6目摄像头照片球面投影变换拼接全景图片

偏执的太偏执、 2024-04-18 10:21 95阅读 0赞

最近在做全景拼接项目，有有六张来自于六目摄像头的图片，分别来自不同方向上的图片，摄像头示意图如下：  
                                                                    ![在这里插入图片描述][20190905203052960.png]

各方向上图片的位置示意图如下：  
                                                       ![在这里插入图片描述][2019090521374156.png]

假设相对应位置的图片为拼接起来后（此处我们用代码进行了拼接，而且需要这样结构拼接起来作为输入）：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3U0NDExNzgx_size_16_color_FFFFFF_t_70]  
球面变换和拼接的代码如下：

from PIL import Image
    import math
    import numpy as np
    import cv2
    
    def spherical_coordinates(i, j, w, h):
        """ Returns spherical coordinates of the pixel from the output image. """
        theta = 2*float(i)/float(w)-1
        phi = 2*float(j)/float(h)-1
        # phi = lat, theta = long
        return phi*(math.pi/2), theta*math.pi
    
    
    def vector_coordinates(phi, theta):
        """ Returns 3D vector which points to the pixel location inside a sphere. """
        return (math.cos(phi) * math.cos(theta),  # X
                math.sin(phi),                    # Y
                math.cos(phi) * math.sin(theta))  # Z
    
    
    # Assign identifiers to the faces of the cube
    FACE_Z_POS = 1  # Left
    FACE_Z_NEG = 2  # Right
    FACE_Y_POS = 3  # Top
    FACE_Y_NEG = 4  # Bottom
    FACE_X_NEG = 5  # Front
    FACE_X_POS = 6  # Back
    
    
    def get_face(x, y, z):
        """ Uses 3D vector to find which cube face the pixel lies on. """
        largest_magnitude = max(abs(x), abs(y), abs(z))
        if largest_magnitude - abs(x) < 0.00001:
            return FACE_X_POS if x < 0 else FACE_X_NEG
        elif largest_magnitude - abs(y) < 0.00001:
            return FACE_Y_POS if y < 0 else FACE_Y_NEG
        elif largest_magnitude - abs(z) < 0.00001:
            return FACE_Z_POS if z < 0 else FACE_Z_NEG
    
    
    def raw_face_coordinates(face, x, y, z):
        """ Return coordinates with necessary sign (- or +) depending on which face they lie on. From Open-GL specification (chapter 3.8.10) https://www.opengl.org/registry/doc/glspec41.core.20100725.pdf """
        if face == FACE_X_NEG:
            xc = z
            yc = y
            ma = x
            return xc, yc, ma
        elif face == FACE_X_POS:
            xc = -z
            yc = y
            ma = x
            return xc, yc, ma
        elif face == FACE_Y_NEG:
            xc = z
            yc = -x
            ma = y
            return xc, yc, ma
        elif face == FACE_Y_POS:
            xc = z
            yc = x
            ma = y
            return xc, yc, ma
        elif face == FACE_Z_POS:
            xc = x
            yc = y
            ma = z
            return xc, yc, ma
        elif face == FACE_Z_NEG:
            xc = -x
            yc = y
            ma = z
            return xc, yc, ma
    
    
    def raw_coordinates(xc, yc, ma):
        """ Return 2D coordinates on the specified face relative to the bottom-left corner of the face. Also from Open-GL spec."""
        return (float(xc)/abs(float(ma)) + 1) / 2, (float(yc)/abs(float(ma)) + 1) / 2
    
    
    def face_origin_coordinates(face, n):
        """ Return bottom-left coordinate of specified face in the input image. """
        if face == FACE_X_NEG:
            return n, n
        elif face == FACE_X_POS:
            return 3*n, n
        elif face == FACE_Z_NEG:
            return 2*n, n
        elif face == FACE_Z_POS:
            return 0, n
        elif face == FACE_Y_POS:
            return n, 0
        elif face == FACE_Y_NEG:
            return n, 2*n
    
    
    def normalized_coordinates(face, x, y, n):
        """ Return pixel coordinates in the input image where the specified pixel lies. """
        face_coords = face_origin_coordinates(face, n)
        normalized_x = math.floor(x*n)
        normalized_y = math.floor(y*n)
    
        # Stop out of bound behaviour
        if normalized_x < 0:
            normalized_x = 0
        elif normalized_x >= n:
            normalized_x = n-1
        if normalized_y < 0:
            normalized_x = 0
        elif normalized_y >= n:
            normalized_y = n-1
    
        return face_coords[0] + normalized_x, face_coords[1] + normalized_y
    
    
    def find_corresponding_pixel(i, j, w, h, n):
        """ :param i: X coordinate of output image pixel :param j: Y coordinate of output image pixel :param w: Width of output image :param h: Height of output image :param n: Height/Width of each square face :return: Pixel coordinates for the input image that a specified pixel in the output image maps to. """
    
        spherical = spherical_coordinates(i, j, w, h)
        vector_coords = vector_coordinates(spherical[0], spherical[1])
        face = get_face(vector_coords[0], vector_coords[1], vector_coords[2])
        raw_face_coords = raw_face_coordinates(face, vector_coords[0], vector_coords[1], vector_coords[2])
    
        cube_coords = raw_coordinates(raw_face_coords[0], raw_face_coords[1], raw_face_coords[2])
    
        return normalized_coordinates(face, cube_coords[0], cube_coords[1], n)
    
    def get_six_pics_stitching(val_resize):
        size_t = val_resize
        # generate a (512,512) black image
        target = np.zeros((size_t, size_t), dtype=np.uint8) 
        ret = cv2.cvtColor(target, cv2.COLOR_GRAY2BGR)
    
        imgs_index = ["top", "left", "front", "right", "back", "down"]
        imgs_set = []
    
        # read all the images from target file
        for i in range(6):
            img = cv2.imread("./pics/{}.jpg".format(imgs_index[i]))
            img_res = cv2.resize(img, (size_t, size_t))
            imgs_set.append(img_res)
    
    
        # first row stitching
        ret_pano1 = np.concatenate((ret, imgs_set[0]), axis=1)
        for i in range(2):
            ret_pano1 = np.concatenate((ret_pano1, ret), axis=1)
    
        # Second row stitching
        imgs_mid_four = imgs_set[1:5]
        ret_pano2 = imgs_mid_four[0]
        for i in range(3):
            ret_pano2 = np.concatenate((ret_pano2, imgs_mid_four[i + 1]), axis=1)
    
        # Third row stitching
        ret_pano3 = np.concatenate((ret, imgs_set[5]), axis=1)
        for i in range(2):
            ret_pano3 = np.concatenate((ret_pano3, ret), axis=1)
    
        # whole image sitiching
        ret_pano = np.concatenate((ret_pano1, ret_pano2), axis=0)
        ret_pano = np.concatenate((ret_pano, ret_pano3), axis=0)
        cv2.imwrite('six_pics_stitching.jpg',ret_pano)
    
    
    def convert_img(infile, outfile):
    
        inimg = Image.open(infile)
    
        wo, ho = inimg.size
    
        # Calculate height and width of output image, and size of each square face
        h = int(wo/3)
        w = 2*h
        n = int(ho/3)
    
        # Create new image with width w, and height h
        outimg = Image.new('RGB', (w, h))
    
        # For each pixel in output image find colour value from input image
        for ycoord in range(0, h):
            for xcoord in range(0, w):
                corrx, corry = find_corresponding_pixel(xcoord, ycoord, w, h, n)
    
                outimg.putpixel((xcoord, ycoord), inimg.getpixel((corrx, corry)))
            # Print progress percentage
            print(str(round((float(ycoord)/float(h))*100, 2)) + '%')
        outimg.save(outfile, 'PNG')
    
    
    if __name__ == '__main__':
        val_resize = 512
        # you need put your images in the file"./pics/" or you can define by yourself.
        get_six_pics_stitching(val_resize)
        convert_img('six_pics_stitching.jpg', 'output.jpg')
        # out_img = cv2.imread('output.jpg')
        # cv2.imshow('out_img', out_img)
        # cv2.waitKey()

拼接后的效果如图：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3U0NDExNzgx_size_16_color_FFFFFF_t_70 1]

[20190905203052960.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/bbf89a42fcf542ecaf7fe804e3ad8095.png
[2019090521374156.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/79512de288394f25a235bbca74002be9.png?
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3U0NDExNzgx_size_16_color_FFFFFF_t_70]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/f3f267fdb6af4336889bf9096c73ffff.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3U0NDExNzgx_size_16_color_FFFFFF_t_70 1]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/2826d34bc1ca4e3fab85164021747c18.png