修改项目布局

src/SanDCJ/KeyPressModule.py

@@ -1,18 +0,0 @@
-import pygame
-
-def init():
-    pygame.init()
-    win = pygame.display.set_mode((400, 400))
-
-def getKey(keyName):
-    ans = False
-    for eve in pygame.event.get(): pass
-    keyInput = pygame.key.get_pressed()
-    myKey = getattr(pygame,'K_{}'.format(keyName))
-    if keyInput[myKey]:
-        ans = True
-    pygame.display.update()
-    return ans
-
-if __name__ == '__main__':
-    init()

src/SanDCJ/calib_RGB.py

@@ -1,327 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Homework: Calibrate the Camera with ZhangZhengyou Method.
-Picture File Folder: ".\pic\RGB_camera_calib_img", Without Distort.
-
-By YouZhiyuan 2019.11.18
-"""
-
-import os
-import cv2
-import json
-import numpy as np
-import glob
-import matplotlib.pyplot as plt
-
-from PIL import Image
-def cart2hom(arr):
-    """ Convert catesian to homogenous points by appending a row of 1s
-    :param arr: array of shape (num_dimension x num_points)
-    :returns: array of shape ((num_dimension+1) x num_points)
-    """
-    if arr.ndim == 1:
-        return np.hstack([arr, 1])
-    return np.asarray(np.vstack([arr, np.ones(arr.shape[1])]))
-def compute_P_from_essential(E):
-    """ Compute the second camera matrix (assuming P1 = [I 0])
-        from an essential matrix. E = [t]R
-    :returns: list of 4 possible camera matrices.
-    """
-    U, S, V = np.linalg.svd(E)
-
-    # Ensure rotation matrix are right-handed with positive determinant
-    if np.linalg.det(np.dot(U, V)) < 0:
-        V = -V
-
-    # create 4 possible camera matrices (Hartley p 258)
-    W = np.array([[0, -1, 0], [1, 0, 0], [0, 0, 1]])
-    P2s = [np.vstack((np.dot(U, np.dot(W, V)).T, U[:, 2])).T,
-           np.vstack((np.dot(U, np.dot(W, V)).T, -U[:, 2])).T,
-           np.vstack((np.dot(U, np.dot(W.T, V)).T, U[:, 2])).T,
-           np.vstack((np.dot(U, np.dot(W.T, V)).T, -U[:, 2])).T]
-
-    return P2s
-def linear_triangulation(p1, p2, m1, m2):
-    """
-    Linear triangulation (Hartley ch 12.2 pg 312) to find the 3D point X
-    where p1 = m1 * X and p2 = m2 * X. Solve AX = 0.
-    :param p1, p2: 2D points in homo. or catesian coordinates. Shape (3 x n)
-    :param m1, m2: Camera matrices associated with p1 and p2. Shape (3 x 4)
-    :returns: 4 x n homogenous 3d triangulated points
-    """
-    num_points = p1.shape[1]
-    res = np.ones((4, num_points))
-
-    for i in range(num_points):
-        A = np.asarray([
-            (p1[0, i] * m1[2, :] - m1[0, :]),
-            (p1[1, i] * m1[2, :] - m1[1, :]),
-            (p2[0, i] * m2[2, :] - m2[0, :]),
-            (p2[1, i] * m2[2, :] - m2[1, :])
-        ])
-
-        _, _, V = np.linalg.svd(A)
-        X = V[-1, :4]
-        res[:, i] = X / X[3]
-
-    return res
-
-def writetofile(dict, path):
-    for index, item in enumerate(dict):
-        dict[item] = np.array(dict[item])
-        dict[item] = dict[item].tolist()
-    js = json.dumps(dict)
-    with open(path, 'w') as f:
-        f.write(js)
-        print("参数已成功保存到文件")
-def readfromfile(path):
-    with open(path, 'r') as f:
-        js = f.read()
-        mydict = json.loads(js)
-    print("参数读取成功")
-    return mydict
-def scale_and_translate_points(points):
-    """ Scale and translate image points so that centroid of the points
-        are at the origin and avg distance to the origin is equal to sqrt(2).
-    :param points: array of homogenous point (3 x n)
-    :returns: array of same input shape and its normalization matrix
-    """
-    x = points[0]
-    y = points[1]
-    center = points.mean(axis=1)  # mean of each row
-    cx = x - center[0]  # center the points
-    cy = y - center[1]
-    dist = np.sqrt(np.power(cx, 2) + np.power(cy, 2))
-    scale = np.sqrt(2) / dist.mean()
-    norm3d = np.array([
-        [scale, 0, -scale * center[0]],
-        [0, scale, -scale * center[1]],
-        [0, 0, 1]
-    ])
-
-    return np.dot(norm3d, points), norm3d
-def correspondence_matrix(p1, p2):
-    p1x, p1y = p1[:2]
-    p2x, p2y = p2[:2]
-
-    return np.array([
-        p1x * p2x, p1x * p2y, p1x,
-        p1y * p2x, p1y * p2y, p1y,
-        p2x, p2y, np.ones(len(p1x))
-    ]).T
-
-    return np.array([
-        p2x * p1x, p2x * p1y, p2x,
-        p2y * p1x, p2y * p1y, p2y,
-        p1x, p1y, np.ones(len(p1x))
-    ]).T
-
-def compute_image_to_image_matrix(x1, x2, compute_essential=False):
-    """ Compute the fundamental or essential matrix from corresponding points
-        (x1, x2 3*n arrays) using the 8 point algorithm.
-        Each row in the A matrix below is constructed as
-        [x'*x, x'*y, x', y'*x, y'*y, y', x, y, 1]
-    """
-    A = correspondence_matrix(x1, x2)
-    # compute linear least square solution
-    U, S, V = np.linalg.svd(A)
-    F = V[-1].reshape(3, 3)
-
-    # constrain F. Make rank 2 by zeroing out last singular value
-    U, S, V = np.linalg.svd(F)
-    S[-1] = 0
-    if compute_essential:
-        S = [1, 1, 0]  # Force rank 2 and equal eigenvalues
-    F = np.dot(U, np.dot(np.diag(S), V))
-
-    return F
-
-def compute_normalized_image_to_image_matrix(p1, p2, compute_essential=False):
-    """ Computes the fundamental or essential matrix from corresponding points
-        using the normalized 8 point algorithm.
-    :input p1, p2: corresponding points with shape 3 x n
-    :returns: fundamental or essential matrix with shape 3 x 3
-    """
-    n = p1.shape[1]
-    if p2.shape[1] != n:
-        raise ValueError('Number of points do not match.')
-
-    # preprocess image coordinates
-    p1n, T1 = scale_and_translate_points(p1)
-    p2n, T2 = scale_and_translate_points(p2)
-
-    # compute F or E with the coordinates
-    F = compute_image_to_image_matrix(p1n, p2n, compute_essential)
-
-    # reverse preprocessing of coordinates
-    # We know that P1' E P2 = 0
-    F = np.dot(T1.T, np.dot(F, T2))
-
-    return F / F[2, 2]
-def skew(x):
-    """ Create a skew symmetric matrix *A* from a 3d vector *x*.
-        Property: np.cross(A, v) == np.dot(x, v)
-    :param x: 3d vector
-    :returns: 3 x 3 skew symmetric matrix from *x*
-    """
-    return np.array([
-        [0, -x[2], x[1]],
-        [x[2], 0, -x[0]],
-        [-x[1], x[0], 0]
-    ])
-def reconstruct_one_point(pt1, pt2, m1, m2):
-    """
-        pt1 and m1 * X are parallel and cross product = 0
-        pt1 x m1 * X  =  pt2 x m2 * X  =  0
-    """
-    A = np.vstack([
-        np.dot(skew(pt1), m1),
-        np.dot(skew(pt2), m2)
-    ])
-    U, S, V = np.linalg.svd(A)
-    P = np.ravel(V[-1, :4])
-
-    return P / P[3]
-
-def compute_essential_normalized(p1, p2):
-    return compute_normalized_image_to_image_matrix(p1, p2, compute_essential=True)
-def calib(inter_corner_shape, size_per_grid, img_dir,img_type,SaveParamPath):
-    # criteria: only for subpix calibration, which is not used here.
-    # criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
-    w,h = inter_corner_shape
-    # cp_int: corner point in int form, save the coordinate of corner points in world sapce in 'int' form
-    # like (0,0,0), (1,0,0), (2,0,0) ....,(10,7,0).
-    cp_int = np.zeros((w*h,3), np.float32)
-    cp_int[:,:2] = np.mgrid[0:w,0:h].T.reshape(-1,2)
-    # cp_world: corner point in world space, save the coordinate of corner points in world space.
-    cp_world = cp_int*size_per_grid
-    
-    obj_points = [] # the points in world space
-    img_points = [] # the points in image space (relevant to obj_points)
-    images = glob.glob(img_dir + os.sep + '**.' + img_type)
-    for i,fname in enumerate(images):
-        img = cv2.imread(images[i])
-        gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-        # find the corners, cp_img: corner points in pixel space.
-        ret, cp_img = cv2.findChessboardCorners(gray_img, (w,h), None)
-        # if ret is True, save.
-        if ret == True:
-            # cv2.cornerSubPix(gray_img,cp_img,(11,11),(-1,-1),criteria)
-            obj_points.append(cp_world)
-            img_points.append(cp_img)
-            # view the corners
-            cv2.drawChessboardCorners(img, (w,h), cp_img, ret)
-            cv2.imshow('FoundCorners',img)
-            #写入
-            cv2.imwrite('Checkerboard_Image/Temp_JPG/Temp_' + str(i) + '.jpg', img)
-            cv2.waitKey(1)
-    cv2.destroyAllWindows()
-    # calibrate the camera
-    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, gray_img.shape[::-1], None, None)
-    dict = {'ret': ret, 'mtx': mtx, 'dist': dist, 'rvecs': rvecs, 'tvecs': tvecs}
-    writetofile(dict, SaveParamPath)
-    print (("ret:"),ret)
-    print (("internal matrix:\n"),mtx)
-    # in the form of (k_1,k_2,p_1,p_2,k_3)
-    print (("distortion cofficients:\n"),dist)
-    print (("rotation vectors:\n"),rvecs)
-    print (("translation vectors:\n"),tvecs)
-    # calculate the error of reproject
-    total_error = 0
-    for i in range(len(obj_points)):
-        img_points_repro, _ = cv2.projectPoints(obj_points[i], rvecs[i], tvecs[i], mtx, dist)
-        error = cv2.norm(img_points[i], img_points_repro, cv2.NORM_L2)/len(img_points_repro)
-        total_error += error
-    print(("Average Error of Reproject: "), total_error/len(obj_points))
-    
-    return mtx,dist
-
-def epipolar_geometric(Images_Path, K):
-    IMG = glob.glob(Images_Path)
-    img1, img2 = cv2.imread(IMG[0]), cv2.imread(IMG[1])
-    img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
-    img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
-
-    # Initiate SURF detector
-    SURF = cv2.xfeatures2d.SURF_create()
-
-    # compute keypoint & descriptions
-    keypoint1, descriptor1 = SURF.detectAndCompute(img1_gray, None)
-    keypoint2, descriptor2 = SURF.detectAndCompute(img2_gray, None)
-    print("角点数量：", len(keypoint1), len(keypoint2))
-
-    # Find point matches
-    bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
-    matches = bf.match(descriptor1, descriptor2)
-    print("匹配点数量：", len(matches))
-
-    src_pts = np.asarray([keypoint1[m.queryIdx].pt for m in matches])
-    dst_pts = np.asarray([keypoint2[m.trainIdx].pt for m in matches])
-    # plot
-    knn_image = cv2.drawMatches(img1_gray, keypoint1, img2_gray, keypoint2, matches[:-1], None, flags=2)
-    image_ = Image.fromarray(np.uint8(knn_image))
-    image_.save("MatchesImage.jpg")
-
-    # Constrain matches to fit homography
-    retval, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 100.0)
-
-    # We select only inlier points
-    points1 = src_pts[mask.ravel() == 1]
-    points2 = dst_pts[mask.ravel() == 1]
-
-    points1 = cart2hom(points1.T)
-    points2 = cart2hom(points2.T)
-    # plot
-    fig, ax = plt.subplots(1, 2)
-    ax[0].autoscale_view('tight')
-    ax[0].imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
-    ax[0].plot(points1[0], points1[1], 'r.')
-    ax[1].autoscale_view('tight')
-    ax[1].imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
-    ax[1].plot(points2[0], points2[1], 'r.')
-    plt.savefig('MatchesPoints.jpg')
-    fig.show()
-    #
-
-    points1n = np.dot(np.linalg.inv(K), points1)
-    points2n = np.dot(np.linalg.inv(K), points2)
-    E = compute_essential_normalized(points1n, points2n)
-    print('Computed essential matrix:', (-E / E[0][1]))
-
-    P1 = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])
-    P2s = compute_P_from_essential(E)
-
-    ind = -1
-    for i, P2 in enumerate(P2s):
-        # Find the correct camera parameters
-        d1 = reconstruct_one_point(points1n[:, 0], points2n[:, 0], P1, P2)
-        # Convert P2 from camera view to world view
-        P2_homogenous = np.linalg.inv(np.vstack([P2, [0, 0, 0, 1]]))
-        d2 = np.dot(P2_homogenous[:3, :4], d1)
-        if d1[2] > 0 and d2[2] > 0:
-            ind = i
-
-    P2 = np.linalg.inv(np.vstack([P2s[ind], [0, 0, 0, 1]]))[:3, :4]
-    Points3D = linear_triangulation(points1n, points2n, P1, P2)
-
-    return Points3D
-
-    
-# if __name__ == '__main__':
-#     inter_corner_shape = (9,6)
-#     size_per_grid = 0.023
-#     #储存用来标定相机的照片，这里是为畸变的照片
-#     img_dir = ".\\pic\\RGB_camera_calib_img_1"
-#     img_type = "jpg"
-#     #储存标定相机之后的参数，应该相机的内外参数与畸变参数
-#     CameraParam_Path = '.\\CameraParam.txt'
-#     #要重建的目标图片
-#     Images_Path='SubstitutionCalibration_Image_1/*.jpg'
-#     #计算相机参数
-#     calib(inter_corner_shape, size_per_grid, img_dir,img_type,CameraParam_Path)
-#     # 读取相机参数
-#     config = readfromfile(CameraParam_Path)
-#     K = np.array(config['mtx'])
-#     # 计算3D点
-#     Points3D = epipolar_geometric(Images_Path, K)
-

src/SanDCJ/huidu_shuai.py

@@ -1,46 +0,0 @@
-import  PIL.Image
-import numpy
-import os
-import shutil
-def sum_right(path):
-    img = PIL.Image.open(path)
-    array = numpy.array(img)
-    num = array.sum(axis=0)
-    print(type(num))
-    res_left = 0
-    res_right = 0
-    for i in range(256,512):
-        res_right += num[i]
-    print(res_right)
-
-# if __name__ == '__main__':
-#     dir2 = r"C:\Users\xinluo\PycharmProjects\pythonProject\Camera Roll"
-#     dir1 = r"C:\Users\xinluo\PycharmProjects\pythonProject\Saved Pictures"
-#     names = os.listdir(dir1)
-#     n = len(names)
-#     print("文件数量",n)
-#     res = 0
-#     average_5 = 25565356
-#     average_25 = 26409377
-#     average_5_right = 10006019
-#     #average_tmp = (average_25+average_5)//2
-#     count = 0
-#     #show(os.path.join(dir1, "uni4F6C.png"))
-#     for i in range(n):
-#         #取图片
-#         img = PIL.Image.open(os.path.join(dir1,names[i]))
-#         file = os.path.join(dir1,names[i])
-#         rmfile = os.path.join(dir2,names[i])
-#         array = numpy.array(img)
-#         num = array.sum(axis=0)
-#         res_right = 0
-#         for i in range(256, 512):
-#             res_right += num[i]
-#         average_5_right += res_right/n
-#
-#         if (res_right > average_5_right).all():
-#              shutil.copyfile(file, rmfile)
-#              os.remove(file)
-#              count += 1
-#     print(average_5_right)
-#     print(count)

src/SanDCJ/qingxi_shuai.py

@@ -1,27 +0,0 @@
-# -*- coding: UTF-8 -*-
-
-import argparse
-import cv2
-import os
-
-# 使用方法：命令行cd到脚本所在文件夹，python picture.py -i "图片路径"
-#便历图片，合格则保存，不合格则删除
-
-
-def variance_of_laplacian(image):
-    # 使用拉普拉斯算子提取图像边缘信息，计算边缘的平均方差
-    return cv2.Laplacian(image, cv2.CV_64F).var()
-def read_path(file_pathname):
-    #遍历该目录下的所有图片文件
-    for filename in os.listdir(file_pathname):
-        print(filename)
-        image = cv2.imread(file_pathname+'/'+filename)
-        # 取图片的灰度通道
-        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-        fm = variance_of_laplacian(gray)
-
-        # 如果得分比阈值低，认为是模糊
-        if fm < 100.0:
-            os.remove(file_pathname+'/'+filename)
-#read_path(r"C:\Users\xinluo\PycharmProjects\pythonProject\Saved Pictures")
-#print(os.getcwd())

src/SanDCJ/video.py

@@ -1,340 +0,0 @@
-import logging
-import os
-import shutil
-
-import PIL
-from PyQt5 import QtCore, QtGui, QtWidgets
-import numpy as np
-import matplotlib
-import sys
-import cv2
-from djitellopy import tello
-import matplotlib.pyplot as plt
-# matplotlib.figure 模块提供了顶层的Artist(图中的所有可见元素都是Artist的子类)，它包含了所有的plot元素
-from matplotlib.figure import Figure
-from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg # pyqt5的画布
-import calib_RGB
-import huidu_shuai,qingxi_shuai,xiangsu_shuai,video_to_photo
-import argparse
-
-class MyMatplotlibFigure(FigureCanvasQTAgg):
-  """
-  创建一个画布类，并把画布放到FigureCanvasQTAgg
-  """
-  def __init__(self, width=10, heigh=10, dpi=100):
-    plt.rcParams['figure.facecolor'] = 'r' # 设置窗体颜色
-    plt.rcParams['axes.facecolor'] = 'b' # 设置绘图区颜色
-    self.width_ = width
-    self.heigh_ = heigh
-    self.dpi = dpi
-    self.figs = Figure(figsize=(self.width_, self.heigh_), dpi=self.dpi)
-    super(MyMatplotlibFigure, self).__init__(self.figs) # 在父类种激活self.fig， 否则不能显示图像
-    #self.axes = self.figs.add_subplot(111,projection='3d')
-    fig = plt.figure()
-    #fig.suptitle('3D reconstructed', fontsize=16)
-    #self.axes = fig.gca(projection='3d')
-    self.axes=fig.add_subplot(111, projection='3d')
-
-  def mat_plot_drow_axes(self, t, s, l):
-
-        """
-        用清除画布刷新的方法绘图
-        :return:
-        """
-        self.axes.cla()  # 清除绘图区
-        self.axes.spines['top'].set_visible(False) # 顶边界不可见
-        self.axes.spines['right'].set_visible(False) # 右边界不可见
-        # fig = plt.figure()
-        # fig.suptitle('3D reconstructed', fontsize=16)
-        # ax = plt.axes(projection='3d')
-        # 设置左、下边界在（0，0）处相交
-        # self.axes.spines['bottom'].set_position(('data', 0)) # 设置y轴线原点数据为 0
-        self.axes.spines['left'].set_position(('data', 0)) # 设置x轴线原点数据为 0
-        # self.axes.plot(t, s, l, 'b.')
-        # self.set_xlabel('x axis')
-        # self.set_ylabel('y axis')
-        # self.set_zlabel('z axis')
-        # self.view_init(elev=135, azim=90)
-        # plt.savefig('Reconstruction.jpg')
-        # plt.show()
-        # self.axes.plot(t, s, l, 'o-r', linewidth=0.5)
-        self.axes.scatter(t,s,l, 'b.')
-        #self.axes.plot_surface(t,s,l)
-        self.axes.set_xlabel('x axis')
-        self.axes.set_ylabel('y axis')
-        self.axes.set_zlabel('z axis')
-        self.axes.view_init(elev=135, azim=90)
-        plt.savefig('Reconstruction.jpg')
-        plt.show()
-        self.figs.canvas.draw() # 这里注意是画布重绘，self.figs.canvas
-        self.figs.canvas.flush_events() # 画布刷新self.figs.canvas
-
-
-
-
-class Ui_MainWindow(QtWidgets.QWidget):
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.timer_camera = QtCore.QTimer()
-        self.cap = cv2.VideoCapture()
-        self.CAM_NUM = 0
-
-        self.set_ui()
-        self.slot_init()
-
-
-    def set_ui(self):
-
-        self.setWindowTitle('虚拟沙盘')  # 标题
-
-        self.resize(1200, 800)  # 窗口尺寸
-
-        #self.status = self.statusTip('虚拟沙盘3D建模系统')  # 消息
-
-        #self.status.showMessage()
-        self.setWindowIcon(QIcon("./img/mou.ico"))  # 图标
-
-        self.drawn_back()
-
-        self.__layout_main = QtWidgets.QVBoxLayout()
-        self.__layout_fun_button = QtWidgets.QHBoxLayout()
-        self.__layout_data_show = QtWidgets.QHBoxLayout()
-        self.button_open_camera = QtWidgets.QPushButton('打开摄像头')
-        self.button_operate = QtWidgets.QPushButton('键盘控制')
-        self.button_takephoto = QtWidgets.QPushButton('视频转图片')
-        self.button_make = QtWidgets.QPushButton('建模')
-        self.button_close = QtWidgets.QPushButton('退出')
-
-        self.button_open_camera.setMinimumHeight(50)
-        self.button_close.setMinimumHeight(50)
-        self.button_operate.setMinimumHeight(50)
-        self.button_takephoto.setMinimumHeight(50)
-        self.button_make.setMinimumHeight(50)
-        self.button_open_camera.move(10, 100)
-        self.label_show_camera = QtWidgets.QLabel()
-        #self.label_show_camera.move(400,400)
-        self.label_show_camera.setFixedSize(841, 681)
-        self.__layout_fun_button.addWidget(self.button_open_camera)
-        self.__layout_fun_button.addWidget(self.button_operate)
-        self.__layout_fun_button.addWidget(self.button_takephoto)
-        self.__layout_fun_button.addWidget(self.button_make)
-        self.__layout_fun_button.addWidget(self.button_close)
-        self.__layout_main.addLayout(self.__layout_fun_button)
-        self.__layout_main.addWidget(self.label_show_camera)
-        self.setLayout(self.__layout_main)
-##########################################
-
-
-
-    # 将信号与槽关联
-    #self.btn1.clicked.connect(self.onClick_Button)
-    #self.btn2.clicked.connect(self.showDialog)
-    def slot_init(self):
-        self.button_open_camera.clicked.connect(
-            self.button_open_camera_clicked)
-        self.timer_camera.timeout.connect(self.show_camera)
-        self.button_close.clicked.connect(self.onClick_Button)
-        self.button_make.clicked.connect(self.showDialog)
-        self.button_takephoto.clicked.connect(self.take_photo)
-        self.button_operate.clicked.connect(self.keyboard_control)
-    def drawn_back(self):
-        self.palette = QPalette()
-        self.palette.setBrush(QPalette.Background, QBrush(QPixmap("./img/back.jpeg")))
-        self.setPalette(self.palette)
-    def keyboard_control(self):
-        dialog = QtWidgets.QDialog()
-        dialog.resize(400, 300)
-        # button = QtWidgets.QPushButton('储存', dialog)
-        # button.clicked.connect(dialog.close)
-        # button.move(350, 550)
-        dialog.setWindowTitle('键盘控制')
-        dialog.setWindowModality(QtCore.Qt.ApplicationModal)
-
-        # dialog.__layout_main = QtWidgets.QHBoxLayout()
-        # dialog.__layout_fun_button = QtWidgets.QVBoxLayout()
-        # dialog.__layout_data_show = QtWidgets.QVBoxLayout()
-        # dialog.button_takeoff = QtWidgets.QPushButton('起飞')
-        # dialog.button_set_down = QtWidgets.QPushButton('降落')
-        # dialog.button.takeoff.setMinimumHeight(50)
-        # dialog.button_takeoff.move(250, 200)
-        # dialog.button_takeoff.setMinimumHeight(50)
-        # dialog.button_set_down.setMinimumHeight(50)
-        #
-        # dialog.button_takeoff.move(10, 100)
-
-        button_takeoff = QtWidgets.QPushButton('起飞', dialog)
-        button_takeoff.move(200, 100)
-
-        button_set_down = QtWidgets.QPushButton('降落', dialog)
-        button_set_down.move(200, 150)
-
-
-
-        lab1 = QLabel()
-        # lab1.resize(200, 200)  # 重设Label大小
-        # lab1.setScaledContents(True)  # 设置图片自适应窗口大小
-        lab1.setPixmap(QPixmap("./img/control.png"))
-
-        vbox = QVBoxLayout()
-
-        vbox.addWidget(lab1)
-
-        dialog.setLayout(vbox)
-        #dialog.label_show_camera = QtWidgets.QLabel()
-        # self.label_show_camera.move(400,400)
-        #dialog.label_show_camera.setFixedSize(841, 681)
-        # dialog.__layout_fun_button.addWidget(dialog.button_takeoff)
-        # dialog.__layout_fun_button.addWidget(dialog.button_set_down)
-        #
-        # # self.__layout_fun_button.addWidget(self.label)
-        # dialog.__layout_main.addLayout(dialog.__layout_fun_button)
-        # dialog.__layout_main.addWidget(lab1)
-        #
-        # dialog.setLayout(dialog.__layout_main)
-
-        #self.show()
-
-
-
-
-        dialog.show()
-
-        #sys.exit(app.exec_())
-        dialog.exec()
-    def take_photo(self):
-        #video to photo
-        args = video_to_photo.parse_args()
-        if not os.path.exists(args.output):
-            os.makedirs(args.output)
-        print('Called with args:')
-        print(args)
-        video_to_photo.process_video(args.input, args.output, args.skip_frame)
-
-        #灰度值筛选
-        dir2 = r"C:\Users\xinluo\PycharmProjects\pythonProject\Camera Roll"
-        dir1 = r"C:\Users\xinluo\PycharmProjects\pythonProject\Saved Pictures"
-        names = os.listdir(dir1)
-        n = len(names)
-        print("文件数量", n)
-        res = 0
-        average_5 = 25565356
-        average_25 = 26409377
-        average_5_right = 10006019
-        # average_tmp = (average_25+average_5)//2
-        count = 0
-        # show(os.path.join(dir1, "uni4F6C.png"))
-        for i in range(n):
-            # 取图片
-            print(1234)
-            img = PIL.Image.open(os.path.join(dir1, names[i]))
-            file = os.path.join(dir1, names[i])
-            rmfile = os.path.join(dir2, names[i])
-            array = np.array(img)
-            num = array.sum(axis=0)
-            res_right = 0
-            for i in range(256, 512):
-                res_right += num[i]
-            average_5_right += res_right / n
-#res_right > average_5_right
-            if (1):
-                print(4321)
-                shutil.copyfile(file, rmfile)
-                os.remove(file)
-                count += 1
-        print(average_5_right)
-        print(count)
-        #像素筛选
-        xiangsu_shuai.pixel()
-        #清晰度筛选
-        qingxi_shuai.read_path(r"C:\Users\xinluo\PycharmProjects\pythonProject\Camera Roll")
-
-    def button_open_camera_clicked(self):
-        if self.timer_camera.isActive() == False:
-            flag = self.cap.open(self.CAM_NUM)
-            if flag == False:
-                msg = QtWidgets.QMessageBox.warning(self, 'warning', "请检查无人机是否连接正确", buttons=QtWidgets.QMessageBox.Ok)
-            else:
-                self.timer_camera.start(30)
-                self.button_open_camera.setText('关闭摄像头')
-        else:
-            self.timer_camera.stop()
-            self.cap.release()
-            self.label_show_camera.clear()
-            self.button_open_camera.setText('打开摄像头')
-
-    def show_camera(self):
-        self.Drone = tello.Tello()  # 创建飞行器对象
-        self.Drone.connect()  # 连接到飞行器
-
-        self.Drone.streamon()  # 开启视频传输
-        self.Drone.LOGGER.setLevel(logging.ERROR)  # 只显示错误信息
-        #sleep(5)  # 等待视频初始化
-        #kp.init()  # 初始化按键处理模块
-        flag,OriginalImage = self.Drone.get_frame_read()
-        #flag, self.image = self.cap.read()
-        show = cv2.resize(OriginalImage, (640, 480))
-        show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
-        showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0],
-                                 QtGui.QImage.Format_RGB888)
-        self.label_show_camera.setPixmap(QtGui.QPixmap.fromImage(showImage))
-
-        #getKeyboardInput(drone=Drone, speed=70, image=Image)  # 按键控制
-        cv2.imshow("Drone Control Centre", OriginalImage)
-    def showDialog(self):
-        dialog = QtWidgets.QDialog()
-        dialog.resize(1800, 1600)
-        button = QtWidgets.QPushButton('储存', dialog)
-        button.clicked.connect(dialog.close)
-        button.move(1350, 750)
-        dialog.setWindowTitle('建模视图')
-        dialog.setWindowModality(QtCore.Qt.ApplicationModal)
-
-        dialog.label = QtWidgets.QLabel(dialog)
-        dialog.label.setGeometry(QtCore.QRect(0, 0, 800, 600))
-        # 实例化自定义画布类
-        dialog.canvas = MyMatplotlibFigure(width=5, heigh=4, dpi=100)
-        #dialog.plotcos()
-        # t = np.arange(0.0, 5.0, 0.01)
-        # s = np.cos(2 * np.pi * t)
-        # dialog.canvas.mat_plot_drow_axes(t, s)
-        # dialog.canvas.figs.suptitle("sin")  # 设置标题
-        # 重建3D点
-        dialog.canvas.mat_plot_drow_axes(Points3D[0], Points3D[1], Points3D[2])
-        dialog.canvas.figs.suptitle("SanDCJ")  # 设置标题
-
-        dialog.hboxlayout = QtWidgets.QHBoxLayout(dialog.label)
-        dialog.hboxlayout.addWidget(dialog.canvas)
-        #不展示dialog，只看figure
-        #dialog.exec()
-
-
-
-    def onClick_Button(self):
-        sender = self.sender() #获得button
-        #print(sender.text() + '按钮被按下')
-        app = QtWidgets.QApplication.instance()
-        #退出程序
-        app.quit()
-
-if __name__ == '__main__':
-    inter_corner_shape = (9, 6)
-    size_per_grid = 0.023
-    # 储存用来标定相机的照片，这里是为畸变的照片
-    img_dir = ".\\pic\\RGB_camera_calib_img_1"
-    img_type = "jpg"
-    # 储存标定相机之后的参数，应该相机的内外参数与畸变参数
-    CameraParam_Path = '.\\CameraParam.txt'
-    # 要重建的目标图片
-    Images_Path = 'SubstitutionCalibration_Image_1/*.jpg'
-    # 计算相机参数
-    calib_RGB.calib(inter_corner_shape, size_per_grid, img_dir, img_type, CameraParam_Path)
-    # 读取相机参数
-    config = calib_RGB.readfromfile(CameraParam_Path)
-    K = np.array(config['mtx'])
-    # 计算3D点
-    Points3D = calib_RGB.epipolar_geometric(Images_Path, K)
-
-    app = QtWidgets.QApplication(sys.argv)
-    ui = Ui_MainWindow()
-    ui.show()
-    sys.exit(app.exec_())

src/SanDCJ/video_to_photo.py

@@ -1,50 +0,0 @@
-import cv2
-import argparse
-import os
-
-
-def parse_args():
-    """
-    Parse input arguments
-    """
-    parser = argparse.ArgumentParser(description='Process pic')
-    parser.add_argument('--input', help='video to process', dest='input', default=None, type=str)
-    parser.add_argument('--output', help='pic to store', dest='output', default=None, type=str)
-    # default为间隔多少帧截取一张图片
-    parser.add_argument('--skip_frame', dest='skip_frame', help='skip number of video', default=10, type=int)
-    # input为输入视频的路径 ，output为输出存放图片的路径
-    args = parser.parse_args(['--input', r'C:\Users\xinluo\PycharmProjects\pythonProject\1234.mp4', '--output', r'C:\Users\xinluo\PycharmProjects\pythonProject\Saved Pictures'])
-    return args
-
-
-def process_video(i_video, o_video, num):
-    cap = cv2.VideoCapture(i_video)
-    num_frame = cap.get(cv2.CAP_PROP_FRAME_COUNT)
-    expand_name = '.jpg'
-    if not cap.isOpened():
-        print("Please check the path.")
-    cnt = 0
-    count = 0
-    while 1:
-        ret, frame = cap.read()
-        cnt += 1
-        #  how
-        # many
-        # frame
-        # to
-        # cut
-        if cnt % num == 0:
-            count += 1
-            cv2.imwrite(os.path.join(o_video, str(count) + expand_name), frame)
-
-        if not ret:
-            break
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    if not os.path.exists(args.output):
-        os.makedirs(args.output)
-    print('Called with args:')
-    print(args)
-    process_video(args.input, args.output, args.skip_frame)

src/SanDCJ/xiangsu_shuai.py

@@ -1,22 +0,0 @@
-import os
-from PIL import Image, ImageFile
-
-# ImageFile.LOAD_TRUNCATED_IMAGES = True    #如果图片太大报错可以使用这个
-
-
-
-def pixel():
-    b = 0
-    dir = r'C:\Users\xinluo\PycharmProjects\pythonProject\Camera Roll'  # 需要处理的图片目录
-    files = os.listdir(dir)  # 得到需要处理的所有图片
-    files.sort()  # 对图片进行排序
-    for each_bmp in files:  # 遍历图片，进行筛选
-
-        each_bmp_root = os.path.join(dir, each_bmp)  # 得到每个图片路径
-        image = Image.open(each_bmp_root)  # 打开每个图片
-        img = image.convert('RGB')  # 转化成RGB，其实图片大多都是RGB，即使灰度图也不一定转RGB，看需求
-        width = img.size[0]  # 获取图像的宽和长
-        height = img.size[1]
-
-        if (width < 650) or (height < 650):  # 判断图形尺寸有一条边小于600像素的直接删除
-            os.remove(each_bmp_root)

src/output/CameraParam.txt

@@ -1 +0,0 @@
-{"ret": 0.4314122041359198, "mtx": [[2100.6166591501005, 0.0, 1403.618417705093], [0.0, 2113.7390947053404, 646.1893342673758], [0.0, 0.0, 1.0]], "dist": [[0.13990518473421606, -1.1145664175828744, -0.004790328119068935, 0.0026096161906401906, 3.6214731996509606]], "rvecs": [[[-0.7978123239090978], [-0.04702517631008598], [-0.24880470852268546]], [[-0.7102956080455235], [-0.3090720535415836], [-0.7867973786851208]], [[-0.6368278308349462], [-0.5218103879290722], [-1.4312083539852147]], [[-0.17491074014363545], [-0.6110118008480857], [-2.2613798449225624]], [[-0.5539370466763418], [0.6786283240691443], [1.9972192831975362]], [[-0.6926384946929811], [0.48508347356507414], [1.291539049716794]], [[-0.6731599504036907], [0.3237097775813646], [0.6548079565882113]], [[-0.7086358440860253], [-0.004129106568727472], [-0.07548292774647616]], [[-0.20995761717360167], [0.04597378178747325], [-0.0014186337329382436]], [[-0.26550343514562097], [-0.08285668420620038], [-0.5685764616550046]]], "tvecs": [[[-0.11424945743525941], [-0.062201672080156], [0.623250900963298]], [[-0.13402771971905275], [-0.03658546400187072], [0.5638717521774091]], [[-0.1789018170809252], [-0.03103289417764265], [0.5334398621072209]], [[-0.024525082630873225], [0.058358619409959685], [0.4357932887715248]], [[0.041544723893712464], [-0.03773633166925087], [0.5049837316658362]], [[-0.007497958150572578], [-0.06758513406764177], [0.5513622056607101]], [[-0.043592194513050096], [-0.06494718112458993], [0.5727055938903775]], [[-0.09699030002785332], [-0.03960215672336743], [0.576438811466496]], [[-0.13117979132934424], [-0.055309826946615966], [0.5227525868997632]], [[-0.10429120632865405], [-0.018878934697527522], [0.5269810502507957]]]}

src/pic/README.txt

@@ -1,3 +0,0 @@
-1. IR_camera_calib_img文件夹下图片含有畸变，需要得到到相机的内外参数与畸变参数，并对畸变图片做矫正，矫正图片保存在save_dedistortion文件夹下；
-2. RGB_camera_calib_img文件夹下图片不含畸变，需要得到到相机的内外参数与畸变参数；
-3. 棋盘格规格为：12*9，格点长度0.02m，由于opencv输入参数为内角点个数，所以输入参数为11*8。