[英]How to apply threshold within multiple rectangular bounding boxes in an image?
我的问题是:我对图像中对象周围的边界框有ROI。 ROI是由更快的R-CNN获得的。 现在我想要的是应用阈值来使对象准确地包含在边界框内。 此图像的ROI由更快的RCNN获得。
因此,在获得ROI之后,我只选择了图像中的ROI并粘贴在相同大小和尺寸的黑色图像上,从而得到以下图像。
正如您所看到的那样,盒子是矩形的,因此在某些地方它会覆盖一些背景区域以及尖峰。 那么,我如何应用阈值处理才能使尖峰和其他像素变为黑色?
编辑 :我已添加到问题中第一个图像的ROI文本文件的链接
使用cv2.inRange()
颜色阈值cv2.inRange()
应该在这里工作。 我假设你想要隔离绿色区域
这是主要的想法
您还可以在获得蒙版后执行形态学操作以平滑或消除噪音
import numpy as np
import cv2
image = cv2.imread('1.jpg')
result = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([18, 0, 0])
upper = np.array([179, 255, 255])
mask = cv2.inRange(image, lower, upper)
result = cv2.bitwise_and(result,result, mask=mask)
cv2.imshow('result', result)
cv2.imwrite('result.png', result)
cv2.waitKey()
您可以使用HSV颜色阈值脚本来隔离所需的颜色范围
import cv2
import sys
import numpy as np
def nothing(x):
pass
# Create a window
cv2.namedWindow('image')
# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)
# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)
# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0
img = cv2.imread('1.jpg')
output = img
waitTime = 33
while(1):
# get current positions of all trackbars
hMin = cv2.getTrackbarPos('HMin','image')
sMin = cv2.getTrackbarPos('SMin','image')
vMin = cv2.getTrackbarPos('VMin','image')
hMax = cv2.getTrackbarPos('HMax','image')
sMax = cv2.getTrackbarPos('SMax','image')
vMax = cv2.getTrackbarPos('VMax','image')
# Set minimum and max HSV values to display
lower = np.array([hMin, sMin, vMin])
upper = np.array([hMax, sMax, vMax])
# Create HSV Image and threshold into a range.
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower, upper)
output = cv2.bitwise_and(img,img, mask= mask)
# Print if there is a change in HSV value
if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
phMin = hMin
psMin = sMin
pvMin = vMin
phMax = hMax
psMax = sMax
pvMax = vMax
# Display output image
cv2.imshow('image',output)
# Wait longer to prevent freeze for videos.
if cv2.waitKey(waitTime) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
这是原始图像的结果
在TensorFlow检测中,运行预测后得到的输出字典有一个字段“detection_scores”。
output_dict = sess.run(tensor_dict,feed_dict={image_tensor: image})
设置一个阈值,
indexes=np.where(output_dict['detection_scores']>0.5)
使用这些框,即output_dict ['detection_boxes']仅对您在上一步中过滤的特定索引。
[编辑]在评论中讨论后添加更多代码
#convert the image to hsv
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
#tune the numbers below accordingly
lower_green = np.array([60, 100, 50])
upper_green = np.array([60 , 255, 255])
mask = cv2.inRange(hsv, lower_green, upper_green)
res = cv2.bitwise_and(frame,frame, mask= mask)
#res has the output masked image
[编辑]使用问题中给出的实际图像进行编辑
img=cv2.imread("idJyc.jpg")
lower_green = np.array([0, 10, 0])
upper_green = np.array([255 , 100, 255])
mask = cv2.inRange(img, lower_green, upper_green)
mask = np.abs(255-mask)
res = cv2.bitwise_and(img,img, mask=mask)
cv2.imshow("a",res)
cv2.waitKey(0)
添加输出图像供您参考。
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