Ejemplo n.º 1
0
    def redraw(self):

        edge_img = cv.Mat()
        # 边缘检测
        cv.Canny(self.img_gray, edge_img, self.th1, self.th2)
        3  ###
        # 计算结果图
        if self.show_canny:
            show_img = cv.Mat()
            cv.cvtColor(edge_img, show_img, cv.CV_GRAY2BGR)
        else:
            show_img = self.img.clone()
        4  ###
        # 线段检测
        theta = self.theta / 180.0 * np.pi
        lines = cv.HoughLinesP(edge_img, self.rho, theta, self.hough_th,
                               self.minlen, self.maxgap)
        for line in lines:
            cv.line(show_img, cv.asPoint(line[:2]), cv.asPoint(line[2:]),
                    cv.CV_RGB(255, 0, 0), 2)
        5  ###
        # 圆形检测
        circles = cv.HoughCircles(self.img_smooth,
                                  3,
                                  self.dp,
                                  self.mindist,
                                  param1=self.param1,
                                  param2=self.param2)

        for circle in circles:
            cv.circle(show_img, cv.Point(int(circle[0]), int(circle[1])),
                      int(circle[2]), cv.CV_RGB(0, 255, 0), 2)

        cv.imshow("Hough Demo", show_img)
Ejemplo n.º 2
0
 def make_grid_img(self):
     img = self.img.clone()
     for i in range(0, self.w, 30):
         cv.line(img, cv.Point(i, 0), cv.Point(i, self.h),
                 cv.CV_RGB(0, 0, 0), 1)
     for i in range(0, self.h, 30):
         cv.line(img, cv.Point(0, i), cv.Point(self.w, i),
                 cv.CV_RGB(0, 0, 0), 1)
     return img
Ejemplo n.º 3
0
    def redraw(self):
        # 同时显示两幅图像
        w = self.img1.size().width
        h = self.img1.size().height
        show_img = cv.Mat(cv.Size(w * 2, h), cv.CV_8UC3)
        for i in range(3):
            show_img[:, :w, i] = self.img1[:]
            show_img[:, w:, i] = self.img2[:]

        # 绘制特征线条
        if self.draw_circle:
            self.draw_keypoints(show_img, self.keypoints1, 0)
            self.draw_keypoints(show_img, self.keypoints2, w)

        # 绘制直线连接距离小于阈值的两个特征点
        for idx1 in np.where(self.mindist < self.max_distance)[0]:
            idx2 = self.idx_mindist[idx1]
            pos1 = self.keypoints1[int(idx1)].pt
            pos2 = self.keypoints2[int(idx2)].pt

            p1 = cv.Point(int(pos1.x), int(pos1.y))
            p2 = cv.Point(int(pos2.x) + w, int(pos2.y))
            cv.line(show_img, p1, p2, cv.CV_RGB(0, 255, 255), lineType=16)

        cv.imshow("SURF Demo", show_img)
Ejemplo n.º 4
0
 def affine(self):
     self.img2 = cv.Mat()
     M = cv.asMat(self.m, force_single_channel=True)
     cv.warpAffine(self.img1,
                   self.img2,
                   M,
                   self.img1.size(),
                   borderValue=cv.CV_RGB(255, 255, 255))
Ejemplo n.º 5
0
 def redraw(self):
     M = cv.asMat(self.m, force_single_channel=True)
     size = cv.Size(int(self.size[0, 0]), int(self.size[0, 1]))
     img2 = cv.Mat()
     if size.width > 0 and size.height > 0:
         cv.warpAffine(self.img,
                       img2,
                       M,
                       size,
                       borderValue=cv.CV_RGB(255, 255, 255))
         cv.imshow("Affine Demo", img2)
Ejemplo n.º 6
0
    def process(self, input_images, connected_outs):
        if len(input_images) == 0:
            return FAIL
        src = input_images['Input']
        dist_res = int(self.getParamContent('Distance resolution'))
        angle_res = int(self.getParamContent('Angle resolution (degrees)'))
        acc_thresh = int(self.getParamContent('Accumulator threshold'))
        min_length = int(self.getParamContent('Minimum length'))
        max_gap = int(self.getParamContent('Maximum gap'))
        choice = self.getParamContent("Type of Hough transform")
        if src.ndim > 2:
            print "In '%s': The hough transform takes a binary image (or 8-bit) as input." % self.name
            return FAIL
        color_dst = numpy.empty((src.shape[0], src.shape[1], 3), dtype='uint8')
        pycv.cvtColor(pycv.asMat(src), pycv.asMat(color_dst), pycv.CV_GRAY2BGR)

        if choice == "Standard":
            lines = pycv.HoughLines(pycv.asMat(src), dist_res,
                                    pycv.CV_PI / angle_res, acc_thresh)
            margin = 0.04
            n = 8
            pi = math.pi
            h, w = src.shape[0:2]
            for i in range(
                    min(len(lines),
                        int(self.getParamContent("draw # lines")))):
                l = lines[i]
                rho = l[0]
                theta = l[1]
                if theta > 3 * pi / 4: theta -= pi
                if abs(rho) < w / n and abs(theta) < margin: pass
                elif abs(rho) > w - w / n and abs(theta) < margin: pass
                elif abs(rho) < h / n and abs(theta - pi / 2) < margin: pass
                elif abs(rho) > h - h / n and abs(theta - pi / 2) < margin:
                    pass
                else:
                    continue
                a = math.cos(theta)
                b = math.sin(theta)
                x0 = a * rho
                y0 = b * rho
                pt1 = pycv.Point(int(round(x0 + 2000 * (-b))),
                                 int(round(y0 + 2000 * (a))))
                pt2 = pycv.Point(int(round(x0 - 2000 * (-b))),
                                 int(round(y0 - 2000 * (a))))
                pycv.line(
                    pycv.asMat(color_dst), pt1, pt2,
                    pycv.CV_RGB(random.randint(0, 255), random.randint(0, 255),
                                random.randint(0, 255)), 2, 8)

        else:
            lines = pycv.HoughLinesP(pycv.asMat(src), dist_res,
                                     pycv.CV_PI / angle_res, acc_thresh,
                                     min_length, max_gap)
            for l in lines:
                pycv.line(pycv.asMat(color_dst),
                          pycv.Point(int(l[0]), int(l[1])),
                          pycv.Point(int(l[2]), int(l[3])),
                          pycv.CV_RGB(*getRandColor()), 2, 8)
        self.lines = [(item[0], item[1]) for item in lines]
        return {
            self.output_names[0]: color_dst,
            self.output_names[1]: self.lines
        }
Ejemplo n.º 7
0
 def draw_keypoints(self, img, keypoints, offset):
     for kp in keypoints:
         center = cv.Point(int(kp.pt.x) + offset, int(kp.pt.y))
         cv.circle(img, center, int(kp.size * 0.25), cv.CV_RGB(255, 255, 0))
Ejemplo n.º 8
0
    # 左键松开时,使用watershed进行图像分割
    if event == cv.CV_EVENT_LBUTTONUP:
        seed += 1
        tmp_markers = markers.clone()
        cv.watershed(img, tmp_markers)
        color_map = tmp_markers[:].astype(np.int)

        img3 = img2.clone()
        img4 = cv.asMat(palette[color_map])
        cv.addWeighted(img3, 1.0, img4, mask_opacity, 0, img3)
        cv.imshow("Watershed Demo", img3)


# 区域的颜色列表
marks_color = [
    cv.CV_RGB(0, 0, 0),
    cv.CV_RGB(255, 0, 0),
    cv.CV_RGB(0, 255, 0),
    cv.CV_RGB(0, 0, 255),
    cv.CV_RGB(255, 255, 0),
    cv.CV_RGB(0, 255, 255),
    cv.CV_RGB(255, 0, 255),
    cv.CV_RGB(255, 255, 255)
]

# 将颜色列表转换为调色板数组,只取前三个通道的值
palette = np.array([c.ndarray[:-1] for c in marks_color], dtype=np.uint8)

seed = 1  # 从序号1开始设置区域颜色
mask_opacity = 0.5  # 绘制区域颜色的透明度