Question

我正在尝试使下图中的背景透明。参见下面的图片。

使用Opencv和matplotlib，我能够实现这一目标。

import cv2
import numpy as np
from matplotlib import pyplot as plt

#== Parameters =======================================================================
BLUR = 21
CANNY_THRESH_1 = 10
CANNY_THRESH_2 = 200
MASK_DILATE_ITER = 10
MASK_ERODE_ITER = 10
MASK_COLOR = (0.0,0.0,1.0) # In BGR format


#== Processing =======================================================================

#-- Read image -----------------------------------------------------------------------
img = cv2.imread('/home/hasher/Documents/30302649.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

#-- Edge detection -------------------------------------------------------------------
edges = cv2.Canny(gray, CANNY_THRESH_1, CANNY_THRESH_2)
edges = cv2.dilate(edges, None)
edges = cv2.erode(edges, None)

#-- Find contours in edges, sort by area ---------------------------------------------
contour_info = []
_, contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
for c in contours:
    contour_info.append((
        c,
        cv2.isContourConvex(c),
        cv2.contourArea(c),
    ))
contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True)
max_contour = contour_info[0]

#-- Create empty mask, draw filled polygon on it corresponding to largest contour ----
# Mask is black, polygon is white
mask = np.zeros(edges.shape)
cv2.fillConvexPoly(mask, max_contour[0], (255))



#-- Smooth mask, then blur it --------------------------------------------------------
mask = cv2.dilate(mask, None, iterations=MASK_DILATE_ITER)
mask = cv2.erode(mask, None, iterations=MASK_ERODE_ITER)
mask = cv2.GaussianBlur(mask, (BLUR, BLUR), 0)

mask_stack = np.dstack([mask]*3)    # Create 3-channel alpha mask

#-- Blend masked img into MASK_COLOR background --------------------------------------
mask_stack  = mask_stack.astype('float32') / 255.0          # Use float matrices, 
img         = img.astype('float32') / 255.0                 #  for easy blending

masked = (mask_stack * img) + ((1-mask_stack) * MASK_COLOR) # Blend
masked = (masked * 255).astype('uint8')                     # Convert back to 8-bit 

# plt.imsave('/home/hasher/Documents/girl_blue.png', masked)
# split image into channels
c_red, c_green, c_blue = cv2.split(img)

# merge with mask got on one of a previous steps
img_a = cv2.merge((c_blue, c_green, c_red, mask.astype('float32') / 255.0))

# show on screen (optional in jupiter)
#%matplotlib inline
plt.imshow(img_a)
plt.show()

# save to disk
# cv2.imwrite('/home/hasher/Documents/girl_1.png', img_a*255)

# or the same using plt
plt.imsave('/home/hasher/Documents/transparent.png', img_a)

# cv2.imshow('img', masked)  # Displays red, saves blue

cv2.waitKey()

我能够将图像转换为After conversion（参见图像）。但是代码中存在一些小问题。在转换后的图像的边框中有一些额外的细节。我不知道。任何帮助表示赞赏。

转换前的样本。 sample-1 sample-2 sample-3 sample-4

Answer 1

任务：将JPEGs with specific color background转换为transparent PNGs。

（1）JPEG

（2）对于这些jpeg，将它们转换为HSV并分割通道。然后我们可以在V通道中分离目标，因为背景与其他通道最不相同。

（3）设置V通道的阈值并执行morph-op，然后我们可以获得alpha蒙版和png。

代码：

import cv2 
import numpy as np 

fname = "alpha.jpg"
img = cv2.imread(fname)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

v = hsv[:,:,2]
th, threshed = cv2.threshold(v, 100, 255, cv2.THRESH_OTSU|cv2.THRESH_BINARY_INV)
threshed[-1] = 255

cnts = cv2.findContours(threshed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]

mask = np.zeros_like(threshed)
cv2.drawContours(mask, cnts, -1, (255, 0, 0), -1, cv2.LINE_AA)
mask = cv2.erode(mask, np.ones((3,3), np.int32), iterations=1)

png = np.dstack((img, mask))
cv2.imwrite("alpha.png", png)

通过转换为PNG

1 个答案: