图像边缘检测的新方向——量子算法

import cv2import numpy as np
def canyEdgeDetector(image):    edged = cv2.Canny(image, 50, 150)    return edged

def getROI(image):    height = image.shape[0]    width = image.shape[1]    # Defining Triangular ROI: The values will change as per your camera mounts    # triangle = np.array([[(100, height), (width, height), (width-500, int(height/1.9))]])    triangle = np.array([[(120, height), (width, height), (400, 228)]])    # creating black image same as that of input image    black_image = np.zeros_like(image)    # Put the Triangular shape on top of our Black image to create a mask    mask = cv2.fillPoly(black_image, triangle, 255)    # applying mask on original image    masked_image = cv2.bitwise_and(image, mask)    return masked_image

def getLines(image):    # lines=cv2.HoughLinesP(image,bin_size,precision,threshold,dummy 2d array--no use,minLineLength,maxLineGap)    # lets take bin size to be 2 pixels    # lets take precision to be 1 degree= pi/180 radians    # threshold is the votes that a bin should have to be accepted to draw a line    # minLineLength --the minimum length in pixels a line should have to be accepted.    # maxLineGap --the max gap between 2 broken line which we allow for 2 lines to be connected together.    lines = cv2.HoughLinesP(image, 0.3, np.pi/180, 100, np.array([]),minLineLength=70, maxLineGap=20)    return lines

#display lines over a imagedef displayLines(image, lines):    if lines is not None:        for line in lines:            # print(line) --output like [[704 418 927 641]] this is 2d array representing [[x1,y1,x2,y2]] for each line            x1, y1, x2, y2 = line.reshape(4)  # converting to 1d array []
            # draw line over black image --(255,0,0) tells we want to draw blue line (b,g,r) values 10 is line thickness            cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 10)    return image

def getLineCoordinatesFromParameters(image, line_parameters):    slope = line_parameters[0]    intercept = line_parameters[1]    y1 = image.shape[0]  # since line will always start from bottom of image    y2 = int(y1 * (3.4 / 5))  # some random point at 3/5    x1 = int((y1 - intercept) / slope)    x2 = int((y2 - intercept) / slope)    return np.array([x1, y1, x2, y2])

#Avergaes all the left and right lines found for a lane and retuns single left and right line for the the lanedef getSmoothLines(image, lines):    left_fit = [] # will hold m,c parameters for left side lines    right_fit = [] # will hold m,c parameters for right side lines
    for line in lines:        x1, y1, x2, y2 = line.reshape(4)        # polyfit gives slope(m) and intercept(c) values from input points        # last parameter 1 is for linear..so it will give linear parameters m,c        parameters = np.polyfit((x1, x2), (y1, y2), 1)        slope = parameters[0]        intercept = parameters[1]
    if slope < 0:        left_fit.append((slope, intercept))    else:        right_fit.append((slope, intercept))
# take averages of all intercepts and slopes separately and get 1 single value for slope,intercept# axis=0 means vertically...see its always (row,column)...so row is always 0 position.# so axis 0 means over row(vertically)left_fit_average = np.average(left_fit, axis=0)right_fit_average = np.average(right_fit, axis=0)
# now we have got m,c parameters for left and right line, we need to know x1,y1 x2,y2 parameters
left_line = getLineCoordinatesFromParameters(image, left_fit_average)right_line = getLineCoordinatesFromParameters(image, right_fit_average)
return np.array([left_line, right_line])

image = cv2.imread("3d21d93f722e1e3a5dd8fd8bbcd1c5d0.png") #Load Image
edged_image = canyEdgeDetector(image) # Step 1roi_image = getROI(edged_image) # Step 2
lines = getLines(roi_image)             # Step 3image_with_lines = displayLines(image, lines)
cv2.imshow("Output", roi_image)# cv2.waitKey(0)
# smooth_lines = getSmoothLines(image, lines)    # Step 5# image_with_smooth_lines = displayLines(image, smooth_lines) # Step 4## cv2.imshow("Output", image_with_smooth_lines)cv2.waitKey(0)

from edge_detection import QED,crop_image,plot_imageimport cv2from PIL import Imageimport numpy as npimport matplotlib.pyplot as plt

img = Image.open('./test.jpeg').convert('L')img.save('grey.png')image256 = plt.imread("grey.png")res = cv2.resize(image256, dsize=(256, 256),interpolation=cv2.INTER_CUBIC)res[res\<.5] = 0res[res\>.5] = 1crop = crop_image(res)cropped = crop.image_stackplot_image(res,"original image")

image_out = np.zeros(cropped.shape)  for i in range(len(cropped)):       if (np.sum(cropped[i])>0) * (np.sum(cropped[i])<256):       method = QED(image_in = cropped[i])       out = method.get_scan_images()       image_out[i] = out[2]  image_edge = crop.reshape(image_out)  error_correction = np.ones((256,256))  for i in range(1,16):       error_correction[i*16-1] = 0       for j in range(1,16):          error_correction[:,j*16-1] = 0  
    plot_image(image_edge*error_correction,"edge detected image")