Проблема с использованием целостного обнаружения краев

Question

Я использую алгоритм HED для лучшей техники обнаружения краев, чем canny, однако я остановился на этом изображении, поскольку оно дает мне более низкое качество, чем canny.

Может ли кто-нибудь помочь мне, как улучшить используемый алгоритм HED или есть какие-либо изменения в алгоритме, которые я должен сделать, чтобы лучше определить края на изображении.

Мои основные исследованияТочка предполагает обнаружение зерен частиц почвы и определение ее значения.

Используемый алгоритм

# import the necessary packages
import argparse
import cv2
import os
import easygui

path = easygui.fileopenbox()
print(path)
hdir = os.path.dirname(path)
print(hdir)
hfilename = os.path.basename(path)
print(hfilename)
hname = os.path.splitext(hfilename)[0]
print(hname)
houtname = hname+"_out.jpg"
print(houtname)
hout = os.path.sep.join([hdir,houtname])
print(hout)
# # 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...")

fpath = os.path.abspath(__file__)
fdir =  os.path.dirname(fpath)
print(fdir)
protoPath = os.path.sep.join([fdir,"hed_model", "deploy.prototxt"])
print(protoPath)
modelPath =  os.path.sep.join([fdir,"hed_model","hed_pretrained_bsds.caffemodel"])
print(modelPath)

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('D:\My work\MASTERS WORK\sandd.jpg')
(H, W) = image.shape[:2]

# 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

# cc = cv2.cvtColor(canny, cv2.COLOR_GRAY2BGR)
# image = image+cc

# mean = (104.00698793, 116.66876762, 122.67891434),

blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(W, H),
                             # mean=(230, 120, 50),
                             mean=(104.00698793, 116.66876762, 122.67891434),
                             swapRB=False, crop=False)
print( blob)
cv2.waitKey(0)
# 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()
hed = cv2.resize(hed[0, 0], (W, H))
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.imwrite(hout, hed)
cv2.waitKey(0)