From b155297216002a6a60eecf57818a5211d6f8cd18 Mon Sep 17 00:00:00 2001
From: HazelWang0 <hugowell@163.com>
Date: Thu, 17 Feb 2022 21:46:11 +0800
Subject: [PATCH 1/2] update

---
 poses_bounds.npy         | Bin 0 -> 3664 bytes
 readnpy.py               |   6 +
 run.py                   |   7 +-
 testdraw.py              | 124 +++++++++++++++++++
 utils/calib 2.py         | 169 ++++++++++++++++++++++++++
 utils/calib 3 blender.py | 217 +++++++++++++++++++++++++++++++++
 utils/calib present.py   | 254 +++++++++++++++++++++++++++++++++++++++
 utils/calib.py           |  34 ++++--
 utils/load_blender.py    |  87 ++++++++++++++
 utils/trans.py           |  49 ++++++--
 utils/utils.py           |   2 +-
 11 files changed, 925 insertions(+), 24 deletions(-)
 create mode 100644 poses_bounds.npy
 create mode 100644 readnpy.py
 create mode 100644 testdraw.py
 create mode 100755 utils/calib 2.py
 create mode 100755 utils/calib 3 blender.py
 create mode 100755 utils/calib present.py
 create mode 100644 utils/load_blender.py

diff --git a/poses_bounds.npy b/poses_bounds.npy
new file mode 100644
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diff --git a/readnpy.py b/readnpy.py
new file mode 100644
index 0000000..5553818
--- /dev/null
+++ b/readnpy.py
@@ -0,0 +1,6 @@
+import numpy as np
+
+# 读取.npy文件
+arr = np.load("/root/vmip/myProject/nerf/camera/nerfCalibration/poses_bounds.npy")
+print(arr[24])
+print("load .npy done")
\ No newline at end of file
diff --git a/run.py b/run.py
index c979827..beef547 100644
--- a/run.py
+++ b/run.py
@@ -1,8 +1,9 @@
-from utils.trans import json_write
+from utils.trans import json_write,json_write_llff
 from utils.calib import set_calibration
 
 
 if __name__ == '__main__':
-    set_calibration()
-    json_write()
+    f = 612
+    c2w_metrix,h,w = set_calibration()
+    json_write_llff(c2w_metrix,h,w,f)
     
diff --git a/testdraw.py b/testdraw.py
new file mode 100644
index 0000000..67c86df
--- /dev/null
+++ b/testdraw.py
@@ -0,0 +1,124 @@
+import cv2
+import numpy as np
+import os
+import sys
+import glob
+import pickle
+import math
+
+import numpy as np
+import matplotlib as mpl
+from matplotlib import cm
+from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+
+def show():
+    # 创建画布
+    fig = plt.figure(figsize=(12, 8),
+                    facecolor='lightyellow'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='whitesmoke',
+                projection='3d' 
+                )
+
+
+    # -------------------------------- 绘制 3D 图形 --------------------------------
+    # # 二元函数定义域平面
+    # x = np.linspace(0, 9, 9)
+    # y = np.linspace(0, 9, 9)
+    # X, Y = np.meshgrid(x, y)
+    # # 平面 z=4.5 的部分
+    # ax.plot_surface(X,
+    #             Y,
+    #             Z=X*0+4.5,
+    #             color='g',
+    #             alpha=0.6
+    #            ) 
+
+    # # 散点图
+    # x = [1,2,3,4,5,6,7,8,9,10]
+    # y = [5,6,7,8,2,5,6,3,7,2]
+    # z = [1,2,6,3,2,7,3,3,7,2]
+
+    # ax.scatter(x, y, z, c='g', marker='o')
+
+    
+def showquiver():
+    # cp = [[1,2,3],[4,5,6]]
+    ax = plt.figure().add_subplot(projection='3d')
+
+    # Make the grid
+    # x, y, z = np.meshgrid(np.arange(-0.8, 1, 0.2),
+    #                     np.arange(-0.8, 1, 0.2),
+    #                     np.arange(-0.8, 1, 0.8))
+    x, y, z = np.meshgrid(np.arange(-0.8, 1, 0.2),
+                    np.arange(-0.8, 1, 0.2),
+                    np.arange(-0.8, 1, 0.8))
+
+    # Make the direction data for the arrows
+    u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z)
+    v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z)
+    w = (np.sqrt(2.0 / 3.0) * np.cos(np.pi * x) * np.cos(np.pi * y) *
+        np.sin(np.pi * z))
+
+    ax.quiver(x, y, z, u, v, w, length=0.1, normalize=True)
+    plt.show()
+
+def plot():
+    cp = [[1,2,3],[4,5,6]]
+    fig = plt.figure(figsize=(5, 5),
+                    facecolor='white'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='white',
+                projection='3d' 
+                )
+
+    # x1 = np.linspace(0,2,40)
+    # y1 = np.linspace(0,2,40)
+    x1,y1 = np.meshgrid()
+    x1, y1 = np.meshgrid(x1, y1)
+    z1 =x1**2+(2-y1)**2
+    
+    ax.plot_surface(x1, y1, z1, rstride=1, cstride=1, cmap='rainbow')
+    
+    plt.show()
+
+def quiver():
+    ax = plt.figure().add_subplot(projection='3d')
+
+    # Make the grid
+    # x, y, z = np.meshgrid(np.arange(-0.8, 1, 0.2),
+    #                     np.arange(-0.8, 1, 0.2),
+    #                     np.arange(-0.8, 1, 0.8))
+    # x,y,z = np.meshgrid([1,2,3],[4,5,6],[7,8,9])
+    # u = [1,2,3]
+    # v = [4,5,6]
+    # w = [7,8,9]
+    l = np.array([[1],[4],[7]])
+    x,y,z = np.meshgrid([l[0]],[l[1]],[l[2]])
+    u = [1]
+    v = [4]
+    w = [7]
+
+    print('x',x)
+    # Make the direction data for the arrows
+    # u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z)
+    # v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z)
+    # w = (np.sqrt(2.0 / 3.0) * np.cos(np.pi * x) * np.cos(np.pi * y) *
+    #     np.sin(np.pi * z))
+
+    ax.quiver(x, y, z, u, v, w, length=0.1, normalize=True)
+
+    plt.show()
+    
+def get():
+    m = np.eye(3)
+    focal = m[0:1,0:1]
+    print(focal)
+
+
+if __name__ == '__main__':
+    quiver()
\ No newline at end of file
diff --git a/utils/calib 2.py b/utils/calib 2.py
new file mode 100755
index 0000000..1c314cb
--- /dev/null
+++ b/utils/calib 2.py	
@@ -0,0 +1,169 @@
+import cv2
+import numpy as np
+import os
+import sys
+import glob
+import pickle
+import math
+from .utils import arse_config
+from .trans import read_pic, read_pic
+
+
+objpoints=[]
+imgpoints=[]
+
+
+def draw(c2w,objp):
+    print('objp',objp)
+    M2  = np.trunc(10*c2w)
+    print('M2',M2)
+    ################相机内参数矩阵################
+    f=8
+    dx=0.01
+    dy=0.01
+    u0=640
+    v0=480
+    list1=[f/dx,0,u0,0,0,f/dy,v0,0,0,0,1,0]
+    M1=np.mat(list1).reshape(3,4)
+    ########################创建空白图像############################################
+    img= np.zeros((240*2,320*2,3), np.uint8) 
+    img.fill(255)
+    ##########对每个点进行透视运算，将世界坐标转换为像素坐标，并将其标记在空白图像中##############
+    corner_out=np.zeros((10*10,2),np.float32)
+    print(corner_out[0])
+    k=0
+    sigma=0.12
+    print('M1',M1)
+    for l in objp:
+        
+        l=np.append(l,1)
+        l=np.mat(l)
+
+        out=(M1*M2*l.T)/((M2*l.T)[2,0])
+        corner_out[k][0]=float(out[0,0])
+        corner_out[k][1]=float(out[1,0])
+    
+        cv2.circle(img,(corner_out[k][0],corner_out[k][1]),1,(0,0,255),4)
+        k+=1
+
+    np.save('corner.npy',corner_out)
+    cv2.imwrite('output.jpg',img)
+    cv2.imshow('image', img)  
+    cv2.waitKey(0)  
+    cv2.destroyAllWindows()  
+
+    # corner = tuple(corners[0].ravel())
+    # img = cv2.line(img, corner, tuple(imgpts[0].ravel()), (255, 0, 0), 5)
+    # img = cv2.line(img, corner, tuple(imgpts[1].ravel()), (0, 255, 0), 5)
+    # img = cv2.line(img, corner, tuple(imgpts[2].ravel()), (0, 0, 255), 5)
+    # return img
+
+
+# 获得像素坐标
+def calibration_photo(rvecs,tvecs,w1,h1):
+    tvecs = np.array(tvecs).reshape(3,1)
+    rvecs = np.array(rvecs).reshape(3,1)
+    objp = np.zeros((w1*h1,3), np.float32)
+    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
+    objp = objp*18.1  # 18.1 mm
+
+    list1 = rvecs
+    in_site=np.mat(list1)
+    in_rr=in_site/180*math.pi
+
+    print('tvecs',tvecs)
+    print('rvecs',rvecs)
+
+    #旋转向量转化为旋转矩阵
+    in_r=cv2.Rodrigues(in_rr,jacobian=0)[0]
+
+    #获得外参数矩阵
+    list2 = tvecs
+    list2 = np.vstack((in_r,list2.reshape(1,3)))
+
+    #列合并
+    yi=np.mat([0,0,0,1])
+    c2w_metrix=np.hstack((list2,yi.T))
+    # print('c2w_metrix:',c2w_metrix)
+   
+    return(c2w_metrix,objp)
+
+# # 计算位姿
+# def calibration(ret,mtx,dist,rvecs,tvecs,corners,w1,h1):
+#     tvecs = np.array(tvecs).reshape(3,1)
+#     rvecs = np.array(rvecs).reshape(3,1)
+#     objp = np.zeros((w1*h1,3), np.float32)
+#     objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
+#     objp = objp*18.1  # 18.1 mm
+
+#     list1 = rvecs
+#     in_site=np.mat(list1)
+#     in_rr=in_site/180*math.pi
+
+#     # 找到图像平面点角点坐标
+    
+#     ret =True
+
+#     if ret:
+#         _,R,T=cv2.solvePnP(objp,corners,mtx,dist)
+#         print('所求结果：')
+#         print("旋转向量",R)
+#         print("平移向量",T)
+
+
+
+def get_inner_mtx(photo_path,w1,h1):
+    # number of coners
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # 阈值
+    objp = np.zeros((w1*h1,3), np.float32)
+    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
+    objp = objp*18.1  # 18.1 mm
+    img = cv2.imread(photo_path)
+    # print(img.shape)
+    #获取画面中心点
+    #图像的长宽(480, 640, 3)
+    print()
+    h, w = img.shape[0], img.shape[1]
+    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+    u, v = img.shape[:2]
+    # 找到棋盘格角点
+    ret, corners = cv2.findChessboardCorners(gray, (w1,h1), None)
+    # 如果找到足够点对，将其存储起来
+    if ret == True:
+        objpoints.append(objp)
+        imgpoints.append(corners)
+
+    #标定
+    ret, mtx, dist, rvecs, tvecs = \
+        cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
+    return(ret,mtx,dist,rvecs,tvecs,corners)
+
+
+def set_calibration():
+    args = arse_config()
+    read_pic(args.folder)
+    root = os.path.join(args.folder,'*.png') # 标定图像保存路径
+    photos_path = glob.glob(root)
+    w1 = args.config[0]
+    h1 = args.config[1] 
+    c2w_metrix = []
+    print('calibrating')
+    for photo_path in photos_path:
+        # mtx相机内参，dist相机畸变
+        ret,mtx,dist,rvecs,tvecs,corners = get_inner_mtx(photo_path,w1,h1)
+        c2w,objp = calibration_photo(rvecs[-1],tvecs[-1],w1,h1)
+        draw(c2w,objp)
+        c2w_metrix.append(c2w)
+        
+
+
+
+
+
+    # os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
+    # f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
+    # log = {'c2w_metrix':c2w_metrix,'ret':ret,'rvecs':rvecs}
+    # with open(f, 'wb') as file:
+    #     pickle.dump(log, file)
+    # print('finish calibrating')
+
diff --git a/utils/calib 3 blender.py b/utils/calib 3 blender.py
new file mode 100755
index 0000000..6351ef4
--- /dev/null
+++ b/utils/calib 3 blender.py	
@@ -0,0 +1,217 @@
+import cv2
+import numpy as np
+import os
+import sys
+import glob
+import pickle
+# import math
+from .utils import arse_config
+from .trans import read_pic, read_pic
+
+import numpy as np
+import matplotlib as mpl
+from matplotlib import cm
+from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+
+objpoints=[]
+imgpoints=[]
+
+def show(c2w,cp):
+    # 创建画布
+    fig = plt.figure(figsize=(5, 5),
+                    facecolor='whitesmoke'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='whitesmoke',
+                projection='3d' 
+                )
+    # -------------------------------- 绘制 3D 图形 --------------------------------
+    # # 二元函数定义域平面
+    # x = np.linspace(0, 9, 9)
+    # y = np.linspace(0, 9, 9)
+    # X, Y = np.meshgrid(x, y)
+    # # 平面 z=4.5 的部分
+    # ax.plot_surface(X,
+    #             Y,
+    #             Z=X*0+4.5,
+    #             color='g',
+    #             alpha=0.6
+    #            ) 
+    # # 散点图
+    dots = np.array(cp).astype(int) # list,在画图前必须转为int类型
+    print(type(cp))
+    print(dots[0],dots[1],sep=';')
+    print(dots.shape[0])
+    for i in range(dots.shape[0]):
+        dot = dots[i]
+        print('dot:',dot)
+        print('i:',i)
+
+        ax.scatter(dot[0],dot[1],dot[2],c='g', marker='o')
+        ax.text(dot[0][0],dot[1][0],dot[2][0],i,fontsize=12, color = "r", style = "italic")
+    # --------------------------------  --------------------------------
+    # 设置坐标轴标题和刻度
+    ax.set(xlabel='X',
+        ylabel='Y',
+        zlabel='Z',
+        xlim=(0, 9),
+        ylim=(0, 9),
+        zlim=(0, 9),
+        xticks=np.arange(-400, 400, 80),
+        yticks=np.arange(-400, 400, 80),
+        zticks=np.arange(-800, 800, 160)
+        )
+
+    # 调整视角
+    ax.view_init(elev=15,    # 仰角
+                azim=60   # 方位角
+                )
+
+    # 显示图形
+    plt.show()
+
+def showsurface(c2w,cp):
+    fig = plt.figure(figsize=(5, 5),
+                    facecolor='whitesmoke'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='whitesmoke',
+                projection='3d' 
+                )
+
+    # x1 = np.linspace(0,2,40)
+    # y1 = np.linspace(0,2,40)
+    x = c2w[0]
+    X, Y = np.meshgrid(x, y)
+    z1 =X*2,
+    
+    ax.plot_surface(x1, y1, z1, rstride=1, cstride=1, cmap='rainbow')
+    
+    plt.show()
+ 
+
+def draw(img, corners, imgpts):
+    # img  = np.trunc(10*img)
+    img = img.astype(int)
+    img = np.mat(img)
+    corners = corners.astype(int)
+    imgpts = imgpts.astype(int)
+    print(type(corners))
+    print(type(imgpts))
+    corner = tuple(corners[0].ravel())
+    img = cv2.line(img, corner, tuple(imgpts[0].ravel()), (255, 0, 0), 5)
+    img = cv2.line(img, corner, tuple(imgpts[1].ravel()), (0, 255, 0), 5)
+    img = cv2.line(img, corner, tuple(imgpts[2].ravel()), (0, 0, 255), 5)
+    return img
+
+
+
+
+# 获得像素坐标
+def calibration_photo(photo_path,mtx,dist):
+    # 设置要标定的角点个数
+    x_nums = 11  # x方向上的角点个数
+    y_nums = 8
+    # 设置(生成)标定图在世界坐标中的坐标
+    world_point = np.zeros((x_nums * y_nums, 3), np.float32)  # 生成x_nums*y_nums个坐标，每个坐标包含x,y,z三个元素
+    world_point[:, :2] = 15 * np.mgrid[:x_nums, :y_nums].T.reshape(-1, 2)  # mgrid[]生成包含两个二维矩阵的矩阵，每个矩阵都有x_nums列,y_nums行
+    # print('world point:',world_point)
+    # .T矩阵的转置
+    # reshape()重新规划矩阵，但不改变矩阵元素
+    # 设置世界坐标的坐标
+    axis = 15* np.float32([[3, 0, 0], [0, 3, 0], [0, 0, -3]]).reshape(-1, 3)
+    # 设置角点查找限制
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+
+    image = cv2.imread(photo_path)
+
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    # 查找角点
+    ok, corners = cv2.findChessboardCorners(gray, (x_nums, y_nums), )
+    # print(ok)
+    if ok:
+        # 获取更精确的角点位置
+        exact_corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
+
+        # 获取外参
+        _, rvec, tvec, inliers = cv2.solvePnPRansac(world_point, exact_corners, mtx, dist)
+        #获得的旋转矩阵是向量，是3×1的矩阵，想要还原回3×3的矩阵，需要罗德里格斯变换Rodrigues，
+        
+        rotation_m, _ = cv2.Rodrigues(rvec)#罗德里格斯变换
+        print(rotation_m)
+        print('旋转矩阵是：\n', rvec)
+        print('平移矩阵是:\n', tvec)
+        rotation_t = np.hstack([rotation_m,tvec])
+        rotation_t_Homogeneous_matrix = np.vstack([rotation_t,np.array([[0, 0, 0, 1]])])
+        print('w2c',rotation_t_Homogeneous_matrix)
+        imgpts, jac = cv2.projectPoints(axis, rvec, tvec, mtx, dist)
+        # # 可视化角点
+        # img = draw(image, corners, imgpts)
+        # cv2.imshow('img', img)
+        cameraPosition = -np.matrix(rotation_m).T * np.matrix(tvec)
+        print('cp',cameraPosition)
+        return rotation_t_Homogeneous_matrix,cameraPosition # 返回旋转矩阵和平移矩阵组成的齐次矩阵
+
+
+
+def get_inner_mtx(photo_path,w1,h1):
+    # number of coners
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # 阈值
+    objp = np.zeros((w1*h1,3), np.float32)
+    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
+    objp = objp*18.1  # 18.1 mm
+    img = cv2.imread(photo_path)
+    # print(img.shape)
+    #获取画面中心点
+    #图像的长宽(480, 640, 3)
+    h, w = img.shape[0], img.shape[1]
+    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+    u, v = img.shape[:2]
+    # 找到棋盘格角点
+    ret, corners = cv2.findChessboardCorners(gray, (w1,h1), None)
+    # 如果找到足够点对，将其存储起来
+    if ret == True:
+        objpoints.append(objp)
+        imgpoints.append(corners)
+
+    #标定
+    ret, mtx, dist, rvecs, tvecs = \
+        cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
+
+    print('mtx:',mtx)
+    focal = mtx[0:1,0:1]
+    print('focal:',focal)
+    return(ret,mtx,dist,rvecs,tvecs,corners,focal)
+
+
+def set_calibration():
+    
+    args = arse_config()
+    read_pic(args.folder)
+    root = os.path.join(args.folder,'*.png') # 标定图像保存路径
+    photos_path = glob.glob(root)
+    w1 = args.config[0]
+    h1 = args.config[1] 
+    c2w_metrix = []
+    cp_mertix = []
+    print('calibrating')
+    for photo_path in photos_path:
+        # ret相机？？ mtx相机内参，dist相机畸变,rvecs旋转向量，tvecs平移向量,focal焦距
+        ret,mtx,dist,rvecs,tvecs,corners,focal = get_inner_mtx(photo_path,w1,h1)        
+        c2w,cp = calibration_photo(photo_path,mtx,dist)
+        c2w_metrix.append(c2w)
+        cp_mertix.append(cp)
+    # show(c2w_metrix,cp_mertix)
+        
+
+
+
+    # os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
+    # f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
+    # log = {'c2w_metrix':c2w_metrix,'ret':ret,'rvecs':rvecs}
+    # with open(f, 'wb') as file:
+    #     pickle.dump(log, file)
+    # print('finish calibrating')
+
diff --git a/utils/calib present.py b/utils/calib present.py
new file mode 100755
index 0000000..16cc52c
--- /dev/null
+++ b/utils/calib present.py	
@@ -0,0 +1,254 @@
+import cv2
+import numpy as np
+import os
+import sys
+import glob
+import pickle
+# import math
+from .utils import arse_config
+from .trans import read_pic, read_pic
+
+import numpy as np
+import matplotlib as mpl
+from matplotlib import cm
+from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+
+objpoints=[]
+imgpoints=[]
+
+def show(c2w,cp):
+    # 创建画布
+    fig = plt.figure(figsize=(5, 5),
+                    facecolor='whitesmoke'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='whitesmoke',
+                projection='3d' 
+                )
+    # -------------------------------- 绘制 3D 图形 --------------------------------
+    # # 二元函数定义域平面
+    # x = np.linspace(0, 9, 9)
+    # y = np.linspace(0, 9, 9)
+    # X, Y = np.meshgrid(x, y)
+    # # 平面 z=4.5 的部分
+    # ax.plot_surface(X,
+    #             Y,
+    #             Z=X*0+4.5,
+    #             color='g',
+    #             alpha=0.6
+    #            ) 
+    # # 散点图
+    dots = np.array(c2w).astype(int) # list,在画图前必须转为int类型
+    print(dots.shape[0])
+    for i in range(dots.shape[0]):
+        dot = dots[i]
+        print('dot:',dot)
+        print('i:',i)
+        print('dot[0][0]:',dot[0][0])
+        print('dot[1]:',dot[1][0])
+
+
+        ax.scatter(dot[0],dot[1],dot[2],c='g', marker='o')
+        ax.text(dot[0][3],dot[1][3],dot[2][3],i,fontsize=12, color = "r", style = "italic")
+    # --------------------------------  --------------------------------
+    # 设置坐标轴标题和刻度
+    ax.set(xlabel='X',
+        ylabel='Y',
+        zlabel='Z',
+        xlim=(0, 9),
+        ylim=(0, 9),
+        zlim=(0, 9),
+        xticks=np.arange(-200, 400, 60),
+        yticks=np.arange(-200, 400, 60),
+        zticks=np.arange(-600, 0, 60)
+        )
+
+    # 调整视角
+    ax.view_init(elev=15,    # 仰角
+                azim=60   # 方位角
+                )
+
+    # 显示图形
+    plt.show()
+
+def showsurface(c2w,cp,rvecs,tvecs):
+    fig = plt.figure(figsize=(5, 5),
+                    facecolor='whitesmoke'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='whitesmoke',
+                projection='3d' 
+                )
+    c2w = np.array(c2w)
+    cp = np.array(cp)
+    print('shape c2w:',c2w.shape)
+    print('shape cp:',cp.shape)
+    print('c2w[0]:',c2w[0])
+    print('cp[0]:',cp[0])
+    for i in range(cp.shape[0]):
+        print('i:',i)
+        print('cp[i][0]:',cp[i][0])
+        print('cp.shape[0]:',cp.shape[0] )
+        x,y,z = np.meshgrid(list(cp[i][0]),list(cp[i][1]),list(cp[i][2]))
+        # ax.scatter(x,y,z,c='g', marker='o')
+
+        print('x:',x)
+        print('y:',y)
+        print('z:',z)
+        print('c2w[i]:',i)
+        # u = c2w[i][0]
+        # v = c2w[i][1]
+        # w = c2w[i][2]
+        # u, v, w = np.meshgrid(np.dot(cp[i],c2w[i]))
+        # u = np.dot(cp[i],c2w[i])[0]
+        # print('u:',u)
+        print('(np.dot(cp[i],c2w[i]):',(cp[i]*rvecs[i])+tvecs[i])
+        w2c = (cp[i]*rvecs[i])+tvecs[i]
+        u = w2c*cp[i][0][0]
+        v = w2c*cp[i][1][0]
+        w = w2c*cp[i][2][0]
+        print('w2c*cp[i]：',w2c*cp[i] )
+        print('type:',type(w2c*cp[i]))
+        print('w2c*cp[i][0][0]：',w2c*cp[i][0][0] )
+
+        # ax.scatter(u, v, w,c='g', marker='o')
+
+        ax.quiver(x, y, z, u, v, w, length=0.1, normalize=True)
+    
+    plt.show()
+ 
+
+
+
+# 获得像素坐标
+def calibration_photo(photo_path,mtx,dist):
+    # 设置要标定的角点个数
+    x_nums = 11  # x方向上的角点个数
+    y_nums = 8
+    # 设置(生成)标定图在世界坐标中的坐标
+    world_point = np.zeros((x_nums * y_nums, 3), np.float32)  # 生成x_nums*y_nums个坐标，每个坐标包含x,y,z三个元素
+    world_point[:, :2] = 15 * np.mgrid[:x_nums, :y_nums].T.reshape(-1, 2)  # mgrid[]生成包含两个二维矩阵的矩阵，每个矩阵都有x_nums列,y_nums行
+    # print('world point:',world_point)
+    # .T矩阵的转置
+    # reshape()重新规划矩阵，但不改变矩阵元素
+    # 设置世界坐标的坐标
+    axis = 15* np.float32([[3, 0, 0], [0, 3, 0], [0, 0, -3]]).reshape(-1, 3)
+    # 设置角点查找限制
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+
+    image = cv2.imread(photo_path)
+
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    # 查找角点
+    ok, corners = cv2.findChessboardCorners(gray, (x_nums, y_nums), )
+    # print(ok)
+    if ok:
+        # 获取更精确的角点位置
+        exact_corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
+
+        # 获取外参
+        _, rvec, tvec, inliers = cv2.solvePnPRansac(world_point, exact_corners, mtx, dist)
+        #获得的旋转矩阵是向量，是3×1的矩阵，想要还原回3×3的矩阵，需要罗德里格斯变换Rodrigues，
+        
+        rotation_m, _ = cv2.Rodrigues(rvec)#罗德里格斯变换
+        print(rotation_m)
+        print('旋转矩阵是：\n', rvec)
+        print('平移矩阵是:\n', tvec)
+        rotation_t = np.hstack([rotation_m,tvec])
+        rotation_t_Homogeneous_matrix = np.vstack([rotation_t,np.array([[0, 0, 0, 1]])])
+        print('w2c',rotation_t_Homogeneous_matrix)
+        imgpts, jac = cv2.projectPoints(axis, rvec, tvec, mtx, dist)
+        # # 可视化角点
+        # img = draw(image, corners, imgpts)
+        # cv2.imshow('img', img)
+        cameraPosition = -np.matrix(rotation_m).T * np.matrix(tvec)
+        # c2w = np.linalg.inv(rotation_t_Homogeneous_matrix)
+        c2w = np.linalg.inv(rotation_t_Homogeneous_matrix)
+        print('c2w',c2w)
+        print('cp',cameraPosition)
+        return c2w,cameraPosition # 返回旋转矩阵和平移矩阵组成的齐次矩阵
+
+def get_pose(c2w,cp,w1,h1,focal):
+    poses = c2w[:, :3, :4].transpose([1,2,0])
+    hwf = np.array([h1,w1,focal]).reshape([3,1])
+    poses = np.concatenate([poses, np.tile(hwf[..., np.newaxis], [1,1,poses.shape[-1]])], 1)
+    
+    # must switch to [-u, r, -t] from [r, -u, t], NOT [r, u, -t]
+    poses = np.concatenate([poses[:, 1:2, :], poses[:, 0:1, :], -poses[:, 2:3, :], poses[:, 3:4, :], poses[:, 4:5, :]], 1)
+    print('poses:',poses)
+    return poses
+
+
+def get_inner_mtx(photo_path,w1,h1):
+    # number of coners
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # 阈值
+    objp = np.zeros((w1*h1,3), np.float32)
+    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
+    objp = objp*18.1  # 18.1 mm
+    img = cv2.imread(photo_path)
+    # print(img.shape)
+    #获取画面中心点
+    #图像的长宽(480, 640, 3)
+    h, w = img.shape[0], img.shape[1]
+    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+    u, v = img.shape[:2]
+    # 找到棋盘格角点
+    ret, corners = cv2.findChessboardCorners(gray, (w1,h1), None)
+    # 如果找到足够点对，将其存储起来
+    if ret == True:
+        objpoints.append(objp)
+        imgpoints.append(corners)
+
+    #标定
+    ret, mtx, dist, rvecs, tvecs = \
+        cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
+
+    print('mtx:',mtx)
+    focal = mtx[0:1,0:1]
+    print('focal:',focal)
+    camera_angle_x = np.arctan((focal/.5*w1))/0.5
+    print('camera_angle_x:',camera_angle_x)
+    return(ret,mtx,dist,rvecs,tvecs,corners,focal,camera_angle_x)
+
+def save(c2w_metrix,camera_angle_x_metrix,rvecs):
+    os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
+    f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
+    log = {'c2w_metrix':c2w_metrix,'camera_angle_x':camera_angle_x_metrix,'rvecs':rvecs}
+    with open(f, 'wb') as file:
+        pickle.dump(log, file)
+    print('finish calibrating')
+
+
+def set_calibration():
+    
+    args = arse_config()
+    read_pic(args.folder)
+    root = os.path.join(args.folder,'*.png') # 标定图像保存路径
+    photos_path = glob.glob(root)
+    w1 = args.config[0]
+    h1 = args.config[1] 
+    c2w_metrix = []
+    cp_mertix = []
+    camera_angle_x_metrix = []
+    print('calibrating')
+    for photo_path in photos_path:
+        # ret相机？？ mtx相机内参，dist相机畸变,rvecs旋转向量，tvecs平移向量,focal焦距
+        ret,mtx,dist,rvecs,tvecs,corners,focal,camera_angle_x = get_inner_mtx(photo_path,w1,h1)        
+        c2w,cp = calibration_photo(photo_path,mtx,dist)
+        # pose = get_pose(c2w,cp,w1,h1,focal)
+        c2w_metrix.append(c2w)
+        cp_mertix.append(cp)
+        camera_angle_x_metrix.append(camera_angle_x)
+        # pose_metrix.append(pose)
+    showsurface(c2w_metrix,cp_mertix,rvecs,tvecs)
+    
+    print('camera_angle_x_metrix:',camera_angle_x_metrix)
+    
+    save(c2w_metrix,camera_angle_x_metrix,rvecs)
+
+
+
+
+
diff --git a/utils/calib.py b/utils/calib.py
index 5bd6bc4..1c79055 100755
--- a/utils/calib.py
+++ b/utils/calib.py
@@ -36,13 +36,19 @@ def calibration_photo(rvecs,tvecs,w1,h1):
     #旋转向量转化为旋转矩阵
     in_r=cv2.Rodrigues(in_rr,jacobian=0)[0]
 
-    #获得外参数矩阵
-    list2 = tvecs
-    list2 = np.vstack((in_r,list2.reshape(1,3)))
-
-    #列合并
-    yi=np.mat([0,0,0,1])
-    c2w_metrix=np.hstack((list2,yi.T))
+    # #获得外参数矩阵
+    # list2 = tvecs
+    # list2 = np.vstack((in_r,list2.reshape(1,3)))
+
+    # #列合并
+    # yi=np.mat([0,0,0,1])
+    # w2c_metrix=np.hstack((list2,yi.T))
+    R=in_r
+    t=tvecs
+    bottom = np.array([0,0,0,1.]).reshape([1,4])
+    w2c_metrix=np.concatenate([np.concatenate([R, t], 1), bottom], 0)
+    print('w2c_metrix:',w2c_metrix)
+    c2w_metrix = np.linalg.inv(w2c_metrix)
     print('c2w_metrix:',c2w_metrix)
     return(c2w_metrix)
 
@@ -94,7 +100,7 @@ def get_inner_mtx(photo_path,w1,h1):
     #标定
     ret, mtx, dist, rvecs, tvecs = \
         cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
-    return(ret,mtx,dist,rvecs,tvecs,corners)
+    return(ret,mtx,dist,rvecs,tvecs,corners,h,w)
 
 
 def set_calibration():
@@ -108,15 +114,17 @@ def set_calibration():
     print('calibrating')
     for photo_path in photos_path:
         # mtx相机内参，dist相机畸变
-        ret,mtx,dist,rvecs,tvecs,corners = get_inner_mtx(photo_path,w1,h1)
+        ret,mtx,dist,rvecs,tvecs,corners,h,w= get_inner_mtx(photo_path,w1,h1)
         c2w = calibration_photo(rvecs[-1],tvecs[-1],w1,h1)
         # c2w = calibration(ret,mtx,dist,rvecs[-1],tvecs[-1],corners,w1,h1)
         c2w_metrix.append(c2w)
 
+
     os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
     f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
-    log = {'c2w_metrix':c2w_metrix,'ret':ret,'rvecs':rvecs}
-    with open(f, 'wb') as file:
-        pickle.dump(log, file)
-    print('finish calibrating')
+    log = {'c2w_metrix':c2w_metrix,'ret':ret,'rvecs':rvecs,'h:':h,'w:':w}
+    # with open(f, 'wb') as file:
+    #     pickle.dump(log, file)
+    # print('finish calibrating')
+    return c2w_metrix,h,w
 
diff --git a/utils/load_blender.py b/utils/load_blender.py
new file mode 100644
index 0000000..852a605
--- /dev/null
+++ b/utils/load_blender.py
@@ -0,0 +1,87 @@
+import os
+import tensorflow as tf
+import numpy as np
+import imageio 
+import json
+
+
+
+
+trans_t = lambda t : tf.convert_to_tensor([
+    [1,0,0,0],
+    [0,1,0,0],
+    [0,0,1,t],
+    [0,0,0,1],
+], dtype=tf.float32)
+
+rot_phi = lambda phi : tf.convert_to_tensor([
+    [1,0,0,0],
+    [0,tf.cos(phi),-tf.sin(phi),0],
+    [0,tf.sin(phi), tf.cos(phi),0],
+    [0,0,0,1],
+], dtype=tf.float32)
+
+rot_theta = lambda th : tf.convert_to_tensor([
+    [tf.cos(th),0,-tf.sin(th),0],
+    [0,1,0,0],
+    [tf.sin(th),0, tf.cos(th),0],
+    [0,0,0,1],
+], dtype=tf.float32)
+
+
+def pose_spherical(theta, phi, radius):
+    c2w = trans_t(radius)
+    c2w = rot_phi(phi/180.*np.pi) @ c2w
+    c2w = rot_theta(theta/180.*np.pi) @ c2w
+    c2w = np.array([[-1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]]) @ c2w
+    return c2w
+    
+
+
+def load_blender_data(basedir, half_res=False, testskip=1):
+    splits = ['train', 'val', 'test']
+    metas = {}
+    for s in splits:
+        with open(os.path.join(basedir, 'transforms_{}.json'.format(s)), 'r') as fp:
+            metas[s] = json.load(fp)
+
+    all_imgs = []
+    all_poses = []
+    counts = [0]
+    for s in splits:
+        meta = metas[s]
+        imgs = []
+        poses = []
+        if s=='train' or testskip==0:
+            skip = 1
+        else:
+            skip = testskip
+            
+        for frame in meta['frames'][::skip]:
+            fname = os.path.join(basedir, frame['file_path'] + '.png')
+            imgs.append(imageio.imread(fname))
+            poses.append(np.array(frame['transform_matrix']))
+        imgs = (np.array(imgs) / 255.).astype(np.float32) # keep all 4 channels (RGBA)
+        poses = np.array(poses).astype(np.float32)
+        counts.append(counts[-1] + imgs.shape[0])
+        all_imgs.append(imgs)
+        all_poses.append(poses)
+    
+    i_split = [np.arange(counts[i], counts[i+1]) for i in range(3)]
+    
+    imgs = np.concatenate(all_imgs, 0)
+    poses = np.concatenate(all_poses, 0)
+    
+    H, W = imgs[0].shape[:2]
+    camera_angle_x = float(meta['camera_angle_x'])
+    focal = .5 * W / np.tan(.5 * camera_angle_x)
+    
+    render_poses = tf.stack([pose_spherical(angle, -30.0, 4.0) for angle in np.linspace(-180,180,40+1)[:-1]],0)
+    
+    if half_res:
+        imgs = tf.image.resize_area(imgs, [400, 400]).numpy()
+        H = H//2
+        W = W//2
+        focal = focal/2.
+        
+    return imgs, poses, render_poses, [H, W, focal], i_split
\ No newline at end of file
diff --git a/utils/trans.py b/utils/trans.py
index 8663d4c..179635a 100755
--- a/utils/trans.py
+++ b/utils/trans.py
@@ -1,4 +1,5 @@
 import argparse
+from ctypes import sizeof
 import json
 import numpy as np
 import os
@@ -11,12 +12,45 @@
 def read_pic(root):
   # 重命名原图
   i=0
-  for item in os.listdir(root):
+  filenames=os.listdir(root)
+  print(filenames)
+  filenames.sort(key=lambda x:int(x[:-4]))
+  for item in filenames:
     pname = os.path.join(root,item)
     nname = os.path.join(root,'{:0>2d}'.format(i)+'.png')
     os.rename(pname,nname)
     i+=1
 
+def json_write_llff(c2w_metrix,h,w,f):
+  
+  print(type(c2w_metrix)) # list
+  print(c2w_metrix[0])
+  print(len(c2w_metrix))
+  f = f
+  hwf = np.array([h,w,f]).reshape(3,1)
+  # print("hwf:",hwf)
+  # print((hwf).shape)
+  c2w_metrix = np.array(c2w_metrix)
+  print((c2w_metrix).shape)
+  poses = c2w_metrix[:, :3, :4].transpose([1,2,0])
+  poses = np.concatenate([poses, np.tile(hwf[..., np.newaxis], [1,1,poses.shape[-1]])], 1)
+  poses = np.concatenate([poses[:, 1:2, :], poses[:, 0:1, :], -poses[:, 2:3, :], poses[:, 3:4, :], poses[:, 4:5, :]], 1)
+
+  ## 不计算深度
+  # poses = poses.transpose([2,0,1])
+  # poses = np.reshape(poses,(-1,15))
+
+  save_arr = []
+  for i in range(len(c2w_metrix)):
+      close_depth, inf_depth = 0,0
+      # print( i, close_depth, inf_depth )
+      save_arr.append(np.concatenate([poses[..., i].ravel(), np.array([close_depth, inf_depth])], 0))
+  save_arr = np.array(save_arr)
+  print(save_arr[0])
+
+  np.save(os.path.join(os.getcwd(),'poses_bounds.npy'), save_arr)
+  print(".npy has been written")
+
 
 def json_write():
   args = arse_config()
@@ -31,24 +65,25 @@ def json_write():
   for file in files:
     s = str(os.path.join(args.folder,os.path.splitext(file)[0]))
     name.append(s)
-  
 
   #读取
-  
   frames = []
   for i in range(len(name)):
     dic = {}
     dic['file_path'] = name[i]
     # rot = np.array(data.get('rvecs'))[i]
     # dic['rotation'] = rot.tolist()
-    np_s = np.array(data.get('c2w_metrix'))[i].T
+    np_s = np.array(data.get('c2w_metrix'))[i]
     dic['transform_matrix'] = np_s.tolist()
     # print(dic)
     frames.append(dic)
-  print(frames)
+  # print(frames)
+    print("c2w_metrix:",np_s)
     
-  ca = np.array(data.get('ret')).tolist()
-  s = {'camera_angle_x':ca,'frames':frames}
+  # ca = np.array(data.get('camera_angle_x')).tolist()[0]
+  # print('ca:',data.get('camera_angle_x')[0])
+  # s = {'camera_angle_x':float(ca[0][0]),'frames':frames}
+  s = {'frames':frames}
 
   js = json.dumps(s,indent=4)
   with open('./jsontrans.json', 'w') as  f:
diff --git a/utils/utils.py b/utils/utils.py
index 3d6ee0c..1dc9a03 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -3,7 +3,7 @@
 def arse_config():
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--folder', type=str, \
-        default='./chess/train',\
+        default='./chess/images',\
         help="图片所在文件位置")
     parser.add_argument('--config', type=format, default=[11,8],help="格子长宽数")
     args = parser.parse_args()

From 21ede1ea046dfd5496de3617916b8aed84c0ae2c Mon Sep 17 00:00:00 2001
From: HazelWang0 <hugowell@163.com>
Date: Sun, 20 Feb 2022 17:01:13 +0800
Subject: [PATCH 2/2] 3.0

---
 log/0216/poses_bounds.npy                | Bin 0 -> 3664 bytes
 log/0221_correct/poses_bounds.npy        | Bin 0 -> 3664 bytes
 poses_bounds.npy                         | Bin 3664 -> 3664 bytes
 readnpy.py                               |   2 +-
 run.py                                   |   6 +-
 utils/__init__.py                        |   2 +-
 utils/calib 2.py                         | 169 ------------
 utils/calib 3 blender.py                 | 217 ---------------
 utils/calib.py                           | 319 ++++++++++++++++-------
 utils/{calib present.py => calib0218.py} |  16 +-
 utils/calibNot.py                        | 191 ++++++++++++++
 utils/trans.py                           |   8 +-
 utils/utils.py                           |   3 +-
 13 files changed, 430 insertions(+), 503 deletions(-)
 create mode 100644 log/0216/poses_bounds.npy
 create mode 100644 log/0221_correct/poses_bounds.npy
 delete mode 100755 utils/calib 2.py
 delete mode 100755 utils/calib 3 blender.py
 mode change 100755 => 100644 utils/calib.py
 rename utils/{calib present.py => calib0218.py} (95%)
 create mode 100755 utils/calibNot.py

diff --git a/log/0216/poses_bounds.npy b/log/0216/poses_bounds.npy
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diff --git a/poses_bounds.npy b/poses_bounds.npy
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diff --git a/readnpy.py b/readnpy.py
index 5553818..a68395d 100644
--- a/readnpy.py
+++ b/readnpy.py
@@ -2,5 +2,5 @@
 
 # 读取.npy文件
 arr = np.load("/root/vmip/myProject/nerf/camera/nerfCalibration/poses_bounds.npy")
-print(arr[24])
+print(arr[0])
 print("load .npy done")
\ No newline at end of file
diff --git a/run.py b/run.py
index beef547..740d779 100644
--- a/run.py
+++ b/run.py
@@ -1,9 +1,9 @@
 from utils.trans import json_write,json_write_llff
-from utils.calib import set_calibration
+from utils.calib0218 import set_calibration
 
 
 if __name__ == '__main__':
     f = 612
-    c2w_metrix,h,w = set_calibration()
-    json_write_llff(c2w_metrix,h,w,f)
+    c2w_metrix = set_calibration()
+    json_write_llff(c2w_metrix,f)
     
diff --git a/utils/__init__.py b/utils/__init__.py
index c22461f..ce9737e 100644
--- a/utils/__init__.py
+++ b/utils/__init__.py
@@ -1,3 +1,3 @@
-from .calib import set_calibration
+
 from .utils import arse_config
 from .trans import json_write,read_pic
\ No newline at end of file
diff --git a/utils/calib 2.py b/utils/calib 2.py
deleted file mode 100755
index 1c314cb..0000000
--- a/utils/calib 2.py	
+++ /dev/null
@@ -1,169 +0,0 @@
-import cv2
-import numpy as np
-import os
-import sys
-import glob
-import pickle
-import math
-from .utils import arse_config
-from .trans import read_pic, read_pic
-
-
-objpoints=[]
-imgpoints=[]
-
-
-def draw(c2w,objp):
-    print('objp',objp)
-    M2  = np.trunc(10*c2w)
-    print('M2',M2)
-    ################相机内参数矩阵################
-    f=8
-    dx=0.01
-    dy=0.01
-    u0=640
-    v0=480
-    list1=[f/dx,0,u0,0,0,f/dy,v0,0,0,0,1,0]
-    M1=np.mat(list1).reshape(3,4)
-    ########################创建空白图像############################################
-    img= np.zeros((240*2,320*2,3), np.uint8) 
-    img.fill(255)
-    ##########对每个点进行透视运算，将世界坐标转换为像素坐标，并将其标记在空白图像中##############
-    corner_out=np.zeros((10*10,2),np.float32)
-    print(corner_out[0])
-    k=0
-    sigma=0.12
-    print('M1',M1)
-    for l in objp:
-        
-        l=np.append(l,1)
-        l=np.mat(l)
-
-        out=(M1*M2*l.T)/((M2*l.T)[2,0])
-        corner_out[k][0]=float(out[0,0])
-        corner_out[k][1]=float(out[1,0])
-    
-        cv2.circle(img,(corner_out[k][0],corner_out[k][1]),1,(0,0,255),4)
-        k+=1
-
-    np.save('corner.npy',corner_out)
-    cv2.imwrite('output.jpg',img)
-    cv2.imshow('image', img)  
-    cv2.waitKey(0)  
-    cv2.destroyAllWindows()  
-
-    # corner = tuple(corners[0].ravel())
-    # img = cv2.line(img, corner, tuple(imgpts[0].ravel()), (255, 0, 0), 5)
-    # img = cv2.line(img, corner, tuple(imgpts[1].ravel()), (0, 255, 0), 5)
-    # img = cv2.line(img, corner, tuple(imgpts[2].ravel()), (0, 0, 255), 5)
-    # return img
-
-
-# 获得像素坐标
-def calibration_photo(rvecs,tvecs,w1,h1):
-    tvecs = np.array(tvecs).reshape(3,1)
-    rvecs = np.array(rvecs).reshape(3,1)
-    objp = np.zeros((w1*h1,3), np.float32)
-    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
-    objp = objp*18.1  # 18.1 mm
-
-    list1 = rvecs
-    in_site=np.mat(list1)
-    in_rr=in_site/180*math.pi
-
-    print('tvecs',tvecs)
-    print('rvecs',rvecs)
-
-    #旋转向量转化为旋转矩阵
-    in_r=cv2.Rodrigues(in_rr,jacobian=0)[0]
-
-    #获得外参数矩阵
-    list2 = tvecs
-    list2 = np.vstack((in_r,list2.reshape(1,3)))
-
-    #列合并
-    yi=np.mat([0,0,0,1])
-    c2w_metrix=np.hstack((list2,yi.T))
-    # print('c2w_metrix:',c2w_metrix)
-   
-    return(c2w_metrix,objp)
-
-# # 计算位姿
-# def calibration(ret,mtx,dist,rvecs,tvecs,corners,w1,h1):
-#     tvecs = np.array(tvecs).reshape(3,1)
-#     rvecs = np.array(rvecs).reshape(3,1)
-#     objp = np.zeros((w1*h1,3), np.float32)
-#     objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
-#     objp = objp*18.1  # 18.1 mm
-
-#     list1 = rvecs
-#     in_site=np.mat(list1)
-#     in_rr=in_site/180*math.pi
-
-#     # 找到图像平面点角点坐标
-    
-#     ret =True
-
-#     if ret:
-#         _,R,T=cv2.solvePnP(objp,corners,mtx,dist)
-#         print('所求结果：')
-#         print("旋转向量",R)
-#         print("平移向量",T)
-
-
-
-def get_inner_mtx(photo_path,w1,h1):
-    # number of coners
-    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # 阈值
-    objp = np.zeros((w1*h1,3), np.float32)
-    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
-    objp = objp*18.1  # 18.1 mm
-    img = cv2.imread(photo_path)
-    # print(img.shape)
-    #获取画面中心点
-    #图像的长宽(480, 640, 3)
-    print()
-    h, w = img.shape[0], img.shape[1]
-    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-    u, v = img.shape[:2]
-    # 找到棋盘格角点
-    ret, corners = cv2.findChessboardCorners(gray, (w1,h1), None)
-    # 如果找到足够点对，将其存储起来
-    if ret == True:
-        objpoints.append(objp)
-        imgpoints.append(corners)
-
-    #标定
-    ret, mtx, dist, rvecs, tvecs = \
-        cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
-    return(ret,mtx,dist,rvecs,tvecs,corners)
-
-
-def set_calibration():
-    args = arse_config()
-    read_pic(args.folder)
-    root = os.path.join(args.folder,'*.png') # 标定图像保存路径
-    photos_path = glob.glob(root)
-    w1 = args.config[0]
-    h1 = args.config[1] 
-    c2w_metrix = []
-    print('calibrating')
-    for photo_path in photos_path:
-        # mtx相机内参，dist相机畸变
-        ret,mtx,dist,rvecs,tvecs,corners = get_inner_mtx(photo_path,w1,h1)
-        c2w,objp = calibration_photo(rvecs[-1],tvecs[-1],w1,h1)
-        draw(c2w,objp)
-        c2w_metrix.append(c2w)
-        
-
-
-
-
-
-    # os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
-    # f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
-    # log = {'c2w_metrix':c2w_metrix,'ret':ret,'rvecs':rvecs}
-    # with open(f, 'wb') as file:
-    #     pickle.dump(log, file)
-    # print('finish calibrating')
-
diff --git a/utils/calib 3 blender.py b/utils/calib 3 blender.py
deleted file mode 100755
index 6351ef4..0000000
--- a/utils/calib 3 blender.py	
+++ /dev/null
@@ -1,217 +0,0 @@
-import cv2
-import numpy as np
-import os
-import sys
-import glob
-import pickle
-# import math
-from .utils import arse_config
-from .trans import read_pic, read_pic
-
-import numpy as np
-import matplotlib as mpl
-from matplotlib import cm
-from matplotlib import pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-
-
-objpoints=[]
-imgpoints=[]
-
-def show(c2w,cp):
-    # 创建画布
-    fig = plt.figure(figsize=(5, 5),
-                    facecolor='whitesmoke'
-                    )
-    # 创建 3D 坐标系
-    ax = fig.gca(fc='whitesmoke',
-                projection='3d' 
-                )
-    # -------------------------------- 绘制 3D 图形 --------------------------------
-    # # 二元函数定义域平面
-    # x = np.linspace(0, 9, 9)
-    # y = np.linspace(0, 9, 9)
-    # X, Y = np.meshgrid(x, y)
-    # # 平面 z=4.5 的部分
-    # ax.plot_surface(X,
-    #             Y,
-    #             Z=X*0+4.5,
-    #             color='g',
-    #             alpha=0.6
-    #            ) 
-    # # 散点图
-    dots = np.array(cp).astype(int) # list,在画图前必须转为int类型
-    print(type(cp))
-    print(dots[0],dots[1],sep=';')
-    print(dots.shape[0])
-    for i in range(dots.shape[0]):
-        dot = dots[i]
-        print('dot:',dot)
-        print('i:',i)
-
-        ax.scatter(dot[0],dot[1],dot[2],c='g', marker='o')
-        ax.text(dot[0][0],dot[1][0],dot[2][0],i,fontsize=12, color = "r", style = "italic")
-    # --------------------------------  --------------------------------
-    # 设置坐标轴标题和刻度
-    ax.set(xlabel='X',
-        ylabel='Y',
-        zlabel='Z',
-        xlim=(0, 9),
-        ylim=(0, 9),
-        zlim=(0, 9),
-        xticks=np.arange(-400, 400, 80),
-        yticks=np.arange(-400, 400, 80),
-        zticks=np.arange(-800, 800, 160)
-        )
-
-    # 调整视角
-    ax.view_init(elev=15,    # 仰角
-                azim=60   # 方位角
-                )
-
-    # 显示图形
-    plt.show()
-
-def showsurface(c2w,cp):
-    fig = plt.figure(figsize=(5, 5),
-                    facecolor='whitesmoke'
-                    )
-    # 创建 3D 坐标系
-    ax = fig.gca(fc='whitesmoke',
-                projection='3d' 
-                )
-
-    # x1 = np.linspace(0,2,40)
-    # y1 = np.linspace(0,2,40)
-    x = c2w[0]
-    X, Y = np.meshgrid(x, y)
-    z1 =X*2,
-    
-    ax.plot_surface(x1, y1, z1, rstride=1, cstride=1, cmap='rainbow')
-    
-    plt.show()
- 
-
-def draw(img, corners, imgpts):
-    # img  = np.trunc(10*img)
-    img = img.astype(int)
-    img = np.mat(img)
-    corners = corners.astype(int)
-    imgpts = imgpts.astype(int)
-    print(type(corners))
-    print(type(imgpts))
-    corner = tuple(corners[0].ravel())
-    img = cv2.line(img, corner, tuple(imgpts[0].ravel()), (255, 0, 0), 5)
-    img = cv2.line(img, corner, tuple(imgpts[1].ravel()), (0, 255, 0), 5)
-    img = cv2.line(img, corner, tuple(imgpts[2].ravel()), (0, 0, 255), 5)
-    return img
-
-
-
-
-# 获得像素坐标
-def calibration_photo(photo_path,mtx,dist):
-    # 设置要标定的角点个数
-    x_nums = 11  # x方向上的角点个数
-    y_nums = 8
-    # 设置(生成)标定图在世界坐标中的坐标
-    world_point = np.zeros((x_nums * y_nums, 3), np.float32)  # 生成x_nums*y_nums个坐标，每个坐标包含x,y,z三个元素
-    world_point[:, :2] = 15 * np.mgrid[:x_nums, :y_nums].T.reshape(-1, 2)  # mgrid[]生成包含两个二维矩阵的矩阵，每个矩阵都有x_nums列,y_nums行
-    # print('world point:',world_point)
-    # .T矩阵的转置
-    # reshape()重新规划矩阵，但不改变矩阵元素
-    # 设置世界坐标的坐标
-    axis = 15* np.float32([[3, 0, 0], [0, 3, 0], [0, 0, -3]]).reshape(-1, 3)
-    # 设置角点查找限制
-    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
-
-    image = cv2.imread(photo_path)
-
-    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-    # 查找角点
-    ok, corners = cv2.findChessboardCorners(gray, (x_nums, y_nums), )
-    # print(ok)
-    if ok:
-        # 获取更精确的角点位置
-        exact_corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
-
-        # 获取外参
-        _, rvec, tvec, inliers = cv2.solvePnPRansac(world_point, exact_corners, mtx, dist)
-        #获得的旋转矩阵是向量，是3×1的矩阵，想要还原回3×3的矩阵，需要罗德里格斯变换Rodrigues，
-        
-        rotation_m, _ = cv2.Rodrigues(rvec)#罗德里格斯变换
-        print(rotation_m)
-        print('旋转矩阵是：\n', rvec)
-        print('平移矩阵是:\n', tvec)
-        rotation_t = np.hstack([rotation_m,tvec])
-        rotation_t_Homogeneous_matrix = np.vstack([rotation_t,np.array([[0, 0, 0, 1]])])
-        print('w2c',rotation_t_Homogeneous_matrix)
-        imgpts, jac = cv2.projectPoints(axis, rvec, tvec, mtx, dist)
-        # # 可视化角点
-        # img = draw(image, corners, imgpts)
-        # cv2.imshow('img', img)
-        cameraPosition = -np.matrix(rotation_m).T * np.matrix(tvec)
-        print('cp',cameraPosition)
-        return rotation_t_Homogeneous_matrix,cameraPosition # 返回旋转矩阵和平移矩阵组成的齐次矩阵
-
-
-
-def get_inner_mtx(photo_path,w1,h1):
-    # number of coners
-    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # 阈值
-    objp = np.zeros((w1*h1,3), np.float32)
-    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
-    objp = objp*18.1  # 18.1 mm
-    img = cv2.imread(photo_path)
-    # print(img.shape)
-    #获取画面中心点
-    #图像的长宽(480, 640, 3)
-    h, w = img.shape[0], img.shape[1]
-    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-    u, v = img.shape[:2]
-    # 找到棋盘格角点
-    ret, corners = cv2.findChessboardCorners(gray, (w1,h1), None)
-    # 如果找到足够点对，将其存储起来
-    if ret == True:
-        objpoints.append(objp)
-        imgpoints.append(corners)
-
-    #标定
-    ret, mtx, dist, rvecs, tvecs = \
-        cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
-
-    print('mtx:',mtx)
-    focal = mtx[0:1,0:1]
-    print('focal:',focal)
-    return(ret,mtx,dist,rvecs,tvecs,corners,focal)
-
-
-def set_calibration():
-    
-    args = arse_config()
-    read_pic(args.folder)
-    root = os.path.join(args.folder,'*.png') # 标定图像保存路径
-    photos_path = glob.glob(root)
-    w1 = args.config[0]
-    h1 = args.config[1] 
-    c2w_metrix = []
-    cp_mertix = []
-    print('calibrating')
-    for photo_path in photos_path:
-        # ret相机？？ mtx相机内参，dist相机畸变,rvecs旋转向量，tvecs平移向量,focal焦距
-        ret,mtx,dist,rvecs,tvecs,corners,focal = get_inner_mtx(photo_path,w1,h1)        
-        c2w,cp = calibration_photo(photo_path,mtx,dist)
-        c2w_metrix.append(c2w)
-        cp_mertix.append(cp)
-    # show(c2w_metrix,cp_mertix)
-        
-
-
-
-    # os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
-    # f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
-    # log = {'c2w_metrix':c2w_metrix,'ret':ret,'rvecs':rvecs}
-    # with open(f, 'wb') as file:
-    #     pickle.dump(log, file)
-    # print('finish calibrating')
-
diff --git a/utils/calib.py b/utils/calib.py
old mode 100755
new mode 100644
index 1c79055..ac6fb77
--- a/utils/calib.py
+++ b/utils/calib.py
@@ -4,127 +4,244 @@
 import sys
 import glob
 import pickle
-import math
+# import math
 from .utils import arse_config
 from .trans import read_pic, read_pic
 
+import numpy as np
+import matplotlib as mpl
+from matplotlib import cm
+from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
 
 objpoints=[]
 imgpoints=[]
 
+def show(c2w,cp):
+    # 创建画布
+    fig = plt.figure(figsize=(5, 5),
+                    facecolor='whitesmoke'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='whitesmoke',
+                projection='3d' 
+                )
+    # -------------------------------- 绘制 3D 图形 --------------------------------
+    # # 二元函数定义域平面
+    # x = np.linspace(0, 9, 9)
+    # y = np.linspace(0, 9, 9)
+    # X, Y = np.meshgrid(x, y)
+    # # 平面 z=4.5 的部分
+    # ax.plot_surface(X,
+    #             Y,
+    #             Z=X*0+4.5,
+    #             color='g',
+    #             alpha=0.6
+    #            ) 
+    # # 散点图
+    dots = np.array(c2w).astype(int) # list,在画图前必须转为int类型
+    print(dots.shape[0])
+    for i in range(dots.shape[0]):
+        dot = dots[i]
+        print('dot:',dot)
+        print('i:',i)
+        print('dot[0][0]:',dot[0][0])
+        print('dot[1]:',dot[1][0])
+
+
+        ax.scatter(dot[0],dot[1],dot[2],c='g', marker='o')
+        ax.text(dot[0][3],dot[1][3],dot[2][3],i,fontsize=12, color = "r", style = "italic")
+    # --------------------------------  --------------------------------
+    # 设置坐标轴标题和刻度
+    ax.set(xlabel='X',
+        ylabel='Y',
+        zlabel='Z',
+        xlim=(0, 9),
+        ylim=(0, 9),
+        zlim=(0, 9),
+        xticks=np.arange(-200, 400, 60),
+        yticks=np.arange(-200, 400, 60),
+        zticks=np.arange(-600, 0, 60)
+        )
+
+    # 调整视角
+    ax.view_init(elev=15,    # 仰角
+                azim=60   # 方位角
+                )
+
+    # 显示图形
+    plt.show()
+
+def showsurface(c2w,cp,rvecs,tvecs):
+    fig = plt.figure(figsize=(5, 5),
+                    facecolor='whitesmoke'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='whitesmoke',
+                projection='3d' 
+                )
+    c2w = np.array(c2w)
+    cp = np.array(cp)
+    print('shape c2w:',c2w.shape)
+    print('shape cp:',cp.shape)
+    print('c2w[0]:',c2w[0])
+    print('cp[0]:',cp[0])
+    for i in range(cp.shape[0]):
+        print('i:',i)
+        print('cp[i][0]:',cp[i][0])
+        print('cp.shape[0]:',cp.shape[0] )
+        x,y,z = np.meshgrid(list(cp[i][0]),list(cp[i][1]),list(cp[i][2]))
+        # ax.scatter(x,y,z,c='g', marker='o')
+
+        print('x:',x)
+        print('y:',y)
+        print('z:',z)
+        print('c2w[i]:',i)
+        # u = c2w[i][0]
+        # v = c2w[i][1]
+        # w = c2w[i][2]
+        # u, v, w = np.meshgrid(np.dot(cp[i],c2w[i]))
+        # u = np.dot(cp[i],c2w[i])[0]
+        # print('u:',u)
+        print('(np.dot(cp[i],c2w[i]):',(cp[i]*rvecs[i])+tvecs[i])
+        w2c = (cp[i]*rvecs[i])+tvecs[i]
+        u = w2c*cp[i][0][0]
+        v = w2c*cp[i][1][0]
+        w = w2c*cp[i][2][0]
+        print('w2c*cp[i]：',w2c*cp[i] )
+        print('type:',type(w2c*cp[i]))
+        print('w2c*cp[i][0][0]：',w2c*cp[i][0][0] )
+
+        # ax.scatter(u, v, w,c='g', marker='o')
+
+        ax.quiver(x, y, z, u, v, w, length=0.1, normalize=True)
+    
+    plt.show()
+ 
 
-def draw(img, corners, imgpts):
-    corner = tuple(corners[0].ravel())
-    img = cv2.line(img, corner, tuple(imgpts[0].ravel()), (255, 0, 0), 5)
-    img = cv2.line(img, corner, tuple(imgpts[1].ravel()), (0, 255, 0), 5)
-    img = cv2.line(img, corner, tuple(imgpts[2].ravel()), (0, 0, 255), 5)
-    return img
 
 
 # 获得像素坐标
-def calibration_photo(rvecs,tvecs,w1,h1):
-    tvecs = np.array(tvecs).reshape(3,1)
-    rvecs = np.array(rvecs).reshape(3,1)
-    objp = np.zeros((w1*h1,3), np.float32)
-    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
-    objp = objp*18.1  # 18.1 mm
-
-    list1 = rvecs
-    in_site=np.mat(list1)
-    in_rr=in_site/180*math.pi
-
-    #旋转向量转化为旋转矩阵
-    in_r=cv2.Rodrigues(in_rr,jacobian=0)[0]
-
-    # #获得外参数矩阵
-    # list2 = tvecs
-    # list2 = np.vstack((in_r,list2.reshape(1,3)))
-
-    # #列合并
-    # yi=np.mat([0,0,0,1])
-    # w2c_metrix=np.hstack((list2,yi.T))
-    R=in_r
-    t=tvecs
-    bottom = np.array([0,0,0,1.]).reshape([1,4])
-    w2c_metrix=np.concatenate([np.concatenate([R, t], 1), bottom], 0)
-    print('w2c_metrix:',w2c_metrix)
-    c2w_metrix = np.linalg.inv(w2c_metrix)
-    print('c2w_metrix:',c2w_metrix)
-    return(c2w_metrix)
-
-# # 计算位姿
-# def calibration(ret,mtx,dist,rvecs,tvecs,corners,w1,h1):
-#     tvecs = np.array(tvecs).reshape(3,1)
-#     rvecs = np.array(rvecs).reshape(3,1)
-#     objp = np.zeros((w1*h1,3), np.float32)
-#     objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
-#     objp = objp*18.1  # 18.1 mm
-
-#     list1 = rvecs
-#     in_site=np.mat(list1)
-#     in_rr=in_site/180*math.pi
-
-#     # 找到图像平面点角点坐标
+def calibration_photo(inter_corner_shape, size_per_grid, img_dir,img_type):
+        # 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 fname in images:
+        img = cv2.imread(fname)
+        gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        print("draw")
+        # 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)
+            cv2.waitKey(1)
+    cv2.destroyAllWindows()
+    # calibrate the camera,cv2.calibrateCamera() renturn the calibrate results
+    ret, mat_inter, coff_dis, v_rot, v_trans = cv2.calibrateCamera(obj_points, img_points, gray_img.shape[::-1], None, None)
+    # uv is image.shape[:2]
+        # calculate the error of reproject
+
+    # newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mat_inter, coff_dis,(480,640), 0, (480,640))
+    total_error = 0
+    for i in range(len(obj_points)):
+        img_points_repro, _ = cv2.projectPoints(obj_points[i], v_rot[i], v_trans[i], mat_inter, coff_dis)
+        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))
+
+    v_rot = np.array(v_rot)
+    v_trans = np.array(v_trans)
+    return v_rot,v_trans
+
+
+
+def get_c2w(v_rot,v_trans):
+    # print('shape v_rot:',v_rot[0].shape)
+    rotation_m, _ = cv2.Rodrigues(v_rot) #罗德里格斯变换
+    # print("rotation_matrix:",rotation_m)
+    rotation_t = np.hstack([rotation_m,v_trans])
+    rotation_t_Homogeneous_matrix = np.vstack([rotation_t,np.array([[0, 0, 0, 1]])])
+    print('w2c:',rotation_t_Homogeneous_matrix)
+    c2w = np.linalg.inv(rotation_t_Homogeneous_matrix)
+    print('c2w:',c2w)
+
+
+    v_rot = np.array(v_rot,dtype = float)
+    v_trans = np.array(v_trans,dtype = float)
+    
+    return c2w
+
+
+def get_pose(c2w,w1,h1,focal):
+    poses = c2w[:, :3, :4].transpose([1,2,0])
+    hwf = np.array([h1,w1,focal]).reshape([3,1])
+    poses = np.concatenate([poses, np.tile(hwf[..., np.newaxis], [1,1,poses.shape[-1]])], 1)
     
-#     ret =True
-
-#     if ret:
-#         _,R,T=cv2.solvePnP(objp,corners,mtx,dist)
-#         print('所求结果：')
-#         print("旋转向量",R)
-#         print("平移向量",T)
-
-
-
-def get_inner_mtx(photo_path,w1,h1):
-    # number of coners
-    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # 阈值
-    objp = np.zeros((w1*h1,3), np.float32)
-    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
-    objp = objp*18.1  # 18.1 mm
-    img = cv2.imread(photo_path)
-    # print(img.shape)
-    #获取画面中心点
-    #图像的长宽(480, 640, 3)
-    print()
-    h, w = img.shape[0], img.shape[1]
-    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-    u, v = img.shape[:2]
-    # 找到棋盘格角点
-    ret, corners = cv2.findChessboardCorners(gray, (w1,h1), None)
-    # 如果找到足够点对，将其存储起来
-    if ret == True:
-        objpoints.append(objp)
-        imgpoints.append(corners)
-
-    #标定
-    ret, mtx, dist, rvecs, tvecs = \
-        cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
-    return(ret,mtx,dist,rvecs,tvecs,corners,h,w)
+    # must switch to [-u, r, -t] from [r, -u, t], NOT [r, u, -t]
+    poses = np.concatenate([poses[:, 1:2, :], poses[:, 0:1, :], -poses[:, 2:3, :], poses[:, 3:4, :], poses[:, 4:5, :]], 1)
+    print('poses:',poses)
+    return poses
+
+
+def saveBlender(c2w_metrix,camera_angle_x_metrix,rvecs):
+    os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
+    f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
+    log = {'c2w_metrix':c2w_metrix,'camera_angle_x':camera_angle_x_metrix,'rvecs':rvecs}
+    with open(f, 'wb') as file:
+        pickle.dump(log, file)
+    print('finish calibrating')
 
 
 def set_calibration():
+    
     args = arse_config()
     read_pic(args.folder)
-    root = os.path.join(args.folder,'*.png') # 标定图像保存路径
-    photos_path = glob.glob(root)
-    w1 = args.config[0]
-    h1 = args.config[1] 
+    root = args.folder # 标定图像保存路径
+    size_per_grid = args.size_per_grid
+    inter_corner_shape = args.inter_corner_shape
+    photos_path = glob.glob(os.path.join(root,'*.png'))
     c2w_metrix = []
+    camera_angle_x_metrix = []
     print('calibrating')
-    for photo_path in photos_path:
-        # mtx相机内参，dist相机畸变
-        ret,mtx,dist,rvecs,tvecs,corners,h,w= get_inner_mtx(photo_path,w1,h1)
-        c2w = calibration_photo(rvecs[-1],tvecs[-1],w1,h1)
-        # c2w = calibration(ret,mtx,dist,rvecs[-1],tvecs[-1],corners,w1,h1)
+    v_rot,v_trans= calibration_photo(inter_corner_shape, size_per_grid, root,'png')
+    print('shape rot:',v_rot.shape)
+    print('trans rot:',v_trans.shape)
+
+    print('photo_path',photos_path)
+    print('len(photos_path)',len(photos_path))
+    for i in range(len(photos_path)):
+        print('i:',i)
+        # ret相机？？ mtx相机内参，dist相机畸变,rvecs旋转向量，tvecs平移向量,focal焦距
+        c2w = get_c2w(v_rot[i],v_trans[i])
+        # pose = get_pose(c2w,cp,w1,h1,focal)
         c2w_metrix.append(c2w)
+        # pose_metrix.append(pose)
+    # showsurface(c2w_metrix,cp_mertix,rvecs,tvecs)
+    
+    print('camera_angle_x_metrix:',camera_angle_x_metrix)
+
+    return c2w_metrix
+    
+    # saveBlender(c2w_metrix,camera_angle_x_metrix,rvecs)
+
+
 
 
-    os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
-    f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
-    log = {'c2w_metrix':c2w_metrix,'ret':ret,'rvecs':rvecs,'h:':h,'w:':w}
-    # with open(f, 'wb') as file:
-    #     pickle.dump(log, file)
-    # print('finish calibrating')
-    return c2w_metrix,h,w
 
diff --git a/utils/calib present.py b/utils/calib0218.py
similarity index 95%
rename from utils/calib present.py
rename to utils/calib0218.py
index 16cc52c..4566822 100755
--- a/utils/calib present.py	
+++ b/utils/calib0218.py
@@ -153,7 +153,7 @@ def calibration_photo(photo_path,mtx,dist):
         #获得的旋转矩阵是向量，是3×1的矩阵，想要还原回3×3的矩阵，需要罗德里格斯变换Rodrigues，
         
         rotation_m, _ = cv2.Rodrigues(rvec)#罗德里格斯变换
-        print(rotation_m)
+        print("rotation_matrix:",rotation_m)
         print('旋转矩阵是：\n', rvec)
         print('平移矩阵是:\n', tvec)
         rotation_t = np.hstack([rotation_m,tvec])
@@ -170,7 +170,7 @@ def calibration_photo(photo_path,mtx,dist):
         print('cp',cameraPosition)
         return c2w,cameraPosition # 返回旋转矩阵和平移矩阵组成的齐次矩阵
 
-def get_pose(c2w,cp,w1,h1,focal):
+def get_pose(c2w,w1,h1,focal):
     poses = c2w[:, :3, :4].transpose([1,2,0])
     hwf = np.array([h1,w1,focal]).reshape([3,1])
     poses = np.concatenate([poses, np.tile(hwf[..., np.newaxis], [1,1,poses.shape[-1]])], 1)
@@ -204,15 +204,17 @@ def get_inner_mtx(photo_path,w1,h1):
     #标定
     ret, mtx, dist, rvecs, tvecs = \
         cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
+    print('rvecs:',rvecs[0])
+    print('tvecs:',tvecs[0])
 
     print('mtx:',mtx)
     focal = mtx[0:1,0:1]
     print('focal:',focal)
     camera_angle_x = np.arctan((focal/.5*w1))/0.5
     print('camera_angle_x:',camera_angle_x)
-    return(ret,mtx,dist,rvecs,tvecs,corners,focal,camera_angle_x)
+    return(ret,mtx,dist,rvecs,tvecs,corners,focal,camera_angle_x,h,w)
 
-def save(c2w_metrix,camera_angle_x_metrix,rvecs):
+def saveBlender(c2w_metrix,camera_angle_x_metrix,rvecs):
     os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
     f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
     log = {'c2w_metrix':c2w_metrix,'camera_angle_x':camera_angle_x_metrix,'rvecs':rvecs}
@@ -235,7 +237,7 @@ def set_calibration():
     print('calibrating')
     for photo_path in photos_path:
         # ret相机？？ mtx相机内参，dist相机畸变,rvecs旋转向量，tvecs平移向量,focal焦距
-        ret,mtx,dist,rvecs,tvecs,corners,focal,camera_angle_x = get_inner_mtx(photo_path,w1,h1)        
+        ret,mtx,dist,rvecs,tvecs,corners,focal,camera_angle_x,h,w = get_inner_mtx(photo_path,w1,h1)        
         c2w,cp = calibration_photo(photo_path,mtx,dist)
         # pose = get_pose(c2w,cp,w1,h1,focal)
         c2w_metrix.append(c2w)
@@ -245,8 +247,10 @@ def set_calibration():
     showsurface(c2w_metrix,cp_mertix,rvecs,tvecs)
     
     print('camera_angle_x_metrix:',camera_angle_x_metrix)
+
+    return c2w_metrix,h,w
     
-    save(c2w_metrix,camera_angle_x_metrix,rvecs)
+    # saveBlender(c2w_metrix,camera_angle_x_metrix,rvecs)
 
 
 
diff --git a/utils/calibNot.py b/utils/calibNot.py
new file mode 100755
index 0000000..3a126bd
--- /dev/null
+++ b/utils/calibNot.py
@@ -0,0 +1,191 @@
+import cv2
+import numpy as np
+import os
+import sys
+import glob
+import pickle
+import math
+from .utils import arse_config
+from .trans import read_pic, read_pic
+
+
+objpoints=[]
+imgpoints=[]
+
+def show(c2w,cp):
+    # 创建画布
+    fig = plt.figure(figsize=(5, 5),
+                    facecolor='whitesmoke'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='whitesmoke',
+                projection='3d' 
+                )
+    # -------------------------------- 绘制 3D 图形 --------------------------------
+    # # 二元函数定义域平面
+    # x = np.linspace(0, 9, 9)
+    # y = np.linspace(0, 9, 9)
+    # X, Y = np.meshgrid(x, y)
+    # # 平面 z=4.5 的部分
+    # ax.plot_surface(X,
+    #             Y,
+    #             Z=X*0+4.5,
+    #             color='g',
+    #             alpha=0.6
+    #            ) 
+    # # 散点图
+    dots = np.array(c2w).astype(int) # list,在画图前必须转为int类型
+    print(dots.shape[0])
+    for i in range(dots.shape[0]):
+        dot = dots[i]
+        print('dot:',dot)
+        print('i:',i)
+        print('dot[0][0]:',dot[0][0])
+        print('dot[1]:',dot[1][0])
+
+
+        ax.scatter(dot[0],dot[1],dot[2],c='g', marker='o')
+        ax.text(dot[0][3],dot[1][3],dot[2][3],i,fontsize=12, color = "r", style = "italic")
+    # --------------------------------  --------------------------------
+    # 设置坐标轴标题和刻度
+    ax.set(xlabel='X',
+        ylabel='Y',
+        zlabel='Z',
+        xlim=(0, 9),
+        ylim=(0, 9),
+        zlim=(0, 9),
+        xticks=np.arange(-200, 400, 60),
+        yticks=np.arange(-200, 400, 60),
+        zticks=np.arange(-600, 0, 60)
+        )
+
+    # 调整视角
+    ax.view_init(elev=15,    # 仰角
+                azim=60   # 方位角
+                )
+
+    # 显示图形
+    plt.show()
+
+def showsurface(c2w,cp,rvecs,tvecs):
+    fig = plt.figure(figsize=(5, 5),
+                    facecolor='whitesmoke'
+                    )
+    # 创建 3D 坐标系
+    ax = fig.gca(fc='whitesmoke',
+                projection='3d' 
+                )
+    c2w = np.array(c2w)
+    cp = np.array(cp)
+    print('shape c2w:',c2w.shape)
+    print('shape cp:',cp.shape)
+    print('c2w[0]:',c2w[0])
+    print('cp[0]:',cp[0])
+    for i in range(cp.shape[0]):
+        print('i:',i)
+        print('cp[i][0]:',cp[i][0])
+        print('cp.shape[0]:',cp.shape[0] )
+        x,y,z = np.meshgrid(list(cp[i][0]),list(cp[i][1]),list(cp[i][2]))
+        # ax.scatter(x,y,z,c='g', marker='o')
+
+        print('x:',x)
+        print('y:',y)
+        print('z:',z)
+        print('c2w[i]:',i)
+        # u = c2w[i][0]
+        # v = c2w[i][1]
+        # w = c2w[i][2]
+        # u, v, w = np.meshgrid(np.dot(cp[i],c2w[i]))
+        # u = np.dot(cp[i],c2w[i])[0]
+        # print('u:',u)
+        print('(np.dot(cp[i],c2w[i]):',(cp[i]*rvecs[i])+tvecs[i])
+        w2c = (cp[i]*rvecs[i])+tvecs[i]
+        u = w2c*cp[i][0][0]
+        v = w2c*cp[i][1][0]
+        w = w2c*cp[i][2][0]
+        print('w2c*cp[i]：',w2c*cp[i] )
+        print('type:',type(w2c*cp[i]))
+        print('w2c*cp[i][0][0]：',w2c*cp[i][0][0] )
+
+        # ax.scatter(u, v, w,c='g', marker='o')
+
+        ax.quiver(x, y, z, u, v, w, length=0.1, normalize=True)
+    
+    plt.show()
+ 
+
+
+# 获得像素坐标
+def calibration_photo(rvecs,tvecs,w1,h1):
+    tvecs = np.array(tvecs).reshape(3,1)
+    rvecs = np.array(rvecs).reshape(3,1)
+    objp = np.zeros((w1*h1,3), np.float32)
+    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
+    objp = objp*18.1  # 18.1 mm
+
+    list1 = rvecs
+    in_site=np.mat(list1)
+    in_rr=in_site/180*math.pi
+
+    #旋转向量转化为旋转矩阵
+    in_r=cv2.Rodrigues(in_rr,jacobian=0)[0]
+
+    R=in_r
+    t=tvecs
+    bottom = np.array([0,0,0,1.]).reshape([1,4])
+    w2c_metrix=np.concatenate([np.concatenate([R, t], 1), bottom], 0)
+    print('w2c_metrix:',w2c_metrix)
+    c2w_metrix = np.linalg.inv(w2c_metrix)
+    print('c2w_metrix:',c2w_metrix)
+    return(c2w_metrix)
+
+
+def get_inner_mtx(photo_path,w1,h1):
+    # number of coners
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # 阈值
+    objp = np.zeros((w1*h1,3), np.float32)
+    objp[:,:2] = np.mgrid[0:w1,0:h1].T.reshape(-1,2)
+    objp = objp*18.1  # 18.1 mm
+    img = cv2.imread(photo_path)
+    # print(img.shape)
+    #获取画面中心点
+    #图像的长宽(480, 640, 3)
+    print()
+    h, w = img.shape[0], img.shape[1]
+    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+    u, v = img.shape[:2]
+    # 找到棋盘格角点
+    ret, corners = cv2.findChessboardCorners(gray, (w1,h1), None)
+    # 如果找到足够点对，将其存储起来
+    if ret == True:
+        objpoints.append(objp)
+        imgpoints.append(corners)
+
+    #标定
+    ret, mtx, dist, rvecs, tvecs = \
+        cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
+    return(ret,mtx,dist,rvecs,tvecs,corners,h,w)
+
+
+def set_calibration():
+    args = arse_config()
+    read_pic(args.folder)
+    root = os.path.join(args.folder,'*.png') # 标定图像保存路径
+    photos_path = glob.glob(root)
+    w1 = args.config[0]
+    h1 = args.config[1] 
+    c2w_metrix = []
+    print('calibrating')
+    for photo_path in photos_path:
+        # mtx相机内参，dist相机畸变
+        ret,mtx,dist,rvecs,tvecs,corners,h,w= get_inner_mtx(photo_path,w1,h1)
+        c2w = calibration_photo(rvecs[-1],tvecs[-1],w1,h1)
+        # c2w = calibration(ret,mtx,dist,rvecs[-1],tvecs[-1],corners,w1,h1)
+        c2w_metrix.append(c2w)
+
+
+    os.makedirs(os.path.join(os.getcwd(),'log'),exist_ok=True)
+    f = os.path.join(os.getcwd(),'log','c2w_metrix.pkl')
+    log = {'c2w_metrix':c2w_metrix,'ret':ret,'rvecs':rvecs,'h:':h,'w:':w}
+    return c2w_metrix,h,w
+
diff --git a/utils/trans.py b/utils/trans.py
index 179635a..7bc11ae 100755
--- a/utils/trans.py
+++ b/utils/trans.py
@@ -21,13 +21,13 @@ def read_pic(root):
     os.rename(pname,nname)
     i+=1
 
-def json_write_llff(c2w_metrix,h,w,f):
-  
+def json_write_llff(c2w_metrix,f):
+  args = arse_config()
   print(type(c2w_metrix)) # list
   print(c2w_metrix[0])
   print(len(c2w_metrix))
-  f = f
-  hwf = np.array([h,w,f]).reshape(3,1)
+  hw = args.inter_corner_shape
+  hwf = np.array([hw[1],hw[0],f]).reshape(3,1)
   # print("hwf:",hwf)
   # print((hwf).shape)
   c2w_metrix = np.array(c2w_metrix)
diff --git a/utils/utils.py b/utils/utils.py
index 1dc9a03..ac76d51 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -5,7 +5,8 @@ def arse_config():
     parser.add_argument('--folder', type=str, \
         default='./chess/images',\
         help="图片所在文件位置")
-    parser.add_argument('--config', type=format, default=[11,8],help="格子长宽数")
+    parser.add_argument('--inter_corner_shape', type=format, default=(11,8),help="格子长宽数")
+    parser.add_argument('--size_per_grid', type=format, default=0.02)
     args = parser.parse_args()
 
     return args
\ No newline at end of file