Question

以下是我正在使用的示例图片：

在每张图片上都有一个测量条。测量条的尺寸和角度可以变化。我已经确定了与测量条的某些交叉点，现在需要确定它对应的数字（例如256,192,128 ......）。所以我需要识别像素范围并将每个像素映射到一个数字。为了识别这些范围，似乎唯一的方法是检测每个数字旁边的小线并将它们连接成一个更大的线。

我的计划是隔离这些小的测量线，然后使用HoughTransform连接它们之间的线，但是我发现很难隔离这些小线。我尝试过Canny边缘检测，但小的测量线总是作为垂直边缘的一部分被检测到。我尝试了很多不同的门槛和升级，没有成功。

img = cv2.imread('example.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
resized = cv2.resize(gray,None,fx=2, fy=2, interpolation = cv2.INTER_CUBIC)
blur_gray = cv2.GaussianBlur(resized,(5, 5),0)
edges = cv2.Canny(blur_gray, 100, 200)

升级x2 vs 升级x10

这甚至是正确的方法还是我可以使用另一种方法来提取这些测量线？

Answer 1

我会采取以下方法：

在 Canny 边缘检测后使用 cv2.HoughLinesP() 检测长直线
删除错误检测到的线条，仅保留 2 条与条带长边相对应的线条。
确保两条线都沿着条带上的黑线正确定位。
通过在两条线之间采样图像来抓取感兴趣的区域。
分析沿条带宽度的平均强度。使用带有 find_peaks 参数的 distance 来检测标记/文本之间的白色区域。

以下代码适用于您的示例。

import cv2
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import find_peaks
from skimage.draw import line

NOF_MARKERS = 30

# Show input image
img = cv2.imread("mPIXY.jpg")
img_orig = img.copy()
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
fig, axs = plt.subplots(2, 2)

# Detect long lines in the image
img_edg = cv2.Canny(img, 50, 120)
img_edg = cv2.morphologyEx(img_edg, cv2.MORPH_CLOSE, (5, 5), iterations=1)
img_edg = cv2.cvtColor(img_edg, cv2.COLOR_GRAY2RGB)
axs[0, 0].set_title("Canny edges + morphology closed")
axs[0, 0].imshow(img_edg)
lines = cv2.HoughLinesP(
    img_edg[:, :, 0].copy(),
    rho=1,
    theta=np.pi / 360,
    threshold=70,
    minLineLength=300,
    maxLineGap=15,
)
lines = lines.squeeze()
for x1, y1, x2, y2 in lines:
    cv2.line(img_edg, (x1, y1), (x2, y2), (255, 0, 0))
axs[0, 0].imshow(img_edg, aspect="auto")


def optimize_line_alignment(img_gray, line_end_points):
    # Shift endpoints to find optimal alignment with black line in the  origial image
    opt_line_mean = 255
    x1, y1, x2, y2 = line_end_points
    for dx1 in range(-3, 4):
        for dy1 in range(-3, 4):
            for dx2 in range(-3, 4):
                for dy2 in range(-3, 4):
                    line_discrete = np.asarray(
                        list(zip(*line(*(x1 + dx1, y1 + dy1), *(x2 + dx2, y2 + dy2))))
                    )
                    line_pixel_values = img_gray[
                        line_discrete[:, 1], line_discrete[:, 0]
                    ]
                    line_mean = np.mean(line_pixel_values)
                    if line_mean < opt_line_mean:
                        opt_line_end_points = np.array(
                            [x1 + dx1, y1 + dy1, x2 + dx2, y2 + dy2]
                        )
                        opt_line_discrete = line_discrete
                        opt_line_mean = line_mean
    return opt_line_end_points, opt_line_discrete


# Optimize alignment for the 2 outermost lines
dx = np.mean(abs(lines[:, 2] - lines[:, 0]))
dy = np.mean(abs(lines[:, 3] - lines[:, 1]))
if dy > dx:
    lines = lines[np.argsort(lines[:, 0]), :]
else:
    lines = lines[np.argsort(lines[:, 1]), :]
line1, line1_discrete = optimize_line_alignment(img_gray, lines[0, :])
line2, line2_discrete = optimize_line_alignment(img_gray, lines[-1, :])
cv2.line(img, (line1[0], line1[1]), (line1[2], line1[3]), (255, 0, 0))
cv2.line(img, (line2[0], line2[1]), (line2[2], line2[3]), (255, 0, 0))
axs[0, 1].set_title("Edges of the strip")
axs[0, 1].imshow(img, aspect="auto")

# Take region of interest from image
dx = round(0.5 * (line2[0] - line1[0]) + 0.5 * (line2[2] - line1[2]))
dy = round(0.5 * (line2[1] - line1[1]) + 0.5 * (line2[3] - line1[3]))
strip_width = len(list(zip(*line(*(0, 0), *(dx, dy)))))
img_roi = np.zeros((strip_width, line1_discrete.shape[0]), dtype=np.uint8)
for idx, (x, y) in enumerate(line1_discrete):
    perpendicular_line_discrete = np.asarray(
        list(zip(*line(*(x, y), *(x + dx, y + dy))))
    )
    img_roi[:, idx] = img_gray[
        perpendicular_line_discrete[:, 1], perpendicular_line_discrete[:, 0]
    ]

axs[1, 0].set_title("Strip analysis")
axs[1, 0].imshow(img_roi, cmap="gray")
extra_ax = axs[1, 0].twinx()
roi_mean = np.mean(img_roi, axis=0)
extra_ax.plot(roi_mean, label="mean")
extra_ax.plot(np.min(roi_mean, axis=0), label="min")
plt.legend()

# Locate the markers within region of interest
black_bar = np.argmin(roi_mean)
length = np.max([img_roi.shape[1] - black_bar, black_bar])
if black_bar < img_roi.shape[1] / 2:
    roi_mean = np.append(roi_mean, 0)
    peaks, _ = find_peaks(roi_mean[black_bar:], distance=length / NOF_MARKERS * 0.75)
    peaks = peaks + black_bar
else:
    roi_mean = np.insert(roi_mean, 0, 0)
    peaks, _ = find_peaks(roi_mean[:black_bar], distance=length / NOF_MARKERS * 0.75)
    peaks = peaks - 1
extra_ax.vlines(
    peaks,
    extra_ax.get_ylim()[0],
    extra_ax.get_ylim()[1],
    colors="green",
    linestyles="dotted",
)
axs[1, 1].set_title("Midpoints between markings")
axs[1, 1].imshow(img_orig, aspect="auto")
axs[1, 1].plot(line1_discrete[peaks, 0], line1_discrete[peaks, 1], "r+")
fig.show()

OpenCV检测非常小的线

1 个答案: