OpenCV Saliency Detection

Today’s tutorial is on saliency detection, the process of applying image processing and computer vision algorithms to automatically locate the most “salient” regions of an image.

In essence, saliency is what “stands out” in a photo or scene, enabling your eye-brain connection to quickly (and essentially unconsciously) focus on the most important regions.

For example — consider the figure at the top of this blog post where you see a soccer field with players on it. When looking at the photo, your eyes automatically focus on the players themselves as they are the most important areas of the photo. This automatic process of locating the important parts of an image or scene is called saliency detection.

Saliency detection is applied to many aspects of computer vision and image processing, but some of the more popular applications of saliency include:

  • Object detection — Instead of exhaustively applying a sliding window and image pyramid, only apply our (computationally expensive) detection algorithm to the most salient, interesting regions of an image most likely to contain an object
  • Advertising and marketing — Design logos and ads that “pop” and “stand out” to us from a quick glance
  • Robotics — Design robots with visual systems that are similar to our own

In the rest of today’s blog post, you will learn how to perform saliency detection using Python and OpenCV’s saliency module — keep reading to learn more!

Looking for the source code to this post?
Jump right to the downloads section.

OpenCV Saliency Detection with Python

Today’s post was inspired by PyImageSearch Gurus course member, Jeff Nova.

Inside one of the threads in the private PyImageSearch Gurus community forums Jeff wrote:

Figure 1: Jeff Nova’s OpenCV saliency question in the PyImageSearch Gurus Community forums.

Great question, Jeff!

And to be totally honest, I had completely forgotten about OpenCV’s saliency module.

Jeff’s question motivated me to do some research on the saliency module in OpenCV. After a few hours of research, trial and error, and just simply playing with the code, I was able to perform saliency detection using OpenCV.

Since there aren’t many tutorials on how to perform saliency detection, especially with the Python bindings, I wanted to write up a tutorial and share it with you.

Enjoy it and I hope it helps you bring saliency detection to your own algorithms.

Three different saliency detection algorithms

In OpenCV’s saliency  module there are three primary forms of saliency detection:

  1. Static saliency: This class of saliency detection algorithms relies on image features and statistics to localize the most interesting regions of an image.
  2. Motion saliency: Algorithms in this class typically rely on video or frame-by-frame inputs. The motion saliency algorithms process the frames, keeping track of objects that “move”. Objects that move are considered salient.
  3. Objectness: Saliency detection algorithms that compute “objectness” generate a set of “proposals”, or more simply bounding boxes of where it thinks an object may lie in an image.

Keep in mind that computing saliency is not object detection. The underlying saliency detection algorithm has no idea if there is a particular object in an image or not.

Instead, the saliency detector is simply reporting where it thinks an object may lie in the image — it is up to you and your actual object detection/classification algorithm to:

  1. Process the region proposed by the saliency detector
  2. Predict/classify the region and make any decisions on this prediction

Saliency detectors are often very fast algorithms capable of running in real-time. The results of the saliency detector are then passed into more computationally expensive algorithms that you would not want to run on every single pixel of the input image.

OpenCV’s saliency detectors

Figure 2: OpenCV’s saliency module class diagram. Click for the high-resolution image.

To utilize OpenCV’s saliency detectors you will need OpenCV 3 or greater. OpenCV’s official documentation on their saliency  module can be found on this page.

Keep in mind that you will need to have OpenCV compiled with the contrib  module enabled. If you have followed any of my OpenCV install tutorials on PyImageSearch you will have the contrib  module installed.

Note: I found that OpenCV 3.3 does not work with the motion saliency method (covered later in this blog post) but works with all other saliency implementations. If you find yourself needing motion saliency be sure you are using OpenCV 3.4 or greater.

You can check if the saliency  module is installed by opening up a Python shell and trying to import it:

If the import succeeds, congrats — you have the contrib  extra modules installed! But if the import fails, you will need to follow one of my guides to install OpenCV with the contrib  modules.

OpenCV provides us with four implementations of saliency detectors with Python bindings, including:

  • cv2.saliency.ObjectnessBING_create()
  • cv2.saliency.StaticSaliencySpectralResidual_create()
  • cv2.saliency.StaticSaliencyFineGrained_create()
  • cv2.saliency.MotionSaliencyBinWangApr2014_create()

Each of the above constructors returns an object implementing a .computeSaliency  method — we call this method on our input image, returning a two-tuple of:

  • A boolean indicating if computing the saliency was successful or not
  • The output saliency map which we can use to derive the most “interesting” regions of an image

In the remainder of today’s blog post, I will show you how to perform saliency detection using each of these algorithms.

Saliency detection project structure

Be sure to visit the “Downloads” section of the blog post to grab the Python scripts, image files, and trained model files.

From there, our project structure can be viewed in a terminal using the tree  command:

In our project folder we have two directories:

  • image/ : A selection of testing images.
  • objectness_trained_model/ : This is our model directory for the Objectness Saliency. Included are 9 .yaml files which comprising the objectness model iteslf.

We’re going to review three example scripts today:

  • : This script implements two forms of Static Saliency (based on image features and statistics). We’ll be reviewing this script first.
  • : Uses the BING Objectness Saliency method to generate a list of object proposal regions.
  • : This script will take advantage of your computer’s webcam and process live motion frames in real-time. Salient regions are computed using the Wang and Dudek 2014 method covered later in this guide.

Static saliency

OpenCV implements two algorithms for static saliency detection.

  1. The first method is from Montabone and Soto’s 2010 publication, Human detection using a mobile platform and novel features derived from a visual saliency mechanism. This algorithm was initially used for detecting humans in images and video streams but can also be generalized to other forms of saliency as well.
  2. The second method is by Hou and Zhang in their 2007 CVPR paper, Saliency detection: A spectral residual approach.

This static saliency detector operates on the log-spectrum of an image, computes saliency residuals in this spectrum, and then maps the corresponding salient locations back to the spatial domain. Be sure to refer to the paper for more details.

Let’s go ahead and try both of these static saliency detectors. Open up  and insert the following code:

On Lines 2 and 3 we import argparse  and cv2 . The argparse  module will allow us to parse a single command line argument — the --input  image (Lines 6-9). OpenCV (with the contrib  module) has everything we need to compute static saliency maps.

If you don’t have OpenCV installed you may follow my OpenCV installation guides. At the risk of being a broken record, I’ll repeat my recommendation that you should grab at least OpenCV 3.4 as I had trouble with OpenCV 3.3 for motion saliency further down in this blog post.

We then load the image into memory on Line 12.

Our first static saliency method is static spectral saliency. Let’s go ahead and compute the saliency map of the image and display it:

Using the cv2.saliency  module and calling the StaticSaliencySpectralResidual_create()  method, a static spectral residual saliency  object is instantiated (Line 16).

From there we invoke the computeSaliency  method on Line 17 while passing in our input image .

What’s the result?

The result is a saliencyMap , a floating point, grayscale image that highlights the most prominent, salient regions of the image. The range of floating point values is [0, 1] with values closer to 1 being the “interesting” areas and values closer to 0 being “uninteresting”.

Are we ready to visualize the output?

Not so fast! Before we can display the map, we need to scale the values to the range [0, 255] on Line 18.

From there, we can display the original image  and saliencyMap  image to the screen (Lines 19 and 20) until a key is pressed (Line 21).

The second static saliency method we’re going to apply is called “fine grained”. This next block mimics our first method, with the exception that we’re instantiating the fine grained object. We’re also going to perform a threshold to demonstrate a binary map that you might process for contours (i.e., to extract each salient region). Let’s see how it is done:

On Line 25, we instantiate the fine grained static saliency  object. From there we compute the saliencyMap  on Line 26.

The contributors for this code of OpenCV implemented the fine grained saliency differently than the spectral saliency. This time our values are already scaled in the range [0, 255], so we can go ahead and display on Line 36.

One task you might perform is to compute a binary threshold image so that you can find your likely object region contours. This is performed on Lines 31 and 32 and displayed on Line 37. The next steps would be a series of erosions and dilations (morphological operations) prior to finding and extracting contours. I’ll leave that as an exercise for you.

To execute the static saliency detector be sure to download the source code and example to this post (see the “Downloads” section below) and then execute the following command:

The image of Brazilian professional soccer player, Neymar Jr. first undergoes the spectral method:

Figure 3: Static spectral saliency with OpenCV on a picture of an injured Neymar Jr., a well known soccer player.

And then, after pressing a key, the fine grained method saliency map image is shown. This time I also display a threshold of the saliency map (which easily could have been applied to the spectral method as well):

Figure 4: Static saliency with OpenCV using the fine grained approach (top-right) and binary threshold of the saliency map (bottom).

The fine grained map more closely resembles a human than the blurry blob in the previous spectral saliency map. The thresholded image in the bottom center would be a useful starting point in a pipeline to extract the ROI of the likely object.

Now let’s try both methods on a photo of a boat:

The static spectral saliency map of the boat:

Figure 5: Static spectral saliency with OpenCV on a picture of a boat.

And fine grained:

Figure 6: Static fine grained saliency of a boat image (top-right) and binary threshold of the saliency map (bottom).

And finally, let’s try both the spectral and fine grained static saliency methods on a picture of three soccer players:

Here’s the output of spectral saliency:

Figure 7: A photo of three players undergoes static spectral saliency with OpenCV.

As well as fine-grained saliency detection:

Figure 8: Three soccer players are highlighted in a saliency map created with OpenCV. This time a fine grained approach was used (top-right). Then, a binary threshold of the saliency map was computed which would be useful as a part of a contour detection pipeline (bottom).

Objectness saliency

OpenCV includes one objectness saliency detector — BING: Binarized normed gradients for objectness estimation at 300fps, by Cheng et al. (CVPR 2014).

Unlike the other saliency detectors in OpenCV which are entirely self-contained in their implementation, the BING saliency detector requires nine separate model files for various window sizes, color spaces, and mathematical operations.

The nine model files together are very small (~10KB) and extremely fast, making BING an excellent method for saliency detection.

To see how we can use this objectness saliency detector with OpenCV open up  and insert the following code:

On Lines 2-4 we import our necessary packages. For this script, we’ll make use of NumPy, argparse , and OpenCV.

From there we parse three command line arguments on Lines 7-14:

  • --model : The path to the BING objectness saliency model.
  • --image : Our input image path.
  • --max-detections : The maximum number of detections to examine with the default set to 10 .

Next, we load our image  into memory (Line 17).

Let’s compute objectness saliency:

On Line 21 we initialize the objectness saliency  detector followed by establishing the training path on Line 22.

Given these two actions, we can now compute the objectness saliencyMap  on Line 25.

The number of available saliency detections can be obtained by examining the shape of the returned NumPy array (Line 26).

Now let’s loop over each of the detections (up to our set maximum):

On Line 29, we begin looping over the detections up to the maximum detection count which is contained within our command line args  dictionary.

Inside the loop, we first extract the bounding box coordinates (Line 31).

Then we copy  the image for display purposes (Line 34), followed by assigning a random color  to the bounding box (Lines 35-36).

To see OpenCV’s objectness saliency detector in action be sure to download the source code + example images and then execute the following command:

Figure 9: The objectness saliency detector (BING method) with OpenCV produces a total of 10 object region proposals as shown in the animation.

Here you can see that the objectness saliency method does a good job proposing regions of the input image where both Lionel Messi and Luis Saurez are standing/kneeling on the pitch.

You can imagine taking each of these proposed bounding box regions and passing them into a classifier or object detector for further prediction — and best of all, this method would be more computationally efficient than exhaustively applying a series of image pyramids and sliding windows.

Motion saliency

The final OpenCV saliency detector comes from Wang and Dudek’s 2014 publication, A fast self-tuning background subtraction algorithm.

This algorithm is designed to work on video feeds where objects that move in the video feed are considered salient.

Open up  and insert the following code:

We’re going to be working directly with our webcam in this script, so we first import the VideoStream  class from imutils on Line 2. We’ll also imutils  itself, time , and OpenCV (Lines 3-5).

Now that our imports are out of the way, we’ll initialize our motion saliency object and kick off our threaded VideoStream  object (Line 9).

From there we’ll begin looping and capturing a frame at the top of each cycle:

On Line 16 we grab a frame  followed by resizing it on Line 17. Reducing the size of the frame  will allow the image processing and computer vision techniques inside the loop to run faster. The less data there is to process, the faster our pipeline can run.

Lines 20-23 instantiate OpenCV’s motion saliency  object if it isn’t already established. For this script we’re using the Wang method, as the constructor is aptly named.

Next, we’ll compute the saliency map and display our results:

We convert the frame  to grayscale (Line 27) and subsequently compute our saliencyMap  (Line 28) — the Wang method requires grayscale frames.

As the saliencyMap  contains float values in the range [0, 1], we scale to the range [0, 255] and ensure that the value is an unsigned 8-bit integer (Line 29).

From there, we display the original frame  and the saliencyMap  on Lines 32 and 33.

We then check to see if the quit key (“q”) is pressed, and if it is, we break out of the loop and cleanup (Lines 34-42). Otherwise, we’ll continue to process and display saliency maps to our screen.

To execute the motion saliency script enter the following command:

Below I have recorded an example demo of OpenCV’s motion saliency algorithm in action:

OpenCV Version Note: Motion Saliency didn’t work for me in OpenCV 3.3 (and didn’t throw an error either). I tested in 3.4 and 4.0.0-pre and it worked just fine so make sure you are running OpenCV 3.4 or better if you intend on applying motion saliency.


In today’s blog post you learned how to perform saliency detection using OpenCV and Python.

In general, saliency detectors fall into three classes of algorithms:

  1. Static saliency
  2. Motion saliency
  3. Objectness saliency

OpenCV provides us with four implementations of saliency detectors with Python bindings, including:

  1. cv2.saliency.ObjectnessBING_create()
  2. cv2.saliency.StaticSaliencySpectralResidual_create()
  3. cv2.saliency.StaticSaliencyFineGrained_create()
  4. cv2.saliency.MotionSaliencyBinWangApr2014_create()

I hope this guide helps you apply saliency detection using OpenCV + Python to your own applications!

To download the source code to today’s post (and be notified when future blog posts are published here on PyImageSearch), just enter your email address in the form below!


If you would like to download the code and images used in this post, please enter your email address in the form below. Not only will you get a .zip of the code, I’ll also send you a FREE 17-page Resource Guide on Computer Vision, OpenCV, and Deep Learning. Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! Sound good? If so, enter your email address and I’ll send you the code immediately!

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40 Responses to OpenCV Saliency Detection

  1. Walid July 16, 2018 at 1:27 pm #

    Thanks a lot for the nice tutorial. it worked as promised
    I hoped for better bounding boxes and blobs though 🙂

    • Adrian Rosebrock July 16, 2018 at 2:35 pm #

      Thanks Wahid. As I mentioned, saliency detectors are not meant to be true object detectors capable of producing highly accurate bounding boxes. Instead, they are meant to give you hints as to where your more computationally expensive algorithms.

  2. Bikee Agrawal July 16, 2018 at 2:20 pm #

    hello sir, i m getting the following error in while importing salience:
    AttributeError: module ‘cv2.cv2’ has no attribute ‘saliency’

    I m using opencv version 3.2.0
    plz help me out

    • Adrian Rosebrock July 16, 2018 at 2:34 pm #

      It looks like you have a typo. You are writing “cv2.cv2” but it should be “cv2.saliency” — you should not be typing “cv2” twice.

      • Bikee Agrawal July 17, 2018 at 5:03 am #

        i have type ‘cv2’ once only…its still showing the same error…Any suggestion how can i get rid out of it?

        • Adrian Rosebrock July 17, 2018 at 7:08 am #

          Alexandre Saran in this comment thread had the same issue as well — be sure to see my reply to them. I tested this code with OpenCV 3.3, OpenCV 3.4, and OpenCV 4-pre. My guess is that OpenCV 3.2 is too old and you’ll need to upgrade to OpenCV 3.3 to use the saliency module.

      • Nikita Korolkov July 17, 2018 at 7:13 am #

        I got this problem too.
        with opencv-python==

        import cv2

        and this code raise error: ‘AttributeError: module ‘cv2.cv2’ has no attribute ‘saliency’

        In this version doesn’t have saliency function.

        • Adrian Rosebrock July 17, 2018 at 8:12 am #

          I see the issue. You installed via pip which does not include the “contrib” module where the “saliency” module is stored. I would suggest following one of my OpenCV install guides

          • Speed July 20, 2018 at 2:43 pm #

            pip install opencv-contrib-python solved the same problem for me in Windows using Python 3.6.3 and pip10.0.1

          • Adrian Rosebrock July 21, 2018 at 7:39 am #

            Thanks for sharing!

  3. Alexandre Saran July 16, 2018 at 4:20 pm #

    I’m getting the same error: AttributeError: module ‘cv2.cv2’ has no attribute ‘saliency’

    And, sadly, it’s not a “cv2” tiped twice. Any idea?

    • Adrian Rosebrock July 16, 2018 at 5:19 pm #

      Hey Alexandre — thanks for confirming that it’s not actually a typo. I had assumed it was. Bikee had mentioned using OpenCV 3.2, which version of OpenCV are you using? I tested only on OpenCV 3.3+, I did not test on anything less than that.

      • Chinmay Jog July 17, 2018 at 5:19 pm #

        Hi Adrian, I have run across the same error as some of the others here.
        AttributeError: module ‘cv2’ has no attribute ‘saliency’

        I have version 3.3.1, and I’m able to use other functions from the contrib module.

        • Adrian Rosebrock July 18, 2018 at 7:20 am #

          How did you install OpenCV? Did you follow one of my tutorials or another one?

          • Chinmay Jog July 19, 2018 at 6:37 pm #

            I used another tutorial since I have Windows 10 OS.

          • Adrian Rosebrock July 20, 2018 at 6:26 am #

            Unfortunately I’m not sure what may be causing the error then. Could you try OpenCV 3.3+ on a Unix machine to see if you run into the same error?

  4. Jon Snow July 16, 2018 at 10:38 pm #

    hey Adrian , y u use resize from imutils eventhough you import cv2. is it related to memory issues of Embedded devices ?

    • Adrian Rosebrock July 17, 2018 at 7:02 am #

      Simply because the imutils.resize function automatically preserves the aspect ratio. The cv2.resize function does not. We also did not use embedded devices in this tutorial so maybe I’m misunderstanding your question.

  5. Pranav Agarwal July 16, 2018 at 11:24 pm #

    Great Work! Is there any difference between saliency and attention? Because attention also does the same work. Are they only two different names doing the same work?

    • Adrian Rosebrock July 17, 2018 at 7:06 am #

      That’s a good question. As far as my understanding goes, attention and saliency belong in the same family but attention is also extended to more than just visual stimuli, including memory or “anticipations”.

      • Pranav Agarwal July 17, 2018 at 9:15 pm #


  6. M Sudhakar July 17, 2018 at 4:10 am #

    How to create bounding boxes from these output images?

    • Adrian Rosebrock July 17, 2018 at 7:03 am #

      You would detect contours in the output (binary) saliency map, loop over each of the contours, and compute the bounding box via the “cv2.boundingRect” function.

  7. Isha July 17, 2018 at 4:22 am #

    The line “Instead, the saliency detector is simply reporting where it thinks an object may lie in the image ” says it finds the region where an object might be. It seems to me it finds the interesting region region in image which may have object too. Am I right?

    • Adrian Rosebrock July 17, 2018 at 7:07 am #

      You are correct. The saliency detector will report locations where interesting regions worth further investigation may lie; however, the saliency detector does not know what these regions contain. It’s up to use to take those regions and apply our own detectors/classifiers.

  8. Dhruv Chamania July 17, 2018 at 6:19 am #

    Are there any applications of reinforcement learning(deep enforcement learning) that you have come across?

    • Dhruv Chamania July 17, 2018 at 6:20 am #

      Applications in Computer Vision I mean

      • Adrian Rosebrock July 17, 2018 at 7:00 am #

        Sorry, I don’t do much work in reinforcement learning so I unfortunately would not be the right person to ask.

  9. Douglas Jones July 17, 2018 at 2:22 pm #

    This is awesome! Thank you very much for taking the time! Worked like a champ on Windows with Anaconda and OpenCV 3.4.1. It is quite possible this may be the key to solving a problem that has been bugging me for 2 years!

    • Adrian Rosebrock July 18, 2018 at 7:21 am #

      Awesome, I’m glad to hear it Doug! Be sure to let me know how the project turns out! 😀

  10. ALTAF HUSSAIN July 17, 2018 at 4:30 pm #

    Hi , i am reading your articles every monday , now i am addicted to reading your articles. I ask question please write article about RCNN, YOLO , object segmentation in deeplearning.

    • Adrian Rosebrock July 18, 2018 at 7:21 am #

      I actually cover Faster R-CNNs, Single Shot Detectors (SSDs), and object detection using deep learning inside Deep Learning for Computer Vision with Python — that would be my suggested place to get started. I also have plans to cover object segmentation on the blog as well but that may take a few weeks.

  11. Salman Nourbakhsh July 18, 2018 at 10:22 am #

    Thank you Adrian

    I have a question, This method is faster to find the eyes and lips in a face or dlib? Is it possible at all to use this method for this purpose?

    Thank you

    • Adrian Rosebrock July 19, 2018 at 9:42 am #

      Saliency detection doesn’t have much to do with face detection, facial landmarks, or face applications. Keep in mind that saliency detectors have no idea what they are actually looking at — they are just quantifying if a given region of an image is worth further computation.

  12. Ahmad Jab July 18, 2018 at 11:25 am #

    great tutorial, your website have been the best resource for me to learn computer vision.
    is there a reason you used to get your video stream instead of using cv2.VideoCapture is it faster?

    • Adrian Rosebrock July 19, 2018 at 9:42 am #

      Yes, the VideoStream function is threaded, making it faster. Refer to this blog post for more details.

  13. Belhal Karimi July 23, 2018 at 11:04 am #

    Hi Adrian!
    Thanks for this.
    I am really struggling with the opencv package.
    I am using python 3 and opencv-python
    Yet I get module ‘cv2.cv2’ has no attribute ‘saliency’ error
    Any help?

    • Belhal Karimi July 23, 2018 at 11:27 am #

      Solved looking at the different comments.
      pip install opencv-contrib-python do the tricks on macOS

  14. hxy July 29, 2018 at 11:09 pm #

    in,how can I get a circle instead of a rectangle in image

    • Adrian Rosebrock July 31, 2018 at 9:53 am #

      The underlying algorithm only provides a bounding box rectangle. The only way you could obtain a circle would be to fit one to the returned rectangle.

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