整体嵌套边缘检测OpenCv Python中的hed [0,0]是什么？

如何解决整体嵌套边缘检测OpenCv Python中的hed [0,0]是什么？

我对python也是openCv的新手。我正在尝试检测深度学习边缘的代码，并试图理解它。我在网站https://www.pyimagesearch.com

上找到了此代码

# USAGE
# python detect_edges_image.py --edge-detector hed_model --image images/guitar.jpg

# import the necessary packages
import argparse
import cv2
import os

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d","--edge-detector",type=str,required=True,help="path to OpenCV's deep learning edge detector")
ap.add_argument("-i","--image",help="path to input image")
args = vars(ap.parse_args())

class CropLayer(object):
    def __init__(self,params,blobs):
        # initialize our starting and ending (x,y)-coordinates of
        # the crop
        self.startX = 0
        self.startY = 0
        self.endX = 0
        self.endY = 0

    def getMemoryShapes(self,inputs):
        # the crop layer will receive two inputs -- we need to crop
        # the first input blob to match the shape of the second one,# keeping the batch size and number of channels
        (inputShape,targetShape) = (inputs[0],inputs[1])
        (batchSize,numChannels) = (inputShape[0],inputShape[1])
        (H,W) = (targetShape[2],targetShape[3])

        # compute the starting and ending crop coordinates
        self.startX = int((inputShape[3] - targetShape[3]) / 2)
        self.startY = int((inputShape[2] - targetShape[2]) / 2)
        self.endX = self.startX + W
        self.endY = self.startY + H

        # return the shape of the volume (we'll perform the actual
        # crop during the forward pass
        return [[batchSize,numChannels,H,W]]

    def forward(self,inputs):
        # use the derived (x,y)-coordinates to perform the crop
        return [inputs[0][:,:,self.startY:self.endY,self.startX:self.endX]]

# load our serialized edge detector from disk
print("[INFO] loading edge detector...")
protoPath = os.path.sep.join([args["edge_detector"],"deploy.prototxt"])
modelPath = os.path.sep.join([args["edge_detector"],"hed_pretrained_bsds.caffemodel"])
net = cv2.dnn.readNetFromCaffe(protoPath,modelPath)

# register our new layer with the model
cv2.dnn_registerLayer("Crop",CropLayer)

# load the input image and grab its dimensions
image = cv2.imread(args["image"])
(H,W) = image.shape[:2]

print("Height: ",H)
print("Width: ",W)

# convert the image to grayscale,blur it,and perform Canny
# edge detection
print("[INFO] performing Canny edge detection...")
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray,(5,5),0)
canny = cv2.Canny(blurred,30,150)

# construct a blob out of the input image for the Holistically-Nested
# Edge Detector
blob = cv2.dnn.blobFromImage(image,scalefactor=1.0,size=(W,H),mean=(104.00698793,116.66876762,122.67891434),swapRB=False,crop=False)

# set the blob as the input to the network and perform a forward pass
# to compute the edges
print("[INFO] performing holistically-nested edge detection...")
net.setInput(blob)
hed = net.forward()
print("before: ",hed)
hed = cv2.resize(hed[0,0],(W,H))
print("after:",hed)
hed = (255 * hed).astype("uint8")

# show the output edge detection results for Canny and
# Holistically-Nested Edge Detection
cv2.imshow("Input",image)
cv2.imshow("Canny",canny)
cv2.imshow("HED",hed)
cv2.waitKey(0)

有人可以解释hed[0,0]返回什么，hed = cv2.resize(hed[0,H))做什么吗？ 另外hed = (255 * hed).astype("uint8")

如果可能的话，可以用JAVA或C ++等不同语言进行解释

解决方法

我发现的东西在这里

前馈过程后：

hed = net.forward()
print(hed.shape)

你的答案是： (1,1,2121,4148)

当你调整大小时：

hed = cv2.resize(hed[0,0],(W,H))
print (hed.shape)

你的答案变成了： (2121,4148)

所以您似乎正在减少数组 (hed) 的维度以适应原始图像的 H、W。

,

嗯。我也有同样的问题。

但它似乎将 hed 图像的大小调整为新的 H 和 W 尺寸。

示例如下：

newwidth = 350
newheight = 450
dim = (width,height)
 
# resize image
resized = cv2.resize(img,dim,interpolation = cv2.INTER_AREA)