我有這些影像，所有影像中都有陰影。我的目標是使用這三個影像制作沒有陰影的汽車的單個影像：
最后，我怎樣才能得到這種影像，如下所示：
任何形式的幫助或建議表示贊賞。
uj5u.com熱心網友回復：
這是一個可能的解決方案。總體思路是計算陰影的位置，生成一個識別陰影位置的二元掩碼，并使用此資訊從所有裁剪的子影像中復制像素。
讓我們看看代碼。第一個問題是定位三個影像。我使用黑匣子對每輛車進行分割和裁剪，如下所示：
# Imports:
import cv2
import numpy as np
# image path
path = "D://opencvImages//"
fileName = "qRLI7.png"
# Reading an image in default mode:
inputImage = cv2.imread(path fileName)
# Get the HSV image:
hsvImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2HSV)
# Get the grayscale image:
grayImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
showImage("grayImage", grayImage)
# Threshold via Otsu:
_, binaryImage = cv2.threshold(grayImage, 5, 255, cv2.THRESH_BINARY_INV)
cv2.imshow("binaryImage", binaryImage)
cv2.waitKey(0)
前一位使用grayscale影像的版本并使用閾值 應用固定二值化5。我還預先計算HSV了原始影像的版本。閾值的結果是這樣的：
我正在嘗試獲取黑色矩形并使用它們來裁剪每輛車。讓我們獲取輪廓并按面積過濾它們，因為二值影像上的黑色矩形具有最大的面積：
for i, c in enumerate(currentContour):
# Get the contour's bounding rectangle:
boundRect = cv2.boundingRect(c)
# Get the dimensions of the bounding rect:
rectX = boundRect[0]
rectY = boundRect[1]
rectWidth = boundRect[2]
rectHeight = boundRect[3]
# Get the area:
blobArea = rectWidth * rectHeight
minArea = 20000
if blobArea > minArea:
# Deep local copies:
hsvImage = hsvImage.copy()
localImage = inputImage.copy()
# Get the S channel from the HSV image:
(H, S, V) = cv2.split(hsvImage)
# Crop image:
croppedImage = V[rectY:rectY rectHeight, rectX:rectX rectWidth]
localImage = localImage[rectY:rectY rectHeight, rectX:rectX rectWidth]
_, binaryMask = cv2.threshold(croppedImage, 0, 255, cv2.THRESH_OTSU cv2.THRESH_BINARY_INV)
After filtering each contour to get the biggest one, I need to locate the position of the shadow. The shadow is mostly visible in the HSV color space, particularly, in the V channel. I cropped two versions of the image: The original BGR image, now cropped, and the V cropped channel of the HSV image. This is the binary mask that results from applying an automatic thresholding on the S channel :
To locate the shadow I only need the starting x coordinate and its width, because the shadow is uniform across every cropped image. Its height is equal to each cropped image's height. I reduced the V image to a row, using the SUM mode. This will sum each pixel across all columns. The biggest values will correspond to the position of the shadow:
# Image reduction:
reducedImg = cv2.reduce(binaryMask, 0, cv2.REDUCE_SUM, dtype=cv2.CV_32S)
# Normalize image:
max = np.max(reducedImg)
reducedImg = reducedImg / max
# Clip the values to [0,255]
reducedImg = np.clip((255 * reducedImg), 0, 255)
# Convert the mat type from float to uint8:
reducedImg = reducedImg.astype("uint8")
_, shadowMask = cv2.threshold(reducedImg, 250, 255, cv2.THRESH_BINARY)
The reduced image is just a row:
The white pixels denote the largest values. The location of the shadow is drawn like a horizontal line with the largest area, that is, the most contiguous white pixels. I process this row by getting contours and filtering, again, to the largest area:
# Get the biggest rectangle:
subContour, _ = cv2.findContours(shadowMask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for j, s in enumerate(subContour):
# Get the contour's bounding rectangle:
boundRect = cv2.boundingRect(s)
# Get the dimensions of the bounding rect:
rectX = boundRect[0]
rectY = boundRect[1]
rectWidth = boundRect[2]
rectHeight = boundRect[3]
# Get the area:
blobArea = rectWidth * rectHeight
minArea = 30
if blobArea > minArea:
# Get image dimensions:
(imageHeight, imageWidth) = localImage.shape[:2]
# Set an empty array, this will be the binary mask
shadowMask = np.zeros((imageHeight, imageWidth, 3), np.uint8)
color = (255, 255, 255)
cv2.rectangle(shadowMask, (int(rectX), int(0)),
(int(rectX rectWidth), int(0 imageHeight)), color, -1)
# Invert mask:
shadowMask = 255 - shadowMask
# Store mask and cropped image:
shadowRois.append((shadowMask.copy(), localImage.copy()))
好的，根據這些資訊，我創建了一個蒙版，其中唯一以白色繪制的是蒙版的位置。我將此蒙版和原始BGR裁剪存盤在shadowRois串列中。
下面是一種使用此資訊并創建完整影像的可能方法。這個想法是我使用每個蒙版的資訊來復制所有非蒙版像素。我在緩沖區中累積這些資訊，最初是一個空影像，如下所示：
# Prepare image buffer:
buffer = np.zeros((100, 100, 3), np.uint8)
# Loop through cropped images and produce the final image:
for r in range(len(shadowRois)):
# Get data from the list:
(mask, img) = shadowRois[r]
# Get image dimensions:
(imageHeight, imageWidth) = img.shape[:2]
# Resize the buffer:
newSize = (imageWidth, imageHeight)
buffer = cv2.resize(buffer, newSize, interpolation=cv2.INTER_AREA)
# Get the image mask:
temp = cv2.bitwise_and(img, mask)
# Set info in buffer, substitute the black pixels
# for the new data:
buffer = np.where(temp == (0, 0, 0), buffer, temp)
cv2.imshow("Composite Image", buffer)
cv2.waitKey(0)
結果是這樣的：
uj5u.com熱心網友回復：
根據@fmw42 在評論中的建議，我實作了目標，并在下面發布：
import cv2
import numpy as np
img_1 = cv2.imread('1.png', cv2.COLOR_BGR2RGB)
img_2 = cv2.imread('2.png', cv2.COLOR_BGR2RGB)
img = np.maximum(img_1, img_2)
cv2.imshow('img1', img_1)
cv2.imshow('img2', img_2)
cv2.imshow('img', img)
cv2.waitKey(0)
輸入影像是：
當您運行代碼時，您將得到以下結果：
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