将颜色应用于渐变方向

Question

参考this帖子，当将颜色渐变方向的程度硬编码到图像上时，强度变化的地方应该着色，而没有变化的地方，图像应该是黑色的。

我不确定该帖子是如何实现的。由于所有度数都被分配了一种颜色，因此对图像中的所有像素进行着色而不留下任何黑色。

我的代码如下：

# where gray_blur is a grayscale image of dimension 512 by 512

# 3x3 sobel filters for edge detection
sobel_x = np.array([[ -1, 0, 1], 
                   [ -2, 0, 2], 
                   [ -1, 0, 1]])


sobel_y = np.array([[ -1, -2, -1], 
                   [ 0, 0, 0], 
                   [ 1, 2, 1]])


# Filter the orginal and blurred grayscale images using filter2D
filtered = cv2.filter2D(gray_noise, cv2.CV_32F, sobel_x)

filtered_blurred_x = cv2.filter2D(gray_blur, cv2.CV_32F, sobel_x)  
filtered_blurred_y = cv2.filter2D(gray_blur, cv2.CV_32F, sobel_y) 

# Compute the orientation of the image
orien = cv2.phase(filtered_blurred_x, filtered_blurred_y, angleInDegrees=True)

image_map = np.zeros((orien.shape[0], orien.shape[1], 3), dtype=np.int16)

# Define RGB colours
red = np.array([255, 0, 0])
cyan = np.array([0, 255, 255])
green = np.array([0, 255, 0])
yellow = np.array([255, 255, 0])

# Set colours corresponding to angles
for i in range(0, image_map.shape[0]):
    for j in range(0, image_map.shape[1]):
        if orien[i][j] < 90.0:
            image_map[i, j, :] = red
        elif orien[i][j] >= 90.0 and orien[i][j] < 180.0:
            image_map[i, j, :] = cyan
        elif orien[i][j] >= 180.0 and orien[i][j] < 270.0:
            image_map[i, j, :] = green
        elif orien[i][j] >= 270.0 and orien[i][j] < 360.0:
            image_map[i, j, :] = yellow

# Display gradient orientation
f, ax1 = plt.subplots(1, 1, figsize=(20,10))

ax1.set_title('gradient orientation')
ax1.imshow(image_map)

我的代码在左侧生成输出，而我认为正确的表示是右侧的图像：

我想我错过了将每个像素硬编码为这些颜色之一的东西。

Answer 1

您缺少的是幅度的阈值。通过幅度，您可以知道它是否实际上具有任何相关的方向......在某种程度上，它是一种过滤方向，只给出具有强烈响应的方向，在大多数情况下是边缘。

如果此步骤未完成，那么每个像素至少有 4 种颜色中的一种，我认为这不是您想要的。

这是一个示例代码来说明我的观点：

import numpy as np
import cv2

# reads the image
img = cv2.imread("lena.png", 0

# sobel derivatives
derivX = cv2.Sobel(img, cv2.CV_32F, 1, 0)
derivY = cv2.Sobel(img, cv2.CV_32F, 0, 1)

# orientation and magnitude
orien = cv2.phase(derivX, derivY, angleInDegrees=True)
mag = cv2.magnitude(derivX, derivY)

# thresholding of the magnitude values, play with the thresh value adjust it too your liking
thresh = 50
_, mask = cv2.threshold(mag, thresh, 255, cv2.THRESH_BINARY)

# I used OpenCV imshow instead of matplotlib, so the colors are in BGR (use yours)
red = np.array([0, 0, 255])
cyan = np.array([255, 255, 0])
green = np.array([0, 255, 0])
yellow = np.array([0, 255, 255])

# for the same reason I use np.uint8
image_map = np.zeros((orien.shape[0], orien.shape[1], 3), dtype=np.uint8)

# setting the colors, maybe there is a better way, my numpy skills are rusty
# it checks that magnitude is above the threshold and that the orientation is in range
image_map[ (mask == 255) & (orien < 90) ] = red
image_map[(mask == 255) & (orien > 90) & (orien < 180)] = cyan
image_map[(mask == 255) & (orien > 180) & (orien < 270)] = green
image_map[(mask == 255) & (orien > 270)] = yellow

# just for showing it with opencv, replace it with matplotlib if you prefer
cv2.imshow("frame", image_map)
cv2.waitKey(0)

结果是：