def face_beautify(image,cascade, cascade2, processed_image):
image1 = image[y:y+height, x:x+width]
image_high = image1
eyes = cascade2.detectMultiScale(image1)
for (ex, ey, ew, eh) in eyes:
center_x = ex + ew * 0.5
center_y = ey + eh * 0.5
eyes1 = image1[ey:ey+eh, ex:ex+ew]
eyes2 = eyes1
kernel_radius = min(ew, eh) * 0.4
for r in range(eh):
for c in range(ew):
diff_x = c - ew*0.5
diff_y = r - eh*0.5
distance = math.sqrt(diff_x * diff_x + diff_y * diff_y)
p_x = 0
p_y = 0
if distance <= kernel_radius:
re = (1 - math.cos(distance / kernel_radius * 2 * math.pi)) * 2.5
p_x = -diff_x * (re / kernel_radius)
p_y = -diff_y * (re / kernel_radius)
if p_x < 0 :
p_x = 0
if p_y < 0 :
p_y = 0
eyes2[r,c] = eyes1[int(r + p_y),int(c + p_x)]
image1[ey:ey+eh, ex:ex+ew] = eyes2
image_high1 = cv2.bilateralFilter(image_high, 15, 37, 37)
#image_high2 = image_high1 - image1 + 128
image_high3 = cv2.GaussianBlur(image_high1,(1, 1),0)
#image_high4 = image1 + 2 * image_high3 - 255
#final = image1 * 0.45 + image_high4 * 0.55
c_x = x + width * 0.5
c_y = y + height * 0.5
radius = min(width, height) * 2
image_high4 = image_high3
for row in range(height):
for col in range(width):
diff_x = col - width * 0.5
diff_y = col - height * 0.5
distance = math.sqrt(square(col - width*0.5) + square(row - height*0.5))
m_x = 0
m_y = 0
if distance <= radius:
re = (1 - math.cos(distance / radius * 2 * math.pi)) * 2
m_x = -diff_x * (re / radius)
m_y = -diff_y * (re / radius)
if m_x < 0:
m_x = 0
if m_y < 0:
m_y = 0
image_high4[row,col] = image_high3[int(row + m_y), int(col + m_x)]
image[y:y+height, x:x+width] = image_high4
return image
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