我有一个图像，在整个图像中具有不同的亮度。我想预测图像中存在的所有点。将图像转换为灰度，并应用THRESH_BINARY_INV和THRESH_OTSU来获得二进制图像。然后执行连接组件分析，并创建一个掩码来过滤掉小组件。由于亮度变化，cv2.simpleBlobDetector()无法找到图像中的所有点。即使在调整其参数后也是如此。
另一种方法是将图像变亮并转换为灰度，然后微调阈值以获得二值图像。然后将其传递给LoG，DoG和DoH检测器，以找到图像中的所有点。找到Hessian行列式（DoH）似乎表现得很好，所以微调了它的参数，得到了比cv2.simpleBlobDetector()更好的结果。然而，它无法找到所有的点，图像。如果有人有一些有用的见解可以分享，那将是非常有用的。请在下面找到要处理的图片样本。
不同类型探测器的代码：

# Blob detection algorithms
blobs_log = blob_log(binary_image, max_sigma=20, num_sigma=10, threshold=.05)
blobs_log[:, 2] = skimage.feature.blobs_log[:, 2] * np.sqrt(2)

blobs_dog = blob_dog(binary_image, max_sigma=20, threshold=.1)
blobs_dog[:, 2] = skimage.feature.blobs_dog[:, 2] * np.sqrt(2)

blobs_doh = skimage.feature.blob_doh(binary_image, max_sigma=20, threshold=.03)

要处理的图像：

结果：蓝色圆点代表待检测目标，圆点周围的红色圆圈代表已被检测器检测到：

通过将sigma值调整为0.65生成的图像。

blob_doh(binary_image,min_sigma=0.65,threshold=.0325)

转换为B通道的LAB和阈值。

# Convert the image to LAB color space
lab_image = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)

# Extract the B channel (representing the blue-yellow axis)
b_channel = lab_image[:, :, 2]

# Define a threshold value (adjust as needed)
threshold_value = 75  # You can adjust this threshold value

# Apply thresholding on the B channel to make blobs darker than the background
binary_image = cv2.threshold(b_channel, threshold_value, 255, cv2.THRESH_BINARY_INV)[1]

cv2_imshow(binary_image)

#Find contours in the binary image
contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Create a copy of the original image to draw circles
result_image = img.copy()

# Draw red circles around the detected dots on the result image
for contour in contours:
    # Calculate the center and radius of the minimum enclosing circle
    (x, y), radius = cv2.minEnclosingCircle(contour)
    center = (int(x), int(y))
    radius = int(radius)
    
    # Draw a red circle with a thickness of 2
    cv2.circle(result_image, center, radius, (0, 0, 255), 1)

# Display the result
cv2_imshow(result_image)

对同一LAB转换图像使用doh检测器。

from skimage.feature import blob_doh
blobs = blob_doh(binary_image,min_sigma=0.65,threshold=.0325)

# Scale the blob coordinates to match the original image size
blobs[:, 0] *= img.shape[1] / binary_image.shape[1]
blobs[:, 1] *= img.shape[0] / binary_image.shape[0]

# Draw circles around the detected blobs on the result image
for blob in blobs:
    y, x, r = blob
    cv2.circle(img, (int(x), int(y)), int(r), (0, 0, 255), 1)  # Draw a red circle with a thickness of 2

# Display the result image with detected blobs
cv2_imshow(img)