def histogram_match(source_img, template_img):
img = source_img
img_ref = template_img
height = img.shape[0]
width = img.shape[1]
pixels = width * height
height_ref = img_ref.shape[0]
width_ref = img_ref.shape[1]
pixels_ref = width_ref * height_ref
hist = h.histogram(img)
hist_ref = h.histogram(img_ref)
cum_hist = ch.cumulative_histogram(hist)
cum_hist_ref = ch.cumulative_histogram(hist_ref)
prob_cum_hist = cum_hist / pixels
prob_cum_hist_ref = cum_hist_ref / pixels_ref
K = 256
new_values = np.zeros((K))
for a in np.arange(K):
j = K - 1
while True:
new_values[a] = j
j = j - 1
if j < 0 or prob_cum_hist[a] > prob_cum_hist_ref[j]:
break
for i in np.arange(height):
for j in np.arange(width):
a = img.item(i, j)
b = new_values[a]
img.itemset((i, j), b)
return img
def mac(self):
image = Image.open(files)
image.save("c.png")
img = cv2.imread('c.png', cv2.IMREAD_GRAYSCALE)
height = img.shape[0]
width = img.shape[1]
pixels = width * height
hist = h.histogram(img)
cum_hist = ch.cumulative_histogram(hist)
p = 0.005
a_low = 0
for i in np.arange(256):
if cum_hist[i] >= pixels * p:
a_low = i
break
a_high = 255
for i in np.arange(255, -1, -1):
if cum_hist[i] <= pixels * (1 - p):
a_high = i
break
for i in np.arange(height):
for j in np.arange(width):
a = img.item(i, j)
b = 0
if a <= a_low:
b = 0
elif a >= a_high:
b = 255
else:
b = float(a - a_low) / (a_high - a_low) * 255
img.itemset((i, j), b)
cv2.imwrite('output/output.png', img)
pixmap = QtGui.QPixmap("output/output.png") # Setup pixmap with the provided image
pixmap = pixmap.scaled(self.imagelab2.width(), self.imagelab2.height(), QtCore.Qt.KeepAspectRatio) # Scale pixmap
self.imagelab2.setPixmap(pixmap) # Set the pixmap onto the label
self.imagelab2.setAlignment(QtCore.Qt.AlignCenter)
#cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.destroyAllWindows()
if len(screenCnt) == 4:
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
else:
warped = orig
# 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)
height = warped.shape[0]
width = warped.shape[1]
pixels = width * height
hist = h.histogram(warped)
cum_hist = ch.cumulative_histogram(hist)
brightness = 40
p = 0.005
a_low = 0
for i in np.arange(256):
if cum_hist[i] >= pixels * p:
a_low = i
break
a_high = 255
for i in np.arange(255, -1, -1):
if cum_hist[i] <= pixels * (1 - p):
a_high = i
break
img = cv2.imread('images/cat.jpg', cv2.IMREAD_GRAYSCALE)
img_ref = cv2.imread('images/img1.jpg', cv2.IMREAD_GRAYSCALE)
height = img.shape[0]
width = img.shape[1]
pixels = width * height
height_ref = img_ref.shape[0]
width_ref = img_ref.shape[1]
pixels_ref = width_ref * height_ref
hist = h.histogram(img)
hist_ref = h.histogram(img_ref)
cum_hist = ch.cumulative_histogram(hist)
cum_hist_ref = ch.cumulative_histogram(hist_ref)
prob_cum_hist = cum_hist / pixels
prob_cum_hist_ref = cum_hist_ref / pixels_ref
K = 256
new_values = np.zeros((K))
for a in np.arange(K):
j = K - 1
while True:
new_values[a] = j
j = j - 1
if j < 0 or prob_cum_hist[a] > prob_cum_hist_ref[j]: