# show the skin in the image along with the mask
cv2.imshow("images", np.hstack([roi_img, skin]))
# show camera image
cv2.imshow('camera', frame)
#cv2.imshow('gray', skinGray)
cv2.imshow('drawing', drawing)
# if the 'q' key is pressed, stop the loop
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()
from skimage import exposure
import numpy as np
import argparse
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-q", "--query", required=True, help="Path to the query image")
args = vars(ap.parse_args())
# load the query image, compute the ratio of the old height
# to the new height, clone it, and resize it
image = cv2.imread(args["query"])
ratio = image.shape[0] / 300.0
orig = image.copy()
image = imutils.resize(image, height=300)
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(gray, 30, 200)
# find contours in the edged image, keep only the largest
# ones, and initialize our screen contour
(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:10]
screenCnt = None
# loop over our contours
ap.add_argument("-q", "--query", required=True, help="Path to the query image")
args = vars(ap.parse_args())
# load the index
index = open(args["index"], 'rb').read()
index = pickle.loads(index)
#Unpickling a python 2 object with python 3
#change ""index = pickle.loads(index)""
#to ""index = pickle.loads(index, encoding='latin1')""
# load the query image, convert it to grayscale, and
# resize it
image = cv2.imread(args["query"])
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = imutils.resize(image, width=64)
# threshold the image
thresh = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 11, 7)
# initialize the outline image, find the outermost
# contours (the outline) of the pokemon, then draw
# it
outline = np.zeros(image.shape, dtype="uint8")
(_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# remember! 3 output
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
cv2.drawContours(outline, [cnts], -1, 255, -1)
def init_image(self, image):
ratio = image.shape[0] / 600.0
orig = image.copy()
image = imutils.resize(image, height = 600)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(gray, 30, 200)
#cv2.imshow("Edged", edged)
_, cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:10]
screenCnt = None
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4:
screenCnt = approx
break
if screenCnt != None:
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 3)
#cv2.imshow("Edged", image)
pts = screenCnt.reshape(4, 2)
rect = np.zeros((4, 2), dtype = "float32")
s = pts.sum(axis = 1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
diff = np.diff(pts, axis = 1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
rect *= ratio
(tl, tr, br, bl) = rect
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))
heightA = np.sqrt(((tr[0] - [br[0]]) ** 2) + ((tr[1] - bl[1]) ** 2))
heightB = np.sqrt(((tl[0] - [bl[0]]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
maxHeight = max(int(heightA), int(heightB))
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]
], dtype = "float32")
if self.check_screen(maxWidth, maxHeight):
#print maxWidth, maxHeight
M = cv2.getPerspectiveTransform(rect, dst)
warp = cv2.warpPerspective(orig, M, (maxWidth, maxHeight))
#cv2.imshow("Warp", warp)
gray = cv2.cvtColor(warp, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray, 17, 17, 17)
athresholded = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
ret, thresholded = cv2.threshold(gray,60, 255, cv2.THRESH_BINARY)
#cv2.namedWindow("Grayscale")
#cv2.setMouseCallback("Grayscale", self.onMouseClick)
self.athrimg = athresholded.copy()
self.thrimg = thresholded.copy()
self.display = athresholded.copy()
self.display = cv2.cvtColor(self.display, cv2.COLOR_GRAY2BGR)
#cv2.imshow("Grayscale", self.athrimg)
# pause
#self.wait = 0
#cv2.imshow("THRES", self.thrimg)
# wait
#cv2.waitKey(0)
return True
return False
# construct the argument parser and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required = True,
# help = "Path to the image to be scanned")
# args = vars(ap.parse_args())
args= {}
args["image"]= 'images/receipt.jpg'
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
image = cv2.imread(args["image"])
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height = 500) # This reshapes in both dimensions with the same factor so that we get height = 500
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# show the original image and the edge detected image
print "STEP 1: Edge Detection"
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()
# find the contours in the edged image, keeping only the
# load the puzzle and waldo images
puzzle = cv2.imread(args["puzzle"])
waldo = cv2.imread(args["waldo"])
(waldoHeight, waldoWidth) = waldo.shape[:2]
# find the waldo in the puzzle
result = cv2.matchTemplate(puzzle, waldo, cv2.TM_CCOEFF)
(_, _, minLoc, maxLoc) = cv2.minMaxLoc(result)
#grab the bounding box of waldo and extract him from
# the puzzle image
topLeft = maxLoc
botRight = (topLeft[0] + waldoWidth, topLeft[1] + waldoHeight)
roi = puzzle[topLeft[1]:botRight[1], topLeft[0]:botRight[0]]
# construct a darkened transparent 'layer' to darken everything
# in the puzzle except for waldo
mask = np.zeros(puzzle.shape, dtype = "uint8")
puzzle = cv2.addWeighted(puzzle, 0.25, mask, 0.75, 0)
# put the original waldo back in the image so that he is
# 'brighter' than the rest of the image
puzzle[topLeft[1]:botRight[1], topLeft[0]:botRight[0]] = roi
# display the images
cv2.imshow("Puzzle", imutils.resize(puzzle, height = 650))
cv2.imshow("Waldo", waldo)
cv2.waitKey(0)
roi = gray[y:y + h, x:x + w]
thresh = roi.copy()
T = mahotas.thresholding.otsu(roi)
thresh[thresh > T] = 255
thresh = cv2.bitwise_not(thresh)
# deskew the image center its extent
thresh = dataset.deskew(thresh, 20)
thresh = dataset.center_extent(thresh, (20, 20))
big_thresh = cv2.resize(thresh,(int(thresh.shape[1]*5),int(thresh.shape[0]*5)))
print(str(thresh.shape[1])+" "+str(thresh.shape[0]))
cv2.imshow("thresh2", big_thresh)
cv2.imshow("thresh", thresh)
thresh_tf = imutils.resize(thresh, height = 28, width = 28)
my_image = np.array([thresh_tf])
prediction=tf.argmax(y_conv,1)
my_image = np.array(thresh_tf.reshape(1, 784))
my_image = my_image / 255
my_image = my_image.astype('Float32')
digit = prediction.eval(feed_dict={x_: my_image,keep_prob: 1.0}, session=sess)[0]
# extract features from the image and classify it
print("I think that number is: {}".format(digit))
# draw a rectangle around the digit, the show what the
# digit was classified as
# construct the face detector and allow the camera to warm # up fd = FaceDetector(args["face"]) time.sleep(0.1) checkOK = False # capture frames from the camera for f in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True): # grab the raw NumPy array representing the image frame = f.array # resize the frame and convert it to grayscale frame = imutils.resize(frame, width = 300) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) color = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # detect faces in the image and then clone the frame # so that we can draw on it faceRects = fd.detect(gray, scaleFactor = 1.1, minNeighbors = 5, minSize = (30, 30)) frameClone = frame.copy() # loop over the face bounding boxes and draw them fX=0 fY=0 fW=0
'''
hist,bins = np.histogram(im.flatten(),256,[0,256])
plt_one = plt.figure(1)
cdf = hist.cumsum()
cdf_normalized = cdf * hist.max()/ cdf.max()
cdf_m = np.ma.masked_equal(cdf,0)
cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
cdf = np.ma.filled(cdf_m,0).astype('uint8')
im = cdf[im]
'''
###
#im = np.fliplr(im)
im = imutils.resize(im, height=500)
imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# Contour detection
#ret,thresh = cv2.threshold(imgray,127,255,0)
imgray = cv2.GaussianBlur(imgray, (5, 5), 0)
imgray = cv2.adaptiveThreshold(imgray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.THRESH_BINARY,11,2)
thresh = imgray
ret, frame = cap.read()
cv2.imwrite(dir_initials + "initial_" + letters[l] + "-1.png", frame)
for i in range(1, 21):
cap.set(1, i)
ret, frame = cap.read()
cv2.imwrite(
dir_frames + "initial_" + letters[l] + "-1-" + format(k) + ".png",
frame)
k += 1
for l in range(31):
print("Select skin color initial frame of letter " + letters[l])
image_initial = cv2.imread(dir_initials + "initial_" + letters[l] +
"-1.png")
initial = imutils.resize(image_initial, width=400, height=225)
cv2.namedWindow('images')
cv2.setMouseCallback('images', draw_circle)
cv2.imshow("images", initial)
brP = cv2.waitKey(0) & 0xff
if brP == 32: # SpaceBar key to stop
cv2.destroyAllWindows()
print("Separate hand from each frame of letter " + letters[l])
for i in range(1, 21):
a = cv2.imread(dir_frames + "initial_" + letters[l] + "-1-" +
format(i) + ".png")
# a = cv2.imread("C:\Users\Kosara\Documents\DIPLOMA THESIS\handgesture-imageprocessing-master/dataset/frames skin renamed/"
# + letters[l] + "-" + str(folder) + "-" + format(i) + ".png")
initial = imutils.resize(a, width=400, height=300)
blank_image = np.zeros((300, 400, 3), np.uint8)
def getSkinColor(pathToImage, outpath_onlySkin, outpath_Kmean, outpath_AGC):
# define the upper and lower boundaries of the HSV pixel
# intensities to be considered 'skin'
lower_a = np.array([0, 5, 40], dtype="uint8")
upper_a = np.array([25, 255, 255], dtype = "uint8")
lower_b = np.array([160, 10, 230], dtype="uint8")
upper_b = np.array([179, 30, 250], dtype="uint8")
lower_c = np.array([0, 70, 70], dtype="uint8")
upper_c = np.array([3, 85, 98], dtype="uint8")
# get the image from the file path
frame = cv2.imread(pathToImage)
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
# the speicifed upper and lower boundaries
frame = imutils.resize(frame, width = 300, height=300)
frame = cv2.medianBlur(frame, 9)
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
skinMask_a = cv2.inRange(converted, lower_a, upper_a)
skinMask_b = cv2.inRange(converted, lower_b, upper_b)
skinMask_c = cv2.inRange(converted, lower_c, upper_c)
# combine the masks (edge cases handled here)
skinMask = skinMask_a + skinMask_b + skinMask_c
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
skinMask = cv2.erode(skinMask, kernel, iterations = 2)
skinMask = cv2.dilate(skinMask, kernel, iterations = 2)
# blur the mask to help remove noise, then apply the
# mask to the frame
skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
skin = cv2.bitwise_and(frame, frame, mask = skinMask)
# show the skin in the image along with the mask
cv2.imwrite(outpath_onlySkin, skin)
# do the action money shot!
# quant = cluster_and_mark(skin)
quant = cluster_and_categorize(skin)
# save outputs of k-mean clustering
cv2.imwrite(outpath_Kmean, quant)
# get solid 150x150 image of the detected skin color
k_color = np.zeros((200, 200, 3), np.uint8)
k_color[:] = get_skin_color_from_hist(quant)
skin_type = categorize_skin_color(k_color[50:51,50:51])
print "Image - ", pathToImage, "\n---------------"
print "Your skin color is: ", k_color[50,50]
print "Your skin type is: ", skin_type, "\n"
cv2.putText(k_color, '#{}'.format(skin_type), (10, 60), cv2.FONT_HERSHEY_COMPLEX_SMALL, 3, (255,255,255),4)
cv2.imwrite(outpath_AGC, k_color)
return (k_color[50,50], skin_type)
for item in datas:
#if item[0] == 255 and item[1] == 255 and item[2] == 255:
#if item[0] > 75 and item[0] < 225: # Colors in background
if item[0] < 75 or item[0] > 225: # Colors in foreground objects
#newData.append((255, 255, 255, 0))
#newData.append((0, 0, 0, 0)) # Make the colors black
newData.append((0,0)) # Make the colors black
else:
newData.append(item)
print("--- %s seconds ---" % (time() - start_time))
img.putdata(newData)
img.save("img2.png", "PNG")
img = cv2.imread('img2.png')
img = imutils.resize(img, height = 700)
cv2.imshow('result', img)
cv2.waitKey(0)
#----------------------------------------------------------#
img = Image.open('img2.png')
img = img.convert("RGBA")
datas = img.getdata()
newData = []
for item in datas:
if item[0] == 0 and item[1] == 0 and item[2] == 0:
newData.append((0, 0, 255, 0))
else:
newData.append(item)
else:
camera = cv2.VideoCapture(args["video"])
(grabbed, frame) = camera.read()
if args.get("video") and not grabbed:
exit(0)
focal_len = focal_len if focal_len is not None else dist.cfg_cam()
print "------------------------------------------------BEGIN------------------------------------------------"
# Video Size Diagnostics
# (grabbed, frame) = camera.read()
# print frame.shape
frame = imutils.resize(frame, width=800)
# Init Video
# vid.vid_init(frame.shape[1], frame.shape[0])
roiY_old = roiY
roiX_old = roiX
roiHeight_old = roiHeight
roiWidth_old = roiWidth
roiX = 0
roiY = frame.shape[0] / 2
roiWidth = frame.shape[1]
roiHeight = frame.shape[0]
print frame.shape
# if we are viewing a video and we did not grab a
# frame, then we have reached the end of the video
if args.get("video") and not grabbed:
break
#Reset Surface Area: #DO I NEED THAT FOR MAC?
areaNumber = 0
# grab the raw NumPy array representing the image - ONLY FOR PI?
# frame = f.array
# resize the frame and convert it to grayscale
frame = cv2.flip(frame, 1)
frameorig = frame
frame = imutils.resize(frame, width=resizeTo)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
grayorig = cv2.cvtColor(frameorig, cv2.COLOR_BGR2GRAY)
# gray = cv2.GaussianBlur(gray, (21, 21), 0)
# detect faces in the image and then clone the frame
# so that we can draw on it
faceRects = fd.detect(gray,
scaleFactor=1.11,
minNeighbors=5,
minSize=(minValue, minValue))
frameClone = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB) #
# frameClone = frame.copy() # (the original line)
# frameClone = gray
overlay = np.zeros((screenHeight, screenWidth, 3),
np.uint8) # EXP overlay maybe for rects on fullres?
wordarr = line.split()
for index in xrange(len(wordarr)/2, len(wordarr)):
if isprice(wordarr[index]):
retlist.append(line)
return retlist
filenamearr = ["Sobeys.jpg", "UWBOOK.jpg", "Walmart.jpeg"]
for filename in filenamearr:
image = cv2.imread("/Users/Jack/Desktop/TestData/" + filename)
ratio = image.shape[0] / 500.0
orig = image.copy()
o_height = image.shape[0]
image = imutils.resize(image, height = 500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
im2, cnts, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse = True)[:5]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4:
screenCnt = approx
break
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True,
help = "Path to the image to be scanned")
args = vars(ap.parse_args())
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
print args['image']
image = cv2.imread(args["image"])
print image
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height = 500)
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# show the original image and the edge detected image
print "STEP 1: Edge Detection"
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()
# find the contours in the edged image, keeping only the
return group_morse, morse_groups, morse_groups_cent, morse_grp_cnts
def get_morse_text(morse_groups, morse_groups_cent):
morse_text = []
for i in range(len(morse_groups)):
morse_text.append(translate_morse_to_letters(find_morse_message(morse_groups[i], morse_groups_cent[i])))
return morse_text
if __name__ == '__main__':
image, true_orig = scan_image()
orig = image.copy()
grey_image = crop_and_clear_image(image)
cv2.imshow("After cropping", imutils.resize(grey_image, height=650))
cv2.waitKey(0)
morse_cent, morse_cnts = find_centroids(grey_image)
group_morse = grey_image.copy()
group_morse, morse_groups, morse_groups_cent, morse_grp_cnts = find_morse_contours(group_morse)
cv2.imshow("Morse code grouped", imutils.resize(group_morse, height=650))
cv2.waitKey(0)
morse_text = get_morse_text(morse_groups, morse_groups_cent)
for i in range(len(morse_groups)):
center = morse_groups_cent[i][0]
cv2.putText(orig, morse_text[i], (center[0], center[1]), cv2.FONT_HERSHEY_COMPLEX, 4, (0, 0, 255), 10)
cv2.imshow("Original", imutils.resize(true_orig, height=650))
cv2.imshow("Morse code scanned", imutils.resize(orig, height=650))
cv2.waitKey(0)
def scanDoc():
#not sure how to grab the file when it gets posted, but it should get passed into cv2.imread("IMAGE goes here") I was going to try this:
# dlImage = request.files['file']
# print dlImage.content_type
# print dlImage.filename
# # print dlImage.read()
# print "HERE"
# img = dlImage
# print img
# img = cv2.imdecode(numpy.fromstring(request.files['file'].read(), numpy.uint8), cv2.CV_LOAD_IMAGE_UNCHANGED)
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
# img = jsonFile.read()
# imgDaat = json.load(request.json)
# print imgDaat["file"]
# response = urllib2.Request(urlFinal)
json = request.json['file']
# print print open(json).read().decode('string-escape').decode("utf-8")
# print type(json)
# print json['files']
# # img = json.loads(get_info())
# print img
# print request.files['file']
# afterrequest
image = cv2.imdecode(np.fromstring(request.json['file'], np.uint8), cv2.CV_LOAD_IMAGE_UNCHANGED)
print 'CV2'
print image
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height = 500)
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# show the original image and the edge detected image
print "STEP 1: Edge Detection"
# find the contours in the edged image, keeping only the
# largest ones, and initialize the screen contour
(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]
# loop over the contours
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# if our approximated contour has four points, then we
# can assume that we have found our screen
if len(approx) == 4:
screenCnt = approx
break
# show the contour (outline) of the piece of paper
print "STEP 2: Find contours of paper"
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
# apply the four point transform to obtain a top-down
# view of the original image
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
# convert the warped image to grayscale, then threshold it
# to give it that 'black and white' paper effect
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
warped = threshold_adaptive(warped, 250, offset = 10)
warped = warped.astype("uint8") * 255
scanImage = imutils.resize(warped, height = 650)
print warped
print scanImage
# show the original and scanned images
print "STEP 3: Apply perspective transform"
cv2.startWindowThread()
cv2.namedWindow("preview")
cv2.imshow("Original", imutils.resize(orig, height = 650))
cv2.imshow("Scanned", scanImage)
cv2.waitKey(0)
return send_file(scanImage, mimetype='image/jpeg');
def readReceipt(path):
image = cv2.imread(path)
if (ap.parse_args().resize):
image = imutils.resize(image, height = 500)
return image
# im = cv2.imread('images/anthony-1.jpg')
# im = cv2.imread('images/car_two.jpg')
# im = cv2.imread('images/object_group_1.jpg')
im = cv2.imread("images/beach_trash_3.jpg")
# im = cv2.imread('images/circles1.png')
# im = cv2.imread('images/waterbottle_0.jpg')
# cv2.imshow('Original', im)
# Histogram equalization to improve contrast
###
# im = np.fliplr(im)
im = imutils.resize(im, height=400)
imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# Contour detection
# ret,thresh = cv2.threshold(imgray,127,255,0)
# imgray = cv2.GaussianBlur(imgray, (5, 5), 200)
imgray = cv2.medianBlur(imgray, 11)
cv2.imshow("Blurred", imgray)
"""
hist,bins = np.histogram(imgray.flatten(),256,[0,256])
plt_one = plt.figure(1)
cdf = hist.cumsum()
from matplotlib import pyplot as plt
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True,
help = "Path to the image to be scanned")
args = vars(ap.parse_args())
# Read in image.
image = cv2.imread(args["image"])
# Resize as the images are hi-res.
ratio = image.shape[0] / 500.0
# Keep a copy of original.
orig = image.copy()
# Resize image.
image = imutils.resize(image, height = 500)
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY )
ret,thresh1 = cv2.threshold(image,100,255,cv2.THRESH_BINARY)
ret,thresh2 = cv2.threshold(image,100,255,cv2.THRESH_BINARY_INV)
ret,thresh3 = cv2.threshold(image,100,255,cv2.THRESH_TRUNC)
ret,thresh4 = cv2.threshold(image,100,255,cv2.THRESH_TOZERO)
ret,thresh5 = cv2.threshold(image,100,255,cv2.THRESH_TOZERO_INV)
thresh = ['image','thresh1','thresh2','thresh3','thresh4','thresh5']
for i in xrange(6):
plt.subplot(2,3,i+1),plt.imshow(eval(thresh[i]),'gray')
plt.title(thresh[i])
from skimage.filter import threshold_adaptive
import argparse
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
args = vars(ap.parse_args())
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
image = cv2.imread(args["image"])
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height=500)
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# show the original image and the edge detected image
print "STEP 1: Edge Detection"
# find the contours in the edged image, keeping only the
# largest ones, and initialize the screen contour
(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
faceRects = fd.detect(gray, scaleFactor = 1.3, minNeighbors = 5,minSize = (100, 100))
frameClone = frame.copy()
face = []
# loop over the face bounding boxes and draw them
for (fX, fY, fW, fH) in faceRects:
try:
#find centroid of the rectangle
cx = fX + int(fW/2)
cy = fY + int(fH/2)
fX -= int(fX/10*offset_constant)
fY -= int(fY/10*offset_constant)
fW += int(int(fX/10)*offset_constant) * 2
face = frameClone[fY:fY+int((fW*proportional_w)/proportional_h),fX:fX+fW]
#resize
face_resized = imutils.resize(face, width = larghezza_foto)
blank_image = face_resized
except:
pass
#merge
(h, w) = blank_image.shape[:2]
image_patente[65:int(65+h), 19:int(19+w)] = blank_image
#show
cv2.imshow("LIVE", image_patente)
# if the '1' key is pressed, stop the loop
c = cv2.waitKey(1)
if '1' == chr(c & 255):
cv2.destroyWindow("LIVE")
break
box = frame.copy()
(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:4]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4:
try:
oldarea = cv2.contourArea(approx)
change = abs(cv2.contourArea(screenCnt) - oldarea)
if change < oldarea/4:
screenCnt = approx
cv2.drawContours(box, [screenCnt], -1, (0, 255, 0), 2)
except:
screenCnt = approx
break
warped = four_point_transform(frame, screenCnt.reshape(4, 2))
cv2.imshow("Original", imutils.resize(box, height = 350))
cv2.imshow("Gray", imutils.resize(gray, height = 350))
cv2.imshow("Scanned", cv2.resize(warped, (872, 800)))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()