What is hed[0,0] in Holistically Nested Edge Detection OpenCv Python?

Question

I am new to python also to openCv. I am trying out the code which detects edges with deep Learning and trying to understand it. I found this code this website https://www.pyimagesearch.com

# 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", type=str, required=True,
    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)

Can any one explain what hed[0,0] returns and what hed = cv2.resize(hed[0,0], (W, H)) does? Also hed = (255 * hed).astype("uint8")

if possible can explain in different Language like JAVA or C++

Answer 1

What I found is here

after FeedForward process:

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

your answer is: (1, 1, 2121, 4148)

when you doing resize:

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

your answer becomes is: (2121, 4148)

so it seems you are reducing the dimension of the array (hed) to fit H,W of the original image.

Answer 2

yah. same question I also have.

But it seems like it makes a resize of the hed image to new dimensions H and W.

example below:

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