Canny Edge Detection in Python with OpenCV

NOTE: Are you interested in machine learning? You can get a copy of my TensorFlow machine learning book on Amazon by clicking HERE

In my previous tutorial, Color Detection in Python with OpenCV, I discussed how you could filter out parts of an image by color.

While it will work for detecting objects of a particular color, it doesn’t help if you’re trying to find a multi-colored object.

For this tutorial, we will be using this basket of fruits. Let’s say we wanted to detect everything except for the table.

We could try and use color, but that would fail very quickly because all of the fruits are different colors. Since canny edge detection doesn’t rely on color, we can use it to solve our problem.

Canny Edge Detection

If I asked you to draw around the basket, you could easily do it.

But hold on, why is it possible for you to differentiate between the basket and the table?

To understand what I mean, take a look at the same image in black and white. Even without color, you can clearly see the edges of the basket and fruits because the gradients are vastly different at the edges.

Canny edge detection uses this principle to differentiate between edges. All edges have different gradient intensities than their surroundings. If two adjacent parts of the image have the same gradient intensity, then it wouldn’t be an edge as they would have the same hue and saturation.

Applying Canny Edge Detection

We will be loading the image in as a black and white photo. Images in black and white still have gradients, so it is still possible to differentiate between edges. As a rule of thumb, things tend to be much simpler when they are in black and white.

After we load the image, we need to apply canny edge detection on it.

import cv2
from matplotlib import pyplot as plt

def nothing(x):
pass

img = cv2.blur(img_noblur, (7,7))

canny_edge = cv2.Canny(img, 0, 0)

cv2.imshow('image', img)
cv2.imshow('canny_edge', canny_edge)

cv2.createTrackbar('min_value','canny_edge',0,500,nothing)
cv2.createTrackbar('max_value','canny_edge',0,500,nothing)

while(1):
cv2.imshow('image', img)
cv2.imshow('canny_edge', canny_edge)

min_value = cv2.getTrackbarPos('min_value', 'canny_edge')
max_value = cv2.getTrackbarPos('max_value', 'canny_edge')

canny_edge = cv2.Canny(img, min_value, max_value)

k = cv2.waitKey(37)
if k == 27:
break

There are four arguments for cv2.Canny. The first one is your input image. The second and third are the min and max values for the gradient intensity difference to be considered an edge. The fourth is an optional argument which we have left blank.

If the fourth argument is set to true, then it uses a slower and more accurate edge detection algorithm. But by default, it is false and will calculate gradient intensity by adding up the absolute values of the gradient’s X and Y components.

Before canny edge detection can be applied, it is usually a good idea to apply a blur to the image so that random noise doesn’t get detected as an edge.

You can adjust the track bar however you’d like to edit the min and max values for the canny edge detection. I found that a min value of 36 and a max value of 53 worked well. It also depends on how much you are blurring the image. The more you blur the image, the less noise there is. However, blurrier image have less accurate edges.

On the flip side, not enough blurring causes random noise to be detected. In the end, the level of blur is a trade-off between noise and edge accuracy.

Conclusion

Canny edge detection is only one of the many ways to do edge detection. There are hundreds of different edge detection methods, including Sobel, Roberts, SUSAN, Prewitt, and Deriche.

All edge detection methods have pros and cons, and Canny is just one of them. In general, canny edge detection tends to yield good results in most scenarios, so it is well-suited for general use. However, other edge detectors may be better depending on the situation.

If Canny isn’t working effectively for you, try a different solution. The best way to know if something works well is to test it out for yourself.

Color Detection in Python with OpenCV

NOTE: Are you interested in machine learning? You can get a copy of my TensorFlow machine learning book on Amazon by clicking HERE

Image processing may seem like a daunting and scary task, but it’s actually not as terrible as some people make it out to be. In this tutorial, we will be doing basic color detection in OpenCV version 2.4.13. with Python 3.5. Note that you will also need to install NumPy to run the code in this article.

How Does Color Work on a Computer?

On a computer, color can be represented in many formats. However, in this tutorial, we will be strictly concerned with only BGR (Blue, Green, Red) and HSV (Hue Saturation Value).

With BGR, a pixel is represented by 3 parameters, blue, green, and red. Each parameter usually has a value from 0 – 255. For example, a pure blue pixel on your computer screen would have a B value of 255, a G value of 0, and a R value of 0. Your computer would read this and say, “Ah. This pixel is 255 parts blue, 0 parts green, and 0 parts red.”

With HSV, a pixel is also represented by 3 parameters, but it is instead Hue, Saturation and Value.

Unlike BGR, HSV does not use the primary color to represent a pixel. Instead, it uses hue, which is the color or shade of the pixel.

The saturation is the intensity of the color. A saturation of 0 is white, and a saturation of 255 is maximum intensity. Another way to think about it is to imagine saturation as the colorfulness of a certain pixel. Value is the simplest of the three, as it is just how bright or dark the color is.

HSV can be imagined like a three dimensional cylinder, as seen in the picture below.

Converting BGR to HSV

Since we will be using HSV, you will need an BGR to HSV to converter because OpenCV uses a different HSV scale from popular image editors like Gimp.

First, copy the following code into your favorite text editor and save it as converter.py. The lower and upper bound part will be explained later.

import sys
import numpy as np
import cv2

blue = sys.argv
green = sys.argv
red = sys.argv

color = np.uint8([[[blue, green, red]]])
hsv_color = cv2.cvtColor(color, cv2.COLOR_BGR2HSV)

hue = hsv_color

print("Lower bound is :"),
print("[" + str(hue-10) + ", 100, 100]\n")

print("Upper bound is :"),
print("[" + str(hue + 10) + ", 255, 255]")

Now, we need an image to do color detection on. Download the image below and place it in the same directory as converter.py We have an image, and an BGR to HSV converter. That’s all we need to get started, so let’s jump into the actual image processing.

Let’s Get to the Code Already!

Fire up your favorite text editor and save a new file called “image.py” in the same directory as the circles.png file.

First, we need to grab our imports and load the image in OpenCV.

import cv2
import numpy as np

The 1 means we want the image in BGR, and not in grayscale.

As stated before, we will be using HSV instead of BGR, so we need to convert our BGR image to a HSV image with the following line.

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

Great! Now that the picture is in HSV, we need something called a “lower range” and an “upper range” for the hue that we are searching for. The lower range is the minimum shade of red that will be detected, and the upper range is the maximum shade of red that will be detected. In our case, let’s search for the red circle at the top left. To do so, we will need to obtain the RGB numbers for the red circle.

I personally prefer Gimp, so I will be using that for the color picker feature. Simply use the color picker and click on the red circle, and you will have copied it. Now, click on the red shade that you copied. (see photo below) After clicking on that, you should see the following screen : We can see that red equals 237, green equals 28, and blue equals 36. We will be using these numbers with the converter to automatically generate the respective lower range and upper range HSV values for OpenCV. (Note that this method is inaccurate when the color is less pure or murky)

Remember that the HSV values shown in the photo are different from the ones in OpenCV. The scaling is different, so you can not use the values Gimp gives you for OpenCV.

Open up your terminal or command line and cd into the directory with the converter.py file and run the following :

python3 converter.py 36 28 237

Note that the order is in BGR, not RGB. After you run the script, it should output that the lower range = [169, 100, 100] and the upper range = [189, 255, 255].

We will now use NumPy to create arrays to hold our lower and upper range.

lower_range = np.array([169, 100, 100], dtype=np.uint8)
upper_range = np.array([189, 255, 255], dtype=np.uint8)

The “dtype = np.uint8” simply means that it will have the data type an 8 bit integer, which makes sense, because the max possible value for the hue, saturation, and value is 2^8 – 1.

Finally, with the lower range and the upper range found, we can create a mask for our image.

cv2.imshow('image', img)

while(1):
k = cv2.waitKey(0)
if(k == 27):
break

cv2.destroyAllWindows()

A mask is simply a specific part of the image. In this case, we are checking through the hsv image, and checking for colors that are between the lower-range and upper-range. The areas that match will be set to the mask variable.

After that, we can display both the mask and the image side-by-side.

The last three lines just state that the program will wait until the user presses the “esc” key (which has an id of 27) before it quits and destroys every OpenCV window.

If you’ve followed up to this point, you should end up with a mask that only has filled in white pixels for where the red circle was. And there you have it! You just did color matching in OpenCV. We found an upper and lower bound for the shade of red that we were looking for, and created a mask that only had white pixels filled in for wherever there was a red that matched.

The next tutorial in this OpenCV series is Canny Edge Detection in Python with OpenCV.