As you can read in the doc, the norm() set of functions all operate on the generic InputArray datatype. So, from here, you can see that this can be either a cv::Mat or std::vector but certainly not cv::Point2f.

After checking in the sources (of 3.0, but I doubt it was different in 2.4 series), I see that that function has 9 overloads, but the only ones with cv::Point<> only have one argument.

So you will need to to convert your points to cv::Mat. This can be done easily, as there is a constructor for cv::Mat that takes a point as argument. So just do:

std::cout << cv::norm( cv::Mat(a), cv::Mat(b) ) << std::endl;

But as a side comment, I am not sure that this is the best solution: it implies additional memory allocation, and it is probably faster to compute the distance directly (i.e.: sqrt( (a.x-b.x)*(a.x-b.x) + (a.y-b.y)*(a.y-b.y) ). It seems harsh, but you can rely on the compiler to optimize that.

The other answers normalize an image based on the entire image. But if your image has a predominant color (such as black), it will mask out the features that you're trying to enhance since it will not be as pronounced. To get around this limitation, we can normalize the image based on a subsection region of interest (ROI). Essentially we will normalize based on the section of the image that we want to enhance instead of equally treating each pixel with the same weight. Take for instance this earth image:

Input image -> Normalization based on entire image

If we want to enhance the clouds by normalizing based on the entire image, the result will not be very sharp and will be over saturated due to the black background. The features to enhance are lost. So to obtain a better result we can crop a ROI, normalize based on the ROI, and then apply the normalization back onto the original image. Say we crop the ROI highlighted in green:

This gives us this ROI

The idea is to calculate the mean and standard deviation of the ROI and then clip the frame based on the lower and upper range. In addition, we could use an offset to dynamically adjust the clip intensity. From here we normalize the original image to this new range. Here's the result:

Before -> After

Code

import cv2
import numpy as np

# Load image as grayscale and crop ROI
image = cv2.imread('1.png', 0)
x, y, w, h = 364, 633, 791, 273
ROI = image[y:y+h, x:x+w]

# Calculate mean and STD
mean, STD  = cv2.meanStdDev(ROI)

# Clip frame to lower and upper STD
offset = 0.2
clipped = np.clip(image, mean - offset*STD, mean + offset*STD).astype(np.uint8)

# Normalize to range
result = cv2.normalize(clipped, clipped, 0, 255, norm_type=cv2.NORM_MINMAX)

cv2.imshow('image', image)
cv2.imshow('ROI', ROI)
cv2.imshow('result', result)
cv2.waitKey()

The difference between normalizing based on the entire image vs a specific section of the ROI can be visualized by applying a heatmap to the result. Notice the difference on how the clouds are defined.

Input image -> heatmap

Normalized on entire image -> heatmap

Normalized on ROI -> heatmap

Heatmap code

import matplotlib.pyplot as plt
import numpy as np
import cv2

image = cv2.imread('result.png', 0)
colormap = plt.get_cmap('inferno')
heatmap = (colormap(image) * 2**16).astype(np.uint16)[:,:,:3]
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_RGB2BGR)

cv2.imshow('image', image)
cv2.imshow('heatmap', heatmap)
cv2.waitKey()

Note: The ROI bounding box coordinates were obtained using how to get ROI Bounding Box Coordinates without Guess & Check and heatmap code was from how to convert a grayscale image to heatmap image with Python OpenCV

There are several types of normalization in Opencv.

Try running samples in the Online OpenCV-Flow tool to better understand each type.