a = len(src_pts1)/2
    a = int (a)
    
    c = src_pts1.ravel()
    d = dst_pts1.ravel()

    canvas1 = gray1.copy()
    canvas2 = gray2.copy()

    for k in range(1,a):
        cv2.circle(canvas1, (c[2*k],c[2*k-1]), 80, (255, 255, 255), -1)
        cv2.circle(canvas2, (d[2*k],d[2*k-1]), 80, (255, 255, 255), -1)
    
    blurred1 = cv2.GaussianBlur(canvas1, (9, 9),0)
    blurred2 = cv2.GaussianBlur(canvas2, (9, 9),0)
    gradX1 = cv2.Sobel(blurred1, ddepth=cv2.CV_32F, dx=1, dy=0)
    gradY1 = cv2.Sobel(blurred1, ddepth=cv2.CV_32F, dx=0, dy=1)

    gradient1 = cv2.subtract(gradX1, gradY1)
    gradient1 = cv2.convertScaleAbs(gradient1)
    
    gradX2 = cv2.Sobel(blurred2, ddepth=cv2.CV_32F, dx=1, dy=0)
    gradY2 = cv2.Sobel(blurred2, ddepth=cv2.CV_32F, dx=0, dy=1)

    gradient2 = cv2.subtract(gradX2, gradY2)
    gradient2 = cv2.convertScaleAbs(gradient2)
    
    blurred = cv2.GaussianBlur(gradient1, (9, 9),0)
    (_, thresh) = cv2.threshold(blurred, 225, 0, 4)
    (_, thresh) = cv2.threshold(thresh, 30, 0, 3)
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (25, 25))
Esempio n. 2
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def gradient(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    sobelx = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize=5)
    sobely = cv2.Sobel(gray, cv2.CV_8U, 0, 1, ksize=5)
    return sobelx, sobely
Esempio n. 3
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def circle_dectet(image):
    penalty = []
    #t1 = time.time()
    shrink = 2
    image = cv2.resize(image, (math.ceil(
        image.shape[1] / shrink), math.ceil(image.shape[0] / shrink)))
    th = 30  # 边缘检测后大于th的才算边界

    gray = cv2.cvtColor(image, cv2.COLOR_BGRA2GRAY)
    gray = cv2.GaussianBlur(gray, (5, 5), 0)

    x = cv2.Sobel(gray, cv2.CV_16S, 1, 0)  # x方向梯度
    y = cv2.Sobel(gray, cv2.CV_16S, 0, 1)  # y方向梯度
    absX = cv2.convertScaleAbs(x)  # 转回uint8
    absY = cv2.convertScaleAbs(y)
    edges = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)  # 各0.5的权重将两个梯度叠加

    #t2 = time.time()
    #print('t1:',t2-t1)

    # edges = skeletonize(edges / 255)
    # print(type(edges))
    # edges = edges.astype('uint8')*255

    # st = time.time()
    # lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 255, minLineLength=min(gray.shape[0], gray.shape[1])/3,
    #                        maxLineGap=20)
    # x_all = []
    # y_all = []
    # if lines is not None:
    #     for line in lines:
    #         x1, y1, x2, y2 = line[0]
    #         cv2.line(edges, (x1, y1), (x2, y2), (0, 0, 0), 1)  # 画线
    # #         x_all.append(x1)
    # #         x_all.append(x2)
    # #         y_all.append(y1)
    # #         y_all.append(y2)
    # #         # plt.imshow(img_line)
    # #         # plt.show()

    # REMOVE THE NOISE

    # area_obj = cv2.contourArea(d)
    # if area_obj / (w*h) >0.2:
    #     edges[y:y+h,x:x+w] = np.zeros((h,w))

    # print('4', time.time() - st)

    shrink2 = 2
    edges = cv2.resize(edges, (math.ceil(
        edges.shape[1] / shrink2), math.ceil(edges.shape[0] / shrink2)))
    dst, edges = cv2.threshold(edges, th, 255,
                               cv2.THRESH_BINARY)  # 大于th的赋值255(白色)
    kernel = np.ones((3, 3), np.uint8)
    edges = cv2.dilate(edges, kernel, iterations=1)
    # edges = cv2.erode(edges,kernel,iterations= 2 )

    contours_person, hier = cv2.findContours(edges, cv2.RETR_EXTERNAL,
                                             cv2.CHAIN_APPROX_SIMPLE)
    for d in contours_person:
        x, y, w, h = cv2.boundingRect(d)
        if d.shape[0] < math.ceil(100 / shrink2):
            edges[y:y + h, x:x + w] = np.zeros((h, w))
    t3 = time.time()
    #print('t2:',t3-t2)
    # 霍夫圆变换
    # dp累加器分辨率与图像分辨率的反比默认1.5,取值范围0.1-10,越小越准
    dp = 2
    # minDist检测到的圆心之间的最小距离。如果参数太小,则除了真实的圆圈之外,还可能会错误地检测到多个邻居圆圈。 如果太大,可能会错过一些圈子。取值范围10-500
    minDist = 100
    circles = cv2.HoughCircles(edges,
                               cv2.HOUGH_GRADIENT,
                               dp,
                               minDist,
                               param1=45,
                               param2=10,
                               minRadius=math.ceil(60 / (shrink * shrink2)),
                               maxRadius=math.ceil(300 / (shrink * shrink2)))
    circles = np.uint16(np.around(circles))
    if circles is not None:
        circles = np.uint16(np.around(circles))
    else:
        return 0, [0, 0, 0, 0]

    t4 = time.time()
    #print('t3:',t4-t3)
    # c = []
    # for circle in circles[0]:
    #     # circle = circles[0]
    #     cir = np.zeros((edges.shape[0],edges.shape[1]))
    #     # 绘制外圆
    #     cv2.circle(cir, (circle[0], circle[1]), circle[2], (255, 255, 255), 1)
    #     # 绘制圆心
    #     # cv2.circle(cir, (circle[0], circle[1]), 2, (255, 255, 255), 2)
    #     cir = cv2.bitwise_and(cir,cir,mask=edges)
    #     c.append(len(cir[cir == 255])/(2*math.pi*circle[2]))
    # index = c.index(max(c))

    index = 0
    #只取中间的圆
    for circle in circles[0]:
        if abs(circle[0] - edges.shape[1] // 2) > 20:
            if index < circles.shape[1] - 1:
                #print(circles.shape[1])
                index = index + 1
            else:
                index = 0
        else:
            penalty.append([
                circle[0] * shrink * shrink2, circle[1] * shrink * shrink2,
                circle[2] * shrink * shrink2, shrink * shrink2
            ])
    penalty.append([
        circles[0][index][0] * shrink * shrink2,
        circles[0][index][1] * shrink * shrink2,
        circles[0][index][2] * shrink * shrink2, shrink * shrink2
    ])

    # cv2.imshow('edge', edges)

    return 1, penalty
import cv2

o = cv2.imread('yuko.jpg', cv2.IMREAD_GRAYSCALE)

Sobelx = cv2.Sobel(o, cv2.CV_64F, 1, 0)
Sobely = cv2.Sobel(o, cv2.CV_64F, 0, 1)
Sobelx = cv2.convertScaleAbs(Sobelx)
Sobely = cv2.convertScaleAbs(Sobely)
Sobelxy = cv2.addWeighted(Sobelx, 0.5, Sobely, 0.5, 0)
Sobelxyll = cv2.Sobel(o, cv2.CV_64F, 1, 1)
Sobelxyll = cv2.convertScaleAbs(Sobelxyll)

cv2.imshow('o', o)
cv2.imshow('xy', Sobelxy)
cv2.imshow('xyll', Sobelxyll)

cv2.waitKey()
cv2.destroyAllWindows()
    def process_image(self, input_image, output_image):

        width = input_image.shape[1]
        height = input_image.shape[0]

        current_page = self.ui.mainTabs.currentIndex()
        print("current_page_index: ", current_page)

        if current_page == BILATERAL_FILTER_PAGE:
            output_image = cv2.bilateralFilter(input_image,
                                               self.ui.bilateral_dia_spin.value(),
                                               self.ui.bilateral_sigma_color_spin.value(),
                                               self.ui.bilateral_sigma_space_spin.value(),
                                               self.ui.bilateral_border_type_combo.currentIndex())
            return output_image

        elif current_page == BLUR_FILTER_PAGE:
            print("kernel: ", self.ui.blur_kernel_spin.value())
            # print("anchor: ", self.ui.blur_anchor_x_spin.value(), self.ui_blur anchor_y_spin.value())
            output_image = cv2.blur(input_image,
                                    (self.ui.blur_kernel_spin.value(),
                                    self.ui.blur_kernel_spin.value()),
                                    output_image,
                                    (self.ui.blur_anchor_x_spin.value(),
                                    self.ui.blur_anchor_y_spin.value()),
                                    self.ui.blur_border_type_combo.currentIndex())
            return output_image

        elif current_page == BOX_FILTER_PAGE:
            print("normalize: ", self.ui.box_normalize_check.isChecked())
            output_image = cv2.boxFilter(src=input_image,
                                         ddepth=self.ui.box_depth_spin.value(),
                                         ksize=(self.ui.box_kernel_spin.value(),
                                         self.ui.box_kernel_spin.value()),
                                         dst=output_image,
                                         anchor=(self.ui.box_anchor_x_spin.value(),
                                         self.ui.box_anchor_y_spin.value()),
                                         normalize=self.ui.box_normalize_check.isChecked(),
                                         borderType=self.ui.box_border_type_combo.currentIndex())
            return output_image

        elif current_page == GAUSSIAN_FILTER_PAGE:
            output_image = cv2.GaussianBlur(input_image,
                                            ksize=(self.ui.gaussian_kernel_spin.value(),
                                                   self.ui.gaussian_kernel_spin.value()),
                                            sigmaX=self.ui.gaussian_sig_x_spin.value(),
                                            dst=output_image,
                                            sigmaY=self.ui.gaussian_sig_y_spin.value(),
                                            borderType=self.ui.gaussian_border_type_combo.currentIndex())

            return output_image

        elif current_page == MEDIAN_FILTER_PAGE:
            output_image = cv2.medianBlur(input_image,
                                          self.ui.median_kernel_spin.value())
            return output_image

        elif current_page == FILTER2D_FILTER_PAGE:
            f2dkernel = (0, 1.5, 0,
                         1.5, -6, 1.5,
                         0, 1.5, 0)
            output_image = cv2.filter2D(input_image,
                                        output_image,
                                        -1,
                                        f2dkernel,
                                        (-1, -1))
            return output_image

        elif current_page == DERIVATIVES_FILTER_PAGE:
            if self.ui.derivatives_sobel_radio.isChecked():
                cv2.Sobel(src=input_image,
                          ddepth=self.ui.derivatives_ddepth_spin.value(),
                          dx=self.ui.derivatives_dx_spin.value(),
                          dy=self.ui.derivatives_dy_spin.value(),
                          dst=output_image,
                          ksize=self.ui.derivatives_kernel_spin.value(),
                          scale=self.ui.derivatives_scale_spin.value(),
                          delta=self.ui.derivatives_delta_spin.value(),
                          borderType=self.ui.derivatives_border_type_combo.currentIndex())

            elif self.ui.derivatives_scharr_radio.isChecked():
                cv2.Scharr(src=input_image,
                           ddepth=self.ui.derivatives_ddepth_spin.value(),
                           dx=self.ui.derivatives_dx_spin.value(),
                           dy=self.ui.derivatives_dy_spin.value(),
                           dst=output_image,
                           ksize=self.derivatives_kernel_spin.value(),
                           scale=self.ui.derivatives_scale_spin.value(),
                           delta=self.ui.derivatives_delta_spin.value(),
                           borderType=self.ui.derivatives_border_type_combo.currentIndex())

            elif self.ui.derivatives_laplacian_radio.isChecked():
                output_image = cv2.Laplacian(src=input_image,
                                             ddepth=self.ui.derivatives_ddepth_spin.value(),
                                             dst=output_image,
                                             ksize=self.ui.derivatives_kernel_spin.value(),
                                             scale=self.ui.derivatives_scale_spin.value(),
                                             delta=self.ui.derivatives_delta_spin.value(),
                                             borderType=self.ui.derivatives_border_type_combo.currentIndex())

            return output_image

        elif current_page == MORPH_FILTER_PAGE:
            if self.ui.morph_erode_radio.isChecked():
                cv2.erode(input_image,
                          output_image,
                          cv2.getStructuringElement(self.ui.morph_shape_combo.currentIndex(),
                                                    (5, 5)),
                          (-1, -1),
                          self.ui.morph_iteration_spin.value())
            elif self.ui.morph_dilate_radio.isChecked():
                cv2.dilate(input_image,
                           output_image,
                           cv2.getStructingElement(self.ui.morph_shape_combo.currentIndex(),
                                                   (5,5)),
                           (-1,-1),
                           self.ui.morph_iteration_spin.value())

            elif self.ui.morph_morph_radio.isChecked():
                m_anchor = (self.ui.morph_anchor_x_spin.value(),
                            self.ui.morph_anchor_y_spin.value())
                m_kernel = self.ui.morph_kernel_spin.value(), self.ui.morph_kernel_spin.value()

                output_image = cv2.morphologyEx(input_image,
                                                self.ui.morph_type_combo.currentIndex(),
                                                cv2.getStructuringElement(self.ui.morph_shape_combo.currentIndex(),
                                                                         m_kernel),
                                                anchor=m_anchor,
                                                iterations=self.ui.morph_iteration_spin.value(),
                                                borderType=self.ui.morph_border_type_combo.currentIndex()
                                                # borderValue=
                                                )


                                 # return outputImage
            return output_image
Esempio n. 6
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def filteringHoughLines(img, ori_img, houghLinesArrary):
    filteredHoughLines = []

    # First gradients along x & y direction and then calculate magnitude
    dx = cv2.Sobel(img, cv2.CV_32F, 1, 0)  # Gradient along x direction
    dy = cv2.Sobel(img, cv2.CV_32F, 0, 1)  # Gradient along y direction
    magnitude = np.absolute(cv2.magnitude(dx, dy))  # Magnitude of the image

    meanValue = np.mean(magnitude)

    for line in houghLinesArrary:
        points = bresenham_line(
            (line[0][0], line[0][1]),
            (line[1][0],
             line[1][1]))  # Call Bresenham algorithm to get the points
        tempPoint1 = [(line[0][0] - line[1][0]), (line[0][1] - line[1][1])
                      ]  # Store the point for dot product
        threshold = 0

        # Removing the Vertical and Horizontal lines
        if line[1][0] == 0 or line[0][0] == 0 or line[1][0] - line[0][0] == 0:
            lineSlope = 1
            lineAngle = 0
            continue
        else:
            lineSlope = (line[1][1] - line[0][1]) / (line[1][0] - line[0][0])
            lineAngle = np.absolute(np.arctan(lineSlope) * 180.0 / np.pi)

        # Check for Vertical Lines
        if lineAngle > 85 and lineAngle < 95:
            continue
        # Check for Horizontal Lines
        if lineAngle >= 0 and lineAngle < 10:
            continue

        # Iterate through all the points
        for a, b in points:
            if a < 1200 and b < 1600:
                xVal = np.absolute(dx[a, b])  # Get gradient for point at X
                yVal = np.absolute(dy[a, b])  # Get gradient for point at Y

                # Save point 2 only if the magnitude is greater than the threshold
                if magnitude[a, b] > 30:
                    tempPoint2 = [xVal, yVal]
                else:
                    continue
            else:
                continue

            #Get the dot product of the two points
            resDotProduct = np.dot(tempPoint1, tempPoint2)
            point1Mag = np.dot(tempPoint1, tempPoint1)**0.5
            point2Mag = np.dot(tempPoint2, tempPoint2)**0.5

            if point1Mag == 0 or point2Mag == 0:
                continue

            # Get the cosine of the angle
            cosAngle = math.cos(resDotProduct / point2Mag / point1Mag)

            angleMod = math.degrees(cosAngle) % 360

            if angleMod - 180 >= 0:
                angleMod = 360 - angleMod
            else:
                angleMod = angleMod

            # Increase thresholdValue if the angle is between 85 & 95
            if not (angleMod > 85 and angleMod < 95):
                threshold += 1

        # Plot Line on the original image and add it to the array
        if threshold > 45:
            cv2.line(ori_img, (line[0][0], line[0][1]),
                     (line[1][0], line[1][1]), (255, 0, 0), 2)
            filteredHoughLines.append(
                ((line[0][0], line[0][1]), (line[1][0], line[1][1])))

    cv2.imwrite("FilteredHoughLines.jpg", ori_img)
    return filteredHoughLines
    y1, y2, x1, x2 = corner[0][0] - w, corner[0][0] + w, corner[0][
        1] - w, corner[0][1] + w
    if x1 < 0:
        x1 = 0
    elif x2 > X_len:
        x2 = X_len
    elif y1 > Y_len:
        y1 = Y_len
    elif y2 < 0:
        y2 = 0
    # print(corner[0][0],corner[0][1])
    # print(x1,x2,y1,y2)
    image_patch = img[x1:x2, y1:y2]
    #cv2.imshow('patches', image_patch)
    #cv2.waitKey()
    gx = cv2.Sobel(image_patch, cv2.CV_32F, 1, 0, ksize=1)
    gy = cv2.Sobel(image_patch, cv2.CV_32F, 0, 1, ksize=1)
    # print(gx,gy)
    mag, angle = cv2.cartToPolar(gx, gy, angleInDegrees=True)
    # mag, angle = np.array(mag, dtype=int), np.array(angle, dtype=int)
    blue_bin = [0] * 9
    blue_bin = np.array(blue_bin, dtype=float)
    green_bin = [0] * 9
    green_bin = np.array(green_bin, dtype=float)
    red_bin = [0] * 9
    red_bin = np.array(red_bin, dtype=float)
    # print(mag)
    # print("next")
    # print(angle)

    # -------------- BGR CHANNEL------------
Esempio n. 8
0
# @Author  : cap
# @FileName: myOpencvEdge.py
# @Software: PyCharm Community Edition
# @introduction: 边缘检测
import cv2 as cv

# image = cv.imread('./data/chair.jpg', cv.IMREAD_GRAYSCALE)
image = cv.imread('./data/J01_2018.06.17 15_30_49.jpg', cv.IMREAD_GRAYSCALE)
image = cv.resize(image, None, fx=0.2, fy=0.2, interpolation=cv.INTER_LINEAR)
print(image.shape)
print(image.dtype)
cv.imshow('chair', image)

# 1 索贝尔边缘检测
# 检测水平梯度变化, ksize:卷积框
hor = cv.Sobel(image, cv.CV_64F, 1, 0, ksize=5)
cv.imshow('Hor', hor)
# 检测垂直梯度变化
ver = cv.Sobel(image, cv.CV_64F, 0, 1, ksize=5)
cv.imshow('Ver', ver)
# 检测两个方向
hor_ver = cv.Sobel(image, cv.CV_64F, 1, 1, ksize=5)
cv.imshow('Hor_Ver', hor_ver)

# 2 拉普拉斯边缘检测
lap = cv.Laplacian(image, cv.CV_64F)
cv.imshow('Lap', lap)

# 3 Canny 50:
canny = cv.Canny(image, 50, 240)
cv.imshow('Canny', canny)
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
ap.add_argument("-l", "--lower-angle", type=float, default=175.0,
                help="Lower orientation angle")
ap.add_argument("-u", "--upper-angle", type=float, default=180.0,
                help="Upper orientation angle")
args = vars(ap.parse_args())

# load the image, convert it to grayscale, and display the original
# image
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Original", image)

# compute gradients along the X and Y axis, respectively
gX = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
gY = cv2.Sobel(gray, cv2.CV_64F, 0, 1)

# compute the gradient magnitude and orientation respectively
mag = np.sqrt((gX ** 2) + (gY ** 2))
orientation = np.arctan2(gY, gX) * (180 / np.pi) % 180

# find all pixels that are within the upper and low angle boundaries
idxs = np.where(orientation >= args["lower_angle"], orientation, -1)
idxs = np.where(orientation <= args["upper_angle"], idxs, -1)
mask = np.zeros(gray.shape, dtype="uint8")
mask[idxs > -1] = 255

# show the images
cv2.imshow("Mask", mask)
cv2.waitKey(0)
import cv2
import numpy as np
import matplotlib.pyplot as plt

hourseImg = cv2.imread("../../../gallery/logo.png", cv2.IMREAD_GRAYSCALE)

# xGrad = cv2.Sobel(hourseImg, cv2.CV_64F, 1, 0, ksize=3)
# yGrad = cv2.Sobel(hourseImg, cv2.CV_64F, 0, 1, ksize=3)

Grad = cv2.Sobel(hourseImg, cv2.CV_64F, 1, 1, ksize=3)
lapGrads = cv2.Laplacian(hourseImg, cv2.CV_64F, ksize=3)
edges = cv2.bitwise_or(Grad, lapGrads)
canny = cv2.Canny(hourseImg, 100, 200)
gradMorph = cv2.morphologyEx(hourseImg, cv2.MORPH_GRADIENT,
                             np.ones((5, 5), dtype=np.uint8))

cv2.imshow("sobel & laplacian", edges)
cv2.waitKey(0)

cv2.imshow("canny", canny)
cv2.waitKey(0)

cv2.imshow("morph", gradMorph)
cv2.waitKey(0)

cv2.destroyAllWindows()
Esempio n. 11
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# Problem 2:
# For grey histograms:
for imagePath in glob.glob("ST2MainHall4" + "/*.jpg"):
    # extract the image filename (assumed to be unique)
    filename = imagePath[imagePath.rfind("/") + 1:]
    # Read the image as grey image
    image = cv2.imread(imagePath, 0)
    # Use canny edge detector to select edge points
    cannyImageMask = cv2.Canny(image, 100, 250)
    # To create the 8-bit mask
    cannyImageMask = np.uint8(cannyImageMask)
    # Apply bitwise and on the image to mask it
    maskedImage = cv2.bitwise_and(image, image, mask=cannyImageMask)
    # Compute gradients in x and y direction
    sobelXDir = cv2.Sobel(maskedImage, cv2.CV_64F, 1, 0, ksize=5)
    sobelYDir = cv2.Sobel(maskedImage, cv2.CV_64F, 0, 1, ksize=5)
    # Compute magnitude and theta angle using the gradients
    magnitude, theta = cv2.cartToPolar(sobelXDir,
                                       sobelYDir,
                                       angleInDegrees=True)
    # To turn theta into hist index
    theta = np.round(np.divide(theta, 10))
    theta = np.uint8(theta)
    # flatten the theta and magnitude arrays
    flattenedTheta = hist_bins(theta)
    flattenedMagnitude = hist_bins(magnitude)
    # Build 36-bin histograms
    hist, bins = np.histogram(flattenedTheta,
                              range(37),
                              weights=flattenedMagnitude)
Esempio n. 12
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import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

img = cv.imread('sudoku.png', cv.IMREAD_GRAYSCALE)
lap = cv.Laplacian(img, cv.CV_64F, ksize=3)
lap = np.uint8(np.absolute(lap))
sobelX = cv.Sobel(img, cv.CV_64F, 1, 0, ksize=1)
sobelY = cv.Sobel(img, cv.CV_64F, 0, 1, ksize=1)

sobelX = np.uint8(np.absolute(sobelX))
sobelY = np.uint8(np.absolute(sobelY))

sobelCombined = cv.bitwise_or(sobelY, sobelX)

titles = ['image', 'lap', 'sobelX', 'sobelY', 'sobel']
images = [img, lap, sobelX, sobelY, sobelCombined]
for i in range(5):
    plt.subplot(2, 3, i + 1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])

plt.show()
Esempio n. 13
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def preprocess(curr_frame, size=(640, 480), thres_condi=0.3):
    """
    function:
        preprocess(curr_frame[, size=(640, 480)[, thres_condi=0.2]]): 設定 curr_frame 為 size 大小去做預處理

    parameter:
        curr_frame: 當前要處理的幀(一張圖片)
        size: 將圖片設定成 width * height 大小 (width, height), tuple
        thres_condi: 設定二值化的條件, 預設為 0.2, 範圍 [0, 1), float

    method:
        1. 先使用找出影片的 ROI 區域
        (邊界的灰階值如果在 10 以下則濾掉)
        2. 先重新設定 curr_frame 大小
        (判斷該影片為直的或橫的, 根據方向不同做出適合的縮放)
        3. 轉灰階
        4. 高斯濾波
        (kernel 大小設定 5*5, SD = 0)
        5. Sobel
        (dx, dy = 1, kernel 大小設定 5*5)
        6. 二值化
        (f(x) = (max(sobel) - min(sobel)) * thres_condi)
        (255 if sobel >= f(x) else 0)

    return:
        frame: 傳回重塑過後的 3 通道圖片
        gray: 傳回重塑過後的灰階圖片
        frame_pre: 傳回二值化的圖片
    """

    # 1.
    gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
    gray[gray < 10] = 0

    contour, hierarchy = cv2.findContours(gray.copy(), cv2.RETR_EXTERNAL,
                                          cv2.CHAIN_APPROX_SIMPLE)

    all_area = list()
    for cnt in contour:
        area = cv2.contourArea(cnt)
        all_area.append(area)

    index = all_area.index(max(all_area))
    del all_area

    x, y, w, h = cv2.boundingRect(contour[index])

    frame_roi = curr_frame[y:y + h, x:x + w]

    # 2.
    re_y, re_x, _ = frame_roi.shape
    size = (size[1], size[0]) if re_y > re_x else (size[0], size[1])

    frame = cv2.resize(frame_roi, size, cv2.INTER_AREA)

    # 3.
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 4.
    blur = cv2.GaussianBlur(gray, (5, 5), 0)

    # 5.
    frame_sobel = cv2.Sobel(blur, ddepth=-1, dx=1, dy=1, ksize=5)
    frame_pre = frame_sobel.copy()

    # 6.
    thres = ((np.max(frame_sobel) - np.min(frame_sobel)) * thres_condi).astype(
        np.uint8)

    frame_pre[frame_sobel >= thres] = 255
    frame_pre[frame_sobel < thres] = 0

    return frame, gray, frame_pre
Esempio n. 14
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#median filter
median_image = cv2.medianBlur(gray_image, 5)
cv2.imshow("Over the Clouds - median", median_image)
cv2.imwrite('median.jpg', median_image)

#min filter
kernel = np.ones((5, 5), np.uint8)
min_image = cv2.erode(gray_image, kernel, iterations=1)
cv2.imshow("Over the Clouds - min", min_image)
cv2.imwrite('min.jpg', min_image)

#max filter
max_image = cv2.dilate(gray_image, kernel, iterations=1)
cv2.imshow("Over the Clouds - max", max_image)
cv2.imwrite('max.jpg', max_image)

#laplacian filter
laplace_image = cv2.Laplacian(median_image, cv2.CV_64F)
cv2.imwrite('laplace.jpg', laplace_image)

#sobel horizontal filter
sobelh_image = cv2.Sobel(median_image, cv2.CV_64F, 1, 0, ksize=5)
cv2.imwrite('sobel_h.jpg', sobelh_image)

#sobel vertical filter
sobelv_image = cv2.Sobel(median_image, cv2.CV_64F, 0, 1, ksize=5)
cv2.imwrite('sobel_v.jpg', sobelv_image)

cv2.waitKey(0)
cv2.destroyAllWindows()
Esempio n. 15
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 def exact_feature(self):
     gradient_x = cv2.Sobel(self.img, cv2.CV_32F, 1, 0, ksize=3)
     gradient_y = cv2.Sobel(self.img, cv2.CV_32F, 0, 1, ksize=3)
     gradient_magnitude = np.sqrt(gradient_x**2 + gradient_y**2)
     gradient_angle = cv2.phase(gradient_x, gradient_y, angleInDegrees=True)
     return gradient_magnitude, gradient_angle
Esempio n. 16
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plt.subplot(4, 2, 3)
plt.imshow(dst_prewitt, cmap='gray')
plt.title('$f\'_x$: image filtered with Prewitt')

#######################################
# cv2.Sobel() also exist
kernel = 1 / 8 * np.array([[-1, 0, +1], [-2, 0, +2], [-1, 0, +1]])
dst_sobel = cv2.filter2D(img, -1, kernel)

plt.subplot(4, 2, 4)
plt.imshow(dst_sobel, cmap='gray')
plt.title('$f\'_x$: image filtered with Sobel')

#######################################
dst_cv2_sobel = cv2.Sobel(img, -1, 1, 0)  #cv2.Sobel(img,ddepth,x_size,y_size)

plt.subplot(4, 2, 5)
plt.imshow(dst_cv2_sobel, cmap='gray')
plt.colorbar()
plt.title('cv2.Sobel X')

#######################################
plt.subplot(4, 2, 6)
plt.imshow(np.abs(dst_sobel - dst_sym))
plt.colorbar()
plt.title('|sobel-symmetric|')

#######################################
plt.subplot(4, 2, 7)
plt.imshow(np.abs(dst_sobel - dst_prewitt))
Esempio n. 17
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#      if event == cv2.EVENT_LBUTTONDOWN:
#          print('x = %d, y = %d'%(x, y))
#          count = count + 1

#          if(len(coordinate)==4):
#              coordinate.clear()
#          else:
#              coordinate.append(x)
#              coordinate.append(y)
#          if count %2 == 0 and count > 0 :

#              cropimg(coordinate)

#cv2.imshow('res2',res2)

sobely = cv2.Sobel(res2, cv2.CV_8U, 0, 1, ksize=5)
counter = 0
#cv2.imshow('sobely',sobely)
print(sobely.shape)
height, width = sobely.shape
for x in range(0, width):
    for y in range(0, height):
        if (sobely[y, x] < 255):
            sobely[y, x] = 0
        else:
            if (counter == 0):
                counter = counter + 1

                print(y, x)

cv2.imshow("masked", sobely)
Esempio n. 18
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def combine_thresh_debug(img, sobel_kernel=3, mag_thresh=(0, 255)):
    bgr = img
    R = bgr[:, :, 2]
    G = bgr[:, :, 1]
    B = bgr[:, :, 0]
    cv2.imshow('R', R)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imshow('G', G)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imshow('B', B)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    H = hls[:, :, 0]
    L = hls[:, :, 1]
    S = hls[:, :, 2]
    cv2.imshow('H', H)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imshow('L', L)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imshow('S', S)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    # Convert to grayscale
    # gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    gray = img
    # Take both Sobel x and y gradients
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    abs_sobel = np.absolute(sobelx)
    scaled_sobel = np.uint8(255 * abs_sobel / np.max(abs_sobel))
    print(scaled_sobel.shape)
    cv2.imshow('img', scaled_sobel)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    bgr = scaled_sobel
    R = bgr[:, :, 2]
    G = bgr[:, :, 1]
    B = bgr[:, :, 0]
    cv2.imshow('H', R)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imshow('L', G)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imshow('S', B)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    hls = cv2.cvtColor(scaled_sobel, cv2.COLOR_BGR2HLS)
    H = hls[:, :, 0]
    L = hls[:, :, 1]
    S = hls[:, :, 2]
    cv2.imshow('H', H)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imshow('L', L)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imshow('S', S)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    thresh = (105, 255)
    binary_output = np.zeros_like(L)
    binary_output[(L > thresh[0]) & (L <= thresh[1])] = 1

    # Return the binary image
    return binary_output
        x, y, z = deproject_pixel_to_ros_point(cX, cY, frame_set.depth[cY, cX])
        a, b, c = self.get_normal_vector(frame_set, (startX, startY, endX, endY))
        normal_arctan = math.atan2(b, a)
        r = math.degrees(normal_arctan)
        return (x, y, r)

    def get_normal_vector(self, frame_set, (startX, startY, endX, endY)):
        depth_frame_16 = frame_set.depth
        df_dp = np.expand_dims(depth_frame_16, axis=-1).astype(np.uint8)
        df_dp = np.tile(df_dp, (1, 1, 3))
        depth_frame = depth_frame_16.astype(np.float32)
        cX = startX + (endX - startX) / 2
        cY = startY + (endY - startY) / 2
        sample = depth_frame[cY-20: cY+20, cX-20: cX+20]
        cv.rectangle(df_dp, (cX-20, cY-20), (cX+20, cY+20), (255,0,0), 2)
        dzdx = cv.Sobel(sample,cv.CV_32F,1,0,ksize=5)
        dzdy = cv.Sobel(sample,cv.CV_32F,0,1,ksize=5)
        dzdx = np.median(dzdx)
        dzdy = np.median(dzdy)
        #Convert to ros coordinates:
        # z->a, -dzdx->b, -dzdy->c
        return (1.0, -dzdx, -dzdy)

    def latest_meas(self):
        return self._latest_meas

    def lost_target(self):
        return self._target_lost

class SoldierTow(Alignment):
    def detect(self, frame_set):
Esempio n. 20
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sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))

#读取输入图像,预处理
image = cv2.imread(args["image"])
cv_show('image', image)
image = myutils.resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray', gray)

#礼帽操作,突出更明亮的区域
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
cv_show('tophat', tophat)
#
gradX = cv2.Sobel(
    tophat,
    ddepth=cv2.CV_32F,
    dx=1,
    dy=0,  #ksize=-1相当于用3*3的
    ksize=-1)

gradX = np.absolute(gradX)
(minVal, maxVal) = (np.min(gradX), np.max(gradX))
gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
gradX = gradX.astype("uint8")

print(np.array(gradX).shape)
cv_show('gradX', gradX)

#通过闭操作(先膨胀,再腐蚀)将数字连在一起
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
cv_show('gradX', gradX)
#THRESH_OTSU会自动寻找合适的阈值,适合双峰,需把阈值参数设置为0
Esempio n. 21
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    def findCenterline(gray):
        #showg(gray)
        sobel_kernel = 7
        thresh = (0.6, 1.3)

        # normalize
        t.s('normalize')
        gray = normalize(gray)
        t.e('normalize')

        # Calculate the x and y gradients
        t.s('sobel\t')
        sobelx = cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=sobel_kernel)
        sobely = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=sobel_kernel)
        t.e('sobel\t')

        #graddir = np.arctan2(sobely, sobelx)

        # find the norm (magnitute) of gradient
        norm = np.sqrt(np.square(sobelx) + np.square(sobely))
        norm = normalize(norm)
        #norm > 1 to get good edges

        # find left edges of while lanes
        t.s('find left edges')
        binary_output = np.zeros_like(gray, dtype=np.uint8)
        # XXX gray>1.5 is a sketchy solution that cut data size in half
        binary_output[(gray > 1.5) & (sobelx > 0) & (norm > 1)] = 1
        #showg(binary_output)

        #label connected components
        connectivity = 8
        output = cv2.connectedComponentsWithStats(binary_output, connectivity,
                                                  cv2.CV_32S)
        # The first cell is the number of labels
        num_labels = output[0]
        # The second cell is the label matrix
        labels = output[1]
        # The third cell is the stat matrix
        stats = output[2]
        # The fourth cell is the centroid matrix
        centroids = output[3]
        '''
        # for DEBUG
        
        # Map component labels to hue val
        label_hue = np.uint8(179*labels/np.max(labels))
        blank_ch = 255*np.ones_like(label_hue)
        labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])

        # cvt to BGR for display
        labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)

        # set bg label to black
        labeled_img[label_hue==0] = 0

        showg(labeled_img)
        '''

        # find the two longest left edges
        line_labels = np.argsort(stats[:, cv2.CC_STAT_AREA][1:])[-2:] + 1

        # list of centroids with corresponding left/right edge (of a white line)
        long_edge_centroids = []
        long_edge_lr = ""
        long_edge_label = []

        #XXX error: out of bond
        if (stats[line_labels[0], cv2.CC_STAT_AREA] > 300):
            long_edge_centroids.append(centroids[line_labels[0], 0])
            long_edge_lr += 'L'
            long_edge_label.append(labels == line_labels[0])
        if (stats[line_labels[1], cv2.CC_STAT_AREA] > 300):
            long_edge_centroids.append(centroids[line_labels[1], 0])
            long_edge_lr += 'L'
            long_edge_label.append(labels == line_labels[1])

        t.e('find left edges')

        # find right edge of lanes
        # XXX gray>1.5 is a sketchy solution that cut data size in half
        t.s('find right edg')
        binary_output = np.zeros_like(gray, dtype=np.uint8)
        binary_output[(gray > 1.5) & (sobelx < 0) & (norm > 1)] = 1

        #label connected components
        connectivity = 8
        output = cv2.connectedComponentsWithStats(binary_output, connectivity,
                                                  cv2.CV_32S)
        # The first cell is the number of labels
        num_labels = output[0]
        # The second cell is the label matrix
        labels = output[1]
        # The third cell is the stat matrix
        stats = output[2]
        # The fourth cell is the centroid matrix
        centroids = output[3]

        line_labels = np.argsort(stats[:, cv2.CC_STAT_AREA][1:])[-2:] + 1

        if (stats[line_labels[0], cv2.CC_STAT_AREA] > 300):
            long_edge_centroids.append(centroids[line_labels[0], 0])
            long_edge_lr += 'R'
            long_edge_label.append(labels == line_labels[0])

        if (stats[line_labels[1], cv2.CC_STAT_AREA] > 300):

            long_edge_centroids.append(centroids[line_labels[1], 0])
            long_edge_lr += 'R'
            long_edge_label.append(labels == line_labels[1])

        # rank the edges based on centroid
        order = np.argsort(long_edge_centroids)
        long_edge_centroids = np.array(long_edge_centroids)[order]
        temp_lr = ""
        for i in order:
            temp_lr += long_edge_lr[i]
        long_edge_lr = temp_lr
        long_edge_label = np.array(long_edge_label)[order]

        t.e('find right edg')
        # now we analyze the long edges we have
        # case notation: e.g.(LR) -> left edge, right edge, from left to right

        # this logical is based on the assumption that the edges we find are lane edges
        # now we distinguish between several situations
        t.s('find centerline - lr analysis')
        flag_fail_to_find = False
        flag_good_road = False
        flag_one_lane = False
        centerPoly = None

        # case 1: if we find one and only one pattern (?RL?), we got a match
        if (long_edge_lr.count('RL') == 1):
            index = long_edge_lr.find('RL')
            with warnings.catch_warnings(record=True) as w:
                left_poly = fitPoly(long_edge_label[index])
                index += 1
                right_poly = fitPoly(long_edge_label[index])
                if len(w) > 0:
                    raise Exception('fail to fit poly')

                else:
                    flag_good_road = True
                    center_poly = findCenterFromSide(left_poly, right_poly)

        # case 2: we only see one edge of any sort
        if (len(long_edge_lr) == 1):
            with warnings.catch_warnings(record=True) as w:
                side_poly = fitPoly(long_edge_label[0])
                if len(w) > 0:
                    raise Exception('fail to fit poly')
                else:
                    flag_one_lane = True

        # case 3: if we get  (LR), then we are stepping on a lane, but don't know which that lane is (LR)
        # in this case drive on this lane until we see the other lane
        elif (long_edge_lr == 'LR'):
            index = 0
            with warnings.catch_warnings(record=True) as w:
                left_poly = fitPoly(long_edge_label[index])
                index += 1
                right_poly = fitPoly(long_edge_label[index])
                if len(w) > 0:
                    raise Exception('fail to fit poly')

                else:
                    flag_one_lane = True
                    side_poly = findCenterFromSide(left_poly, right_poly)

        # otherwise we are completely lost
        else:
            flag_fail_to_find = True
            pass

        # based on whether the line inclines to the left or right, guess which side it is
        if (flag_one_lane == True):
            x0 = side_poly[0] * 1**2 + side_poly[1] * 1 + side_poly[
                2] - x_size / 2
            x1 = side_poly[0] * crop_y_size**2 + side_poly[
                1] * crop_y_size + side_poly[2] - x_size / 2
            if (x1 - x0 > 0):
                side = 'right'
            else:
                side = 'left'
        t.e('find centerline - lr analysis')

        binary_output = None
        if (flag_good_road == True):
            # DEBUG - for producing anice testimg
            '''
            t.s('generate testimg')
            # Generate x and y values for plotting
            ploty = np.linspace(0, gray.shape[0]-1, gray.shape[0] )
            left_fitx = left_poly[0]*ploty**2 + left_poly[1]*ploty + left_poly[2]
            right_fitx = right_poly[0]*ploty**2 + right_poly[1]*ploty + right_poly[2] 
            # Recast the x and y points into usable format for cv2.fillPoly()
            pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
            pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
            pts = np.hstack((pts_left, pts_right))

            # Draw the lane onto the blank image
            binary_output =  np.zeros_like(gray,dtype=np.uint8)
            cv2.fillPoly(binary_output, np.int_([pts]), 1)

            # Draw centerline onto the image
            centerlinex = center_poly[0]*ploty**2 + center_poly[1]*ploty + center_poly[2]
            pts_center = np.array(np.transpose(np.vstack([centerlinex, ploty])))
            cv2.polylines(binary_output,np.int_([pts_center]), False, 5,10)

            #driveSys.testimg = np.dstack(40*[binary_output,binary_output,binary_output])
            # END-DEBUG
            t.e('generate testimg')
            '''
            pass

            # get centerline in top-down view

            t.s('change centerline perspective')

            # prepare sample points
            ploty = np.linspace(0, gray.shape[0] - 1, gray.shape[0])
            centerlinex = center_poly[0] * ploty**2 + center_poly[
                1] * ploty + center_poly[2]

            # convert back to uncropped space
            ploty += y_size / 2
            pts_center = np.array(np.transpose(np.vstack([centerlinex,
                                                          ploty])))
            pts_center = cam.undistortPts(np.reshape(pts_center, (1, -1, 2)))

            # unwarp and change of units
            for i in range(len(pts_center[0])):
                pts_center[0, i, 0], pts_center[0, i, 1] = transform(
                    pts_center[0, i, 0], pts_center[0, i, 1])

            # now pts_center should contain points in vehicle coordinate with x axis being rear axle,unit in cm
            #fit(y,x)
            fit = np.polyfit(pts_center[0, :, 1], pts_center[0, :, 0], 2)
            t.e('change centerline perspective')

            return fit

        if (flag_one_lane == True):

            # DEBUG - for producing anice testimg
            '''
	    t.s('generate testimg')
            # Generate x and y values for plotting
            ploty = np.linspace(0, gray.shape[0]-1, gray.shape[0] )

            binary_output =  np.zeros_like(gray,dtype=np.uint8)

            # Draw centerline onto the image
            sidelinex = side_poly[0]*ploty**2 + side_poly[1]*ploty + side_poly[2]
            pts_side = np.array(np.transpose(np.vstack([sidelinex, ploty])))
            cv2.polylines(binary_output,np.int_([pts_side]), False, 1,1)

            #driveSys.testimg = np.dstack(250*[binary_output,binary_output,binary_output])
	    t.e('generate testimg')
            '''
            # END-DEBUG

            # get centerline in top-down view

            t.s('change centerline perspective')

            # prepare sample points
            ploty = np.linspace(0, gray.shape[0] - 1, gray.shape[0])
            sidelinex = side_poly[0] * ploty**2 + side_poly[
                1] * ploty + side_poly[2]

            # convert back to uncropped space
            ploty += y_size / 2
            pts_side = np.array(np.transpose(np.vstack([sidelinex, ploty])))
            pts_side = cam.undistortPts(np.reshape(pts_side, (1, -1, 2)))

            # unwarp and change of units
            for i in range(len(pts_side[0])):
                pts_side[0, i,
                         0], pts_side[0, i,
                                      1] = transform(pts_side[0, i, 0],
                                                     pts_side[0, i, 1])

                # now pts_side should contain points in vehicle coordinate with x axis being rear axle,unit in cm
                #XXX this is really stupid and inefficient
                if (side == 'left'):
                    pts_side[0, i,
                             0] = pts_side[0, i, 0] + 0.5 * driveSys.lanewidth
                else:
                    pts_side[0, i,
                             0] = pts_side[0, i, 0] - 0.5 * driveSys.lanewidth

            # now pts_side should contain points in vehicle coordinate with x axis being rear axle,unit in cm
            #fit(y,x)
            fit = np.polyfit(pts_side[0, :, 1], pts_side[0, :, 0], 2)

            t.e('change centerline perspective')

            return fit

        return None
Esempio n. 22
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 def filter(self, frame):
     frame_dx = cv2.Sobel(frame, cv2.CV_64F, 1, 0, ksize=self.ksize)
     frame_dy = cv2.Sobel(frame, cv2.CV_64F, 0, 1, ksize=self.ksize)
     frame_mag = np.abs(frame_dx) + np.abs(frame_dy)
     frame_mag /= frame_mag.max()
     return frame_mag
Esempio n. 23
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def gradient(blurred):
    # 对平滑后的图像使用Sobel算子计算水平方向和竖直方向的一阶导数(图像梯度)(Gx和Gy)
    # 根据得到的这两幅梯度图找到边界的梯度和方向
    # ksize=3是因为cv2.Canny方法默认的Ksize=3
    # cv2.CV_64F是使用64位存储,为了运算不越界
    sobelx = cv2.Sobel(blurred, cv2.CV_64F, 1, 0, ksize=3)
    sobely = cv2.Sobel(blurred, cv2.CV_64F, 0, 1, ksize=3)

    # 建立两个np.arr类型的数据
    sobel = np.zeros((len(sobelx), len(sobelx[0])))
    theat = np.zeros((len(sobelx), len(sobelx[0])))

    # 根据公式计算合成梯度值
    for i in range(len(sobelx)):
        for j in range(len(sobelx[0])):
            sobel[i][j] = math.sqrt(sobelx[i][j] * sobelx[i][j] +
                                    sobely[i][j] * sobely[i][j])
            # 将弧度转化为度数
            if sobelx[i][j] != 0:
                theat[i][j] = math.atan(
                    sobely[i][j] / sobelx[i][j]) * 180 / math.pi
            else:
                if sobely[i][j] < 0:
                    theat[i][j] = -90
                elif sobely[i][j] > 0:
                    theat[i][j] = 90
                elif sobely[i][j] == 0:
                    theat[i][j] = 45
            # 做边界的划分,划分为四个梯度值
            temp = theat[i][j]
            if -112.5 < temp <= -67.5:
                theat[i][j] = 90
            elif -67.5 < temp <= -22.5:
                theat[i][j] = 135
            elif -22.5 < temp <= 22.5:
                theat[i][j] = 0
            elif 22.5 < temp <= 67.5:
                theat[i][j] = 45
            elif 67.5 < temp <= 112.5:
                theat[i][j] = 90
    # 以下是 非极大值抑制 的算法
    # 模型结束后移到另一个文件并做精简
    # 还要考虑和nms算法的异同

    # 做非边界部分的判断
    # i从1到倒数第二,j同样
    for i in range(1, len(sobel) - 1):
        for j in range(1, len(sobel[0]) - 1):
            if theat[i][j] == 0:
                if sobel[i][j] < max(sobel[i + 1][j], sobel[i - 1][j]):
                    blurred[i][j] = 0
            elif theat[i][j] == 45:
                if sobel[i][j] < max(sobel[i + 1][j - 1], sobel[i - 1][j - 1]):
                    blurred[i][j] = 0
            elif theat[i][j] == 90:
                if sobel[i][j] < max(sobel[i][j + 1], sobel[i][j - 1]):
                    blurred[i][j] = 0
            elif theat[i][j] == 135:
                if sobel[i][j] < max(sobel[i + 1][j - 1], sobel[i - 1][j + 1]):
                    blurred[i][j] = 0

    # 上边界非端点处理,i=0,i不能减1
    for j in range(1, len(sobelx[0]) - 1):
        i = 0
        if theat[0][j] == 0:
            if sobel[i][j] < sobel[i + 1][j]:
                blurred[i][j] = 0
        elif theat[0][j] == 45:
            if sobel[i][j] < sobel[i + 1][j - 1]:
                blurred[i][j] = 0
        elif theat[0][j] == 90:
            if sobel[i][j] < max(sobel[i][j + 1], sobel[i][j - 1]):
                blurred[i][j] = 0
        elif theat[0][j] == 135:
            if sobel[i][j] < sobel[i + 1][j - 1]:
                blurred[i][j] = 0
    # 下边界非端点,i = 255,i不能加1
    for j in range(1, len(sobel[0]) - 1):
        i = len(theat) - 1
        if theat[i][j] == 0:
            if sobel[i][j] < sobel[i - 1][j]:
                blurred[i][j] = 0
        elif theat[i][j] == 45:
            if sobel[i][j] < sobel[i - 1][j - 1]:
                blurred[i][j] = 0
        elif theat[i][j] == 90:
            if sobel[i][j] < max(sobel[i][j + 1], sobel[i][j - 1]):
                blurred[i][j] = 0
        elif theat[i][j] == 135:
            if sobel[i][j] < sobel[i - 1][j + 1]:
                blurred[i][j] = 0
    # 左边界非端点,j=0,j不能减1
    for i in range(1, len(sobel) - 1):
        j = 0
        if theat[i][j] == 0:
            if sobel[i][j] < max(sobel[i + 1][j], sobel[i - 1][j]):
                blurred[i][j] = 0
        elif theat[i][j] == 90:
            if sobel[i][j] < sobel[i][j + 1]:
                blurred[i][j] = 0
        elif theat[i][j] == 135:
            if sobel[i][j] < sobel[i - 1][j + 1]:
                blurred[i][j] = 0
    # 右边界非端点,j=255,不能加1
    for i in range(1, len(sobel) - 1):
        j = len(sobel[0]) - 1
        if theat[i][j] == 0:
            if sobel[i][j] < max(sobel[i + 1][j], sobel[i - 1][j]):
                blurred[i][j] = 0
        elif theat[i][j] == 45:
            if sobel[i][j] < max(sobel[i + 1][j - 1], sobel[i - 1][j - 1]):
                blurred[i][j] = 0
        elif theat[i][j] == 90:
            if sobel[i][j] < sobel[i][j - 1]:
                blurred[i][j] = 0
        elif theat[i][j] == 135:
            if sobel[i][j] < sobel[i + 1][j - 1]:
                blurred[i][j] = 0
    # 左上角。i,j不能减1
    if theat[0][0] == 0:
        if sobel[0][0] < sobel[0 + 1][0]:
            blurred[0][0] = 0
    elif theat[0][0] == 90:
        if sobel[0][0] < sobel[0][0 + 1]:
            blurred[0][0] = 0
    # 左下角,i不能加,j不能减
    if theat[len(sobel) - 1][0] == 0:
        if sobel[len(sobel) - 1][0] < sobel[len(sobel) - 1 - 1][0]:
            blurred[len(sobel - 1)][0] = 0
    elif theat[len(sobel) - 1][0] == 90:
        if sobel[len(sobel) - 1][0] < sobel[len(sobel) - 1][0 + 1]:
            blurred[len(sobel) - 1][0] = 0
    elif theat[len(sobel) - 1][0] == 135:
        if sobel[len(sobel) - 1][0] < sobel[len(sobel) - 1 - 1][0 + 1]:
            blurred[len(sobel) - 1][0] = 0
    #右下角 右上脚暂时没写,可仿照上文实现

    return blurred
Esempio n. 24
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def data_gen_sobel(path, ids):
    """
    In this generator every input is interesected with the Prostate contour
    :param path:
    :param folders_to_read:
    :param img_names:
    :param roi_names:
    :param tot_ex_per_img
    :return:
    """
    print("Inside generator...")

    curr_idx = -1  # First index to use

    all_files_per_digit = []
    tot_files_per_digit = []
    acc_files_per_digit = []
    last_id = 0
    for cur_folder in range(10):
        all_files = listdir(join(path, F'{cur_folder}'))
        tot_files = len(all_files)
        last_id += tot_files
        all_files_per_digit.append(all_files)
        tot_files_per_digit.append(tot_files)
        acc_files_per_digit.append(last_id)

    tot_files_per_digit = np.array(tot_files_per_digit)
    acc_files_per_digit = np.array(acc_files_per_digit)
    all_files_per_digit = np.array(all_files_per_digit)

    tot_files = acc_files_per_digit[-1]
    print(tot_files_per_digit)
    print(acc_files_per_digit)

    while True:
        # These lines are for sequential selection
        if curr_idx >= len(ids) or curr_idx == -1:
            curr_idx = 0
            np.random.shuffle(
                ids
            )  # We shuffle the folders every time we have tested all the examples
        else:
            curr_idx += 1

        try:
            cur_file = ids[curr_idx]
            cur_folder = np.argmax(acc_files_per_digit >= cur_file)
            if cur_folder > 0:
                folder_idx = cur_file - acc_files_per_digit[cur_folder]
            else:
                folder_idx = cur_file

            file_name = join(path, F'{cur_folder}',
                             F'{all_files_per_digit[cur_folder][folder_idx]}')
            X_rgb = cv2.imread(file_name)
            X = X_rgb[:, :, 0]
            Y = cv2.Sobel(X, cv2.CV_64F, 1, 0, ksize=3)

            # plt.subplots(1,2)
            # plt.subplot(1,2,1)
            # plt.imshow(X)
            # plt.subplot(1,2,2)
            # plt.imshow(Y)
            # plt.show()

            # Normalizing the input
            X = X / 255
            max_val = 255 * 4
            Y = (Y + max_val) / (max_val * 2)

            XF = np.expand_dims(np.expand_dims(X, axis=2), axis=0)
            YF = np.expand_dims(np.expand_dims(Y, axis=2), axis=0)
            yield XF, YF
        except Exception as e:
            print(
                F"----- Not able to generate for curr_idx: {curr_idx}, file_name: {file_name}"
            )
Esempio n. 25
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#test-05 edge detection

import cv2 as cv

#read image
img = cv.imread("./opencv/lena.jpg")
cv.imshow("source", img)

#change image space for BGR to GRAY
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow("gray", gray)
cv.imwrite('./opencv/gray.jpg', gray)

#source image edge detection with Sobel
sobel = cv.Sobel(img, cv.CV_8U, 1, 1, 3)
cv.imshow("sobel", sobel)
cv.imwrite('./opencv/sobel.jpg', sobel)

#gray image dege detection with Sobel
graysobel = cv.Sobel(gray, cv.CV_8U, 1, 1, ksize = 3)
cv.imshow("graysobel", graysobel)
cv.imwrite('./opencv/sobelgray.jpg', graysobel)

#gray image edge detection with Laplace
laplace = cv.Laplacian(gray, cv.CV_8U, (3, 3))
cv.imshow('laplace', laplace)
cv.imwrite('./opencv/laplace.jpg', laplace)

#gray image dege detection with Canny
canny = cv.Canny(gray, 100, 200, (3, 3))
Esempio n. 26
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import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while True:
	_, frame = cap.read()
	frame = cv2.flip(frame,2)

	laplacian = cv2.Laplacian(frame, cv2.CV_64F)
	sobelx = cv2.Sobel(frame,cv2.CV_64F,1,0,ksize = 5)
	sobely = cv2.Sobel(frame,cv2.CV_64F,0,1,ksize = 5)
	edges = cv2.Canny(frame,70,70)

	cv2.imshow('normal',frame)
	cv2.imshow('laplacian',laplacian)
	#cv2.imshow('sobelx',sobelx)
	#cv2.imshow('sobely',sobely)
	cv2.imshow('edges',edges)

	if cv2.waitKey(1) & 0xFF == ord('a'):
		break

cap.release()
cv2.destroyAllWindows()
Esempio n. 27
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# 图像渐变和边缘检测

import cv2
import numpy as np

img = cv2.imread('../data/cluo.jpg')

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

lower_red = np.array([30, 150, 50])
upper_red = np.array([255, 255, 180])

mask = cv2.inRange(hsv, lower_red, upper_red)
res = cv2.bitwise_and(img, img, mask=mask)

laplacian = cv2.Laplacian(img, cv2.CV_64F)
sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)
sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)

# canny 边缘检测
edges = cv2.Canny(img, 100, 200)
cv2.imshow('Edges', edges)

cv2.imshow('Original', img)
cv2.imshow('Res', res)
cv2.imshow('laplacian', laplacian)
cv2.imshow('sobelx', sobelx)
cv2.imshow('sobely', sobely)

cv2.waitKey(0) & 0xFF
cv2.destroyAllWindows()
Esempio n. 28
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# @File    : op_readingImg.py
# @Software: PyCharm

import cv2
import numpy as np
import matplotlib.pyplot as plt

image_file = '../captcha/tupian/bailing.png'
img = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE)

# 输入图片的大小
h, w = img.shape
#print(h,w)

# 索贝尔滤波器(边缘检测器)
sobel_horizontal = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)

# 运行索贝尔垂直检测器
sobel_verrical = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)

# 拉普拉斯边缘检测器
laplacian = cv2.Laplacian(img, cv2.CV_64F)

# Canny边检测
canny = cv2.Canny(img, 50, 240)

#plt.imshow(img, cmap='gray', interpolation='bicubic')
#plt.imshow(sobel_horizontal)
#plt.xticks([]),plt.yticks([])
#plt.show()
cv2.imshow('sobel_horizontal', sobel_horizontal)
Esempio n. 29
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# img_cont = cv.drawContours(img_cont_ref/2, contours, 0, (255,0,0))
# cv.imshow('Imagine contures', img_cont)

img = cv.drawContours(image=img,
                      contours=contours,
                      contourIdx=0,
                      color=(255, 0, 0))

cv.circle(img=img,
          center=(np.int(x_centr), np.int(y_centr)),
          radius=1,
          color=(0, 255, 255),
          thickness=5)
cv.imshow('Image', img)

sobelx = cv.Sobel(img_gray, cv.CV_64F, 1, 0, ksize=5)
sobely = cv.Sobel(img_gray, cv.CV_64F, 0, 1, ksize=5)

grad = np.sqrt(sobelx**2 + sobely**2)
grad = grad / np.max(grad)

orient = np.rad2deg(np.arctan2(sobelx, sobely))
orient = orient + 180
orient = np.uint16(orient)

Rtable = [[] for i in range(360)]

for cont in contours[0]:
    wek_kat = orient[cont[0, 1], cont[0, 0]]
    wek_dist = np.sqrt((cont[0, 0] - x_centr)**2 + (cont[0, 1] - y_centr)**2)
    wek_orient = np.arctan2(cont[0, 1] - y_centr, cont[0, 0] - x_centr)
Esempio n. 30
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def process_plate(data, ip):
    #path_pic = "lisi.jpg"
    #Cargamos la foto enviada por el raspberry pi
    #image = cv2.imread(path_pic,1)
    #show_img(cv2,image,'original')
    #preprocessed_image = image
    image = cv2.imdecode(data, 1)
    preprocessed_image = cv2.imdecode(data, 1)
    cv2.imwrite('original_image_path.jpg', image)

    #se detecta la region donde se encuentra la placa en la imagen
    #convertimos la imagen a escala de grices
    escala_grices = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    #show_img('gris',escala_grices)
    cv2.imwrite('gri_image_path.jpg', escala_grices)

    #Se remueve un poco el ruido de la imagen(osea los ejes verticales)
    blur = cv2.GaussianBlur(escala_grices, (5, 5), 0)
    #show_img('ruido',blur)
    cv2.imwrite('difum_image_path.jpg', blur)

    #Buscamos el gradiente
    grad = cv2.Sobel(blur, cv2.CV_8U, 1, 0, ksize=3)
    #show_img('gradiente',grad)
    cv2.imwrite('grad_image_path.jpg', grad)

    #se aplica un filtro para combertir la imagen a una imagen binaria(0 y 1)
    _, umbral = cv2.threshold(grad, 0, 255,
                              cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    #show_img('threshold',umbral)
    cv2.imwrite('thres_image_path.jpg', umbral)

    #Operacion Morfologica para quitar los espacios en blanco entre cada linea de borde vertical
    Estructura = cv2.getStructuringElement(cv2.MORPH_RECT, (23, 4))
    morfo = cv2.morphologyEx(umbral, cv2.MORPH_CLOSE, Estructura)
    #show_img('Morfologica',morfo)
    #se obtuviero las posibles regiones donde se encuetra la placa
    cv2.imwrite('mor_image_path.jpg', morfo)

    contours, _ = cv2.findContours(morfo, cv2.RETR_EXTERNAL,
                                   cv2.CHAIN_APPROX_NONE)
    draw_contours(image, contours)
    #show_img("candidatas",image)
    cv2.imwrite('candi_image_path.jpg', image)

    try:
        verify_plate(preprocessed_image, image, contours)
    except:
        print "Not plate found"
        not_image_found(parsed_plate)
        return False

    #show_img('no pro',preprocessed_image)
    #show_img('verified',image)
    cv2.imwrite('verified_image_path.jpg', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    tess = os.popen('tesseract bien_image_path.jpg output', "r").read()
    cat = os.popen('cat output.txt', "r").read()
    print cat
    #plat = cat.replace("-","")
    #print plat
    print ip
    #plate = plat.strip();
    #plate = "L8989"
    plate = cat.strip()
    parsed_plate = SCP_Parser(plate).parse_plate()

    #cam = 1
    print "request = Placa: " + parsed_plate + " Ip : " + str(ip)
    request = requests.get(
        "http://scpweb.herokuapp.com/api/authorize_plate?plate=" +
        parsed_plate + "&cam=192.168.0.41")
    print request.json()
    response = request.json()
    f = open('output.txt', 'r+')
    f.truncate()
    tess = None
    cat = None
    if parsed_plate.strip() == "IMA6EN":
        parsed_plate = "No encontrada"

    ui = UI("original_image_path.jpg", "gri_image_path.jpg",
            "difum_image_path.jpg", "grad_image_path.jpg",
            "thres_image_path.jpg", "mor_image_path.jpg",
            "candi_image_path.jpg", "verified_image_path.jpg",
            "verified_plate_image_path.jpg", "bien_image_path.jpg",
            "192.168.0.40", parsed_plate)

    not_image_found()

    #return (response['message'] !=  "Vehicle not found" and response['message'] != "Cam not found" and response['message'] != "Visitor not found" and response['message'])
    return (response['message'] == "Vehicle Found")