texture = cv2.remap(image, pos_interpolated[:, :, :2].astype(np.float32), None, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=0)是什么意思
时间: 2024-02-29 15:54:32 浏览: 23
这段代码使用了 OpenCV 库中的 remap 函数,对输入的图像 image 进行了重映射操作。其中,pos_interpolated 是一个二维数组,存储了输入图像中的每个像素点在输出图像中对应的位置。这个数组的大小应该和输入图像的大小一致,每个元素包含两个浮点数表示输出图像中对应像素的位置。函数的第二个参数就是这个数组,它被转换为 float32 类型并传递给了 remap 函数。
函数的第三个参数为 None,表示不使用 mask。interpolation 参数指定了插值方法,这里使用了线性插值(INTER_LINEAR)。borderMode 参数指定了边界填充方式,这里使用了常数填充(BORDER_CONSTANT)。borderValue 参数指定了常数填充时的值,这里填充为 0。
最终,函数返回经过重映射后的图像 texture。
相关问题
这段代码是什么意思 def run_posmap_300W_LP(bfm, image_path, mat_path, save_folder, uv_h = 256, uv_w = 256, image_h = 256, image_w = 256): # 1. load image and fitted parameters image_name = image_path.strip().split('/')[-1] image = io.imread(image_path)/255. [h, w, c] = image.shape info = sio.loadmat(mat_path) pose_para = info['Pose_Para'].T.astype(np.float32) shape_para = info['Shape_Para'].astype(np.float32) exp_para = info['Exp_Para'].astype(np.float32) # 2. generate mesh # generate shape vertices = bfm.generate_vertices(shape_para, exp_para) # transform mesh s = pose_para[-1, 0] angles = pose_para[:3, 0] t = pose_para[3:6, 0] transformed_vertices = bfm.transform_3ddfa(vertices, s, angles, t) projected_vertices = transformed_vertices.copy() # using stantard camera & orth projection as in 3DDFA image_vertices = projected_vertices.copy() image_vertices[:,1] = h - image_vertices[:,1] - 1 # 3. crop image with key points kpt = image_vertices[bfm.kpt_ind, :].astype(np.int32) left = np.min(kpt[:, 0]) right = np.max(kpt[:, 0]) top = np.min(kpt[:, 1]) bottom = np.max(kpt[:, 1]) center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0]) old_size = (right - left + bottom - top)/2 size = int(old_size*1.5) # random pertube. you can change the numbers marg = old_size*0.1 t_x = np.random.rand()*marg*2 - marg t_y = np.random.rand()*marg*2 - marg center[0] = center[0]+t_x; center[1] = center[1]+t_y size = size*(np.random.rand()*0.2 + 0.9) # crop and record the transform parameters src_pts = np.array([[center[0]-size/2, center[1]-size/2], [center[0] - size/2, center[1]+size/2], [center[0]+size/2, center[1]-size/2]]) DST_PTS = np.array([[0, 0], [0, image_h - 1], [image_w - 1, 0]]) tform = skimage.transform.estimate_transform('similarity', src_pts, DST_PTS) cropped_image = skimage.transform.warp(image, tform.inverse, output_shape=(image_h, image_w)) # transform face position(image vertices) along with 2d facial image position = image_vertices.copy() position[:, 2] = 1 position = np.dot(position, tform.params.T) position[:, 2] = image_vertices[:, 2]*tform.params[0, 0] # scale z position[:, 2] = position[:, 2] - np.min(position[:, 2]) # translate z # 4. uv position map: render position in uv space uv_position_map = mesh.render.render_colors(uv_coords, bfm.full_triangles, position, uv_h, uv_w, c = 3) # 5. save files io.imsave('{}/{}'.format(save_folder, image_name), np.squeeze(cropped_image)) np.save('{}/{}'.format(save_folder, image_name.replace('jpg', 'npy')), uv_position_map) io.imsave('{}/{}'.format(save_folder, image_name.replace('.jpg', '_posmap.jpg')), (uv_position_map)/max(image_h, image_w)) # only for show # --verify # import cv2 # uv_texture_map_rec = cv2.remap(cropped_image, uv_position_map[:,:,:2].astype(np.float32), None, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT,borderValue=(0)) # io.imsave('{}/{}'.format(save_folder, image_name.replace('.jpg', '_tex.jpg')), np.squeeze(uv_texture_map_rec))
这段代码实现了一个人脸姿态估计的过程,包括以下几个步骤:
1. 加载图像和拟合参数。读取图像和一个.mat文件,其中包含了人脸姿态(pose_para)、形状参数(shape_para)和表情参数(exp_para)等信息。
2. 生成人脸3D模型。使用形状参数和表情参数生成人脸3D模型的顶点坐标,然后根据姿态参数对模型进行旋转、平移和缩放,得到投影到2D图像上的顶点坐标。
3. 根据关键点裁剪图像。根据投影到2D图像上的顶点坐标,找到包围人脸的最小矩形,然后随机扰动一下位置和大小,得到一个更大的矩形,最后将该矩形内的图像裁剪出来。
4. 渲染出UV坐标系下的人脸顶点坐标。基于人脸3D模型的顶点坐标和纹理坐标,通过三角形插值和透视变换,将人脸顶点坐标渲染到UV坐标系下,并生成一张UV位置图。
5. 保存结果。将裁剪后的图像、UV位置图和一些用于显示的图像保存到指定文件夹中。
import cv2 import numpy as np # 加载图像 img = cv2.imread('color_blind_road_2.png') # 转换为灰度图像 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 直线检测 edges = cv2.Canny(gray, 50, 150, apertureSize=3) lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=100, minLineLength=100, maxLineGap=10) for line in lines: x1, y1, x2, y2 = line[0] cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2) # 阈值分割 ret, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY) thresh = cv2.medianBlur(thresh, 5) # 彩色连续性空间分割 hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) h, s, v = cv2.split(hsv) mask = cv2.inRange(h, 0, 20) | cv2.inRange(h, 160, 180) mask = cv2.bitwise_and(thresh, mask) # 纹理分割 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) texture = cv2.Canny(gray, 100, 200, apertureSize=3) texture = cv2.dilate(texture, None, iterations=3) texture = cv2.erode(texture, None, iterations=3) texture = cv2.bitwise_and(thresh, texture) # 显示结果 cv2.imshow('img', img) cv2.imshow('thresh', thresh) cv2.imshow('color', mask) cv2.imshow('texture', texture) cv2.waitKey(0) cv2.destroyAllWindows()改进代码
以下是对代码的一些改进建议:
1. 在 import 语句之间添加空格,使代码更易于阅读。
2. 使用函数封装代码,将不同的操作分别放入不同的函数中,可以使代码更加清晰和易于维护。
3. 添加异常处理,以避免因为文件路径或读取失败而导致程序崩溃。
4. 将一些常量或者需要调整的参数提取出来,以方便后续调整和修改。
5. 添加注释,以便代码的阅读和理解。
6. 使用 with 语句释放资源。
改进后的代码如下:
```python
import cv2
import numpy as np
def load_image(path):
"""加载图片"""
try:
img = cv2.imread(path)
return img
except Exception as e:
print(e)
return None
def gray_transform(img):
"""灰度转换"""
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return gray
def edge_detection(img, threshold1=50, threshold2=150, apertureSize=3):
"""边缘检测"""
edges = cv2.Canny(img, threshold1, threshold2, apertureSize=apertureSize)
return edges
def line_detection(img, edges, threshold=100, minLineLength=100, maxLineGap=10):
"""直线检测"""
lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=threshold, minLineLength=minLineLength, maxLineGap=maxLineGap)
for line in lines:
x1, y1, x2, y2 = line[0]
cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
return img
def threshold_segmentation(img, threshold=150):
"""阈值分割"""
ret, thresh = cv2.threshold(img, threshold, 255, cv2.THRESH_BINARY)
thresh = cv2.medianBlur(thresh, 5)
return thresh
def hsv_segmentation(img, lower_range, upper_range):
"""HSV颜色空间分割"""
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower_range, upper_range)
return mask
def color_segmentation(img, thresh, lower_range1=(0, 100, 100), upper_range1=(20, 255, 255), lower_range2=(160, 100, 100), upper_range2=(180, 255, 255)):
"""颜色分割"""
mask1 = hsv_segmentation(img, lower_range1, upper_range1)
mask2 = hsv_segmentation(img, lower_range2, upper_range2)
mask = cv2.bitwise_or(mask1, mask2)
mask = cv2.bitwise_and(thresh, mask)
return mask
def texture_segmentation(img, thresh, threshold1=100, threshold2=200, iterations=3):
"""纹理分割"""
gray = gray_transform(img)
texture = cv2.Canny(gray, threshold1, threshold2, apertureSize=3)
texture = cv2.dilate(texture, None, iterations=iterations)
texture = cv2.erode(texture, None, iterations=iterations)
texture = cv2.bitwise_and(thresh, texture)
return texture
def show_image(img, winname='image'):
"""显示图片"""
cv2.imshow(winname, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == '__main__':
# 加载图片
img = load_image('color_blind_road_2.png')
if img is None:
exit()
# 灰度转换
gray = gray_transform(img)
# 边缘检测
edges = edge_detection(gray)
# 直线检测
img = line_detection(img, edges)
# 阈值分割
thresh = threshold_segmentation(gray)
# 颜色分割
mask = color_segmentation(img, thresh)
# 纹理分割
texture = texture_segmentation(img, thresh)
# 显示结果
show_image(img, 'img')
show_image(thresh, 'thresh')
show_image(mask, 'color')
show_image(texture, 'texture')
```