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我想在 python 中将此图像的背景更改为白色背景。我尝试了精明的边缘检测，但很难找到产品顶部的边缘，如您在第二张图片中所见。我尝试了不同的阈值，但这会导致更多的背景不是白色的。这可能是由于图像中产品的顶部与背景颜色几乎相同。

有没有一种方法可以检测出这样的微小差异？我也试过在产品后面加个绿屏，但是因为产品的反光状态，产品变绿了。

这是我的代码：

import cv2
import numpy as np
from skimage import filters
import matplotlib.pyplot as plt
from os import listdir
from os.path import isfile, join

plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
plt.rcParams['figure.dpi'] = 200
#== Parameters =======================================================================
BLUR = 15
CANNY_THRESH_1 = 10
CANNY_THRESH_2 = 255
MASK_DILATE_ITER = 10
MASK_ERODE_ITER = 10
MASK_COLOR = (1.0,1.0,1.0) # In BGR format


#== Processing =======================================================================
mypath = "./images"
images = [f for f in listdir(mypath) if isfile(join(mypath, f))]
#-- Read image -----------------------------------------------------------------------
for image in images:
    img_loc = mypath + "/" + image
    img = cv2.imread(img_loc)
    # threshold
    img_thresh = img
    thresh = 180
    img_thresh[ img_thresh >= thresh ] = 255
    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]
    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 
    result_dir = "./results/" + image
    cv2.imwrite(result_dir, masked)           # Save

图像前

后像

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