def desaturate(image,method=0):
'''Method:
0: Average
1: Luminosity (from gimp)
2: Lightness (from gimp)'''
mock = image.copy()
imap = mock.unmap_rgb
pxarray = PixelArray(mock)
w,h = mock.get_size()
for y in range(h):
for x in range(w):
r,g,b,a = imap(pxarray[x,y])
if method == 0:
p = (r+g+b)//3
elif method == 1:
p = int(0.21 * r + 0.72 * g + 0.07 * b)
elif method == 2:
p = int(0.5 + (max([r,g,b]) + min([r,g,b])))
pxarray[x,y] = p,p,p,a
render = pxarray.surface
del pxarray
return render
def transformSurface(image):
'''
Apply transform to make query 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, (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.imwrite("imgs/edged.jpg", edged)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# 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
print screenCnt
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)
# cv2.imwrite("imgs/outline.jpg", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
if screenCnt is not None:
# 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)
# show the original and scanned images
#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()
cv2.imwrite("imgs/query.jpg", warped)
def vscroll(scrollval, image):
offs = image.get_height()
newimage = image.copy()
newimage.fill((0, 0, 0, 0))
newimage.blit(image, (0, scrollval))
if (str(scrollval))[0] == "-":
newimage.blit(image, (0, (scrollval + offs)))
else:
newimage.blit(image, (0, (scrollval - offs)))
return newimage
def hscroll(scrollval, image):
offs = image.get_width()
newimage = image.copy()
newimage.fill((0, 0, 0, 0))
newimage.blit(image, (scrollval, 0))
if (str(scrollval))[0] == "-":
newimage.blit(image, ((scrollval + offs), 0))
else:
newimage.blit(image, ((scrollval - offs), 0))
return newimage
def colorize(image,color):
mock = image.copy()
unmap = mock.unmap_rgb
pxarray = PixelArray(mock)
if type(color) is list:
color = Color(*color)
hi = color.hsva[0]/360
w,h = mock.get_size()
for y in range(h):
for x in range(w):
s,v,alpha = Color(*unmap(pxarray[x,y])).hsva[1:]
r,g,b,a = hsv2rgb(hi,s/100,v/100)+[int(alpha)]
pxarray[x,y] = Color(r,g,b,a)
render = pxarray.surface
del pxarray
return render
def detect_eye(
image,
cords): #na całym obrazku rysuje obrys oczu dla kazdej wykrytej twarzy
(x, y, w, h) = cords
face = image.copy()
face[y:y + int(w / 3.3), x:x + h] = (255, 255, 255)
face[y + int(w / 2.1):y + w, x:x + h] = (255, 255, 255)
face[y:y + w, x:x + int(h / 5)] = (255, 255, 255)
face[y:y + w, x + (h - int(h / 5)):x + h] = (255, 255, 255)
imgray = cv2.cvtColor(face[y:y + w, x:x + h], cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
image2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
image[y:y + w, x:x + h] = cv2.drawContours(image[y:y + w,
x:x + h], contours, -1,
(100, 255, 100), 1)
return image
last_pred = 0
while True:
img = cam.get_image()
pygame.image.save(img, path)
img = io.imread(path)
# 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, clone it for output, and then convert it to grayscale
image = cv2.imread("test.bmp")
output_sv = image.copy()
output = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# detect circles in the image
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2, 75)
r_max = 0
x_max = 0
y_max = 0
# ensure at least some circles were found
if circles is not None:
# convert the (x, y) coordinates and radius of the circles to integers
circles = np.round(circles[0, :]).astype("int")
print(circles)
