Now that we’ve had a taste of Deep Learning and Convolutional Neural Networks in last week’s blog post on LeNet, we’re going to take a step back and start to study machine learning in the context of image classification in more depth.
To start, we’ll reviewing the k-Nearest Neighbor (k-NN) classifier, arguably the most simple, easy to understand machine learning algorithm. In fact, k-NN is so simple that it doesn’t perform any “learning” at all!
In the remainder of this blog post, I’ll detail how the k-NN classifier works. We’ll then apply k-NN to the Kaggle Dogs vs. Cats dataset, a subset of the Asirra dataset from Microsoft.
The goal of the Dogs vs. Cats dataset, as the name suggests, is to classify whether a given image contains a dog or a cat. We’ll be using this dataset a lot in future blog posts (for reasons I’ll explain later in this tutorial), so make sure you take the time now to read through this post and familiarize yourself with the dataset.
All that said, let’s get started implementing k-NN for image classification to recognize dogs vs. cats in images!
k-NN classifier for image classification
After getting your first taste of Convolutional Neural Networks last week, you’re probably feeling like we’re taking a big step backward by discussing k-NN today.
What gives?
Well, here’s the deal.
I once wrote a (controversial) blog post on getting off the deep learning bandwagon and getting some perspective. Despite the antagonizing title, the overall theme of this post centered around various trends in machine learning history, such as Neural Networks (and how research in NNs almost died in the 70-80’s), Support Vector Machines, and Ensemble methods.
When each of these methods were introduced, researchers and practitioners were equipped with new, powerful techniques — in essence, they were given a hammer and every problem looked like a nail, when in reality, all they needed was a few simple turns of a phillips head to solve a particular the problem.
I have news for you: Deep learning is no different.
Go to the vast majority of popular machine learning and computer vision conferences and look at the recent list of publications. What is the overarching theme?
Deep learning.
Then, hop on large LinkedIn groups related to computer vision and machine learning. What are many people asking about?
How to apply deep learning to their datasets.
After that, go over to popular computer science sub-reddits such as /r/machinelearning. What tutorials are the most consistently upvoted?
You guessed it: deep learning.
Here’s the bottom line:
Yes, I will teach you about Deep Learning and Convolutional Neural Networks on this blog — but you’re damn-well going to understand that Deep Learning is just a TOOL, and like any other tool, there is a right and wrong time to use it.
Because of this, it’s important for us to understand the basics of machine learning before we progress too far. Over the next few weeks, I’ll be discussing the basics of machine learning and Neural Networks, eventually building up to Deep Learning (where you’ll be able to appreciate the inner-workings of these algorithms more).
Kaggle Dogs vs. Cats dataset
The Dogs vs. Cats dataset was actually part of a Kaggle challenge a few years back. The challenge itself was simple: given an image, predict whether it contained a dog or a cat:
Simple enough — but if you know anything about image classification, you’ll understand that given:
- Viewpoint variation
- Scale variation
- Deformation
- Occlusion
- Background clutter
- Intra-class variation
That the problem is significantly harder than it might appear on the surface.
By simply randomly guessing, you should be able to reach 50% accuracy (since there are only two class labels). A machine learning algorithm will need to obtain > 50% accuracy in order to demonstrate that it has in fact “learned” something (or found an underlying pattern in the data).
Personally, I love the Dogs vs. Cats challenge, especially for teaching Deep Learning.
Why?
The dataset is simple enough to wrap your head around — there are only two classes: “dog” or “cat”.
However, the dataset is nicely sized, containing 25,000 images in the training data. This means that you have enough data to train data-hungry Convolutional Neural Networks from scratch.
We’ll be using this dataset a lot in future blog posts. I’ve actually included it in the “Downloads” section of this blog post for your convenience, so scroll down to grab the code + data before you follow along.
Project structure
Once you’ve downloaded the archive for this blog post, unzip it to someplace convenient. From there let’s take a look at the project directory structure:
$ tree --filelimit 10 . ├── kaggle_dogs_vs_cats │ └── train [25000 entries exceeds filelimit, not opening dir] └── knn_classifier.py 2 directories, 1 file
The Kaggle dataset is included in the kaggle_dogs_vs_cats/train directory (it comes from train.zip available on the Kaggle webpage).
We’ll be reviewing one Python script today — knn_classifier.py . This file will load the dataset, establish and run the K-NN classifier, and print out the evaluation metrics.
How does the k-NN classifier work?
The k-Nearest Neighbor classifier is by far the most simple machine learning/image classification algorithm. In fact, it’s so simple that it doesn’t actually “learn” anything.
Inside, this algorithm simply relies on the distance between feature vectors, much like building an image search engine — only this time, we have the labels associated with each image so we can predict and return an actual category for the image.
Simply put, the k-NN algorithm classifies unknown data points by finding the most common class among the k-closest examples. Each data point in the k closest examples casts a vote and the category with the most votes wins!
Or, in plain english: “Tell me who your neighbors are, and I’ll tell you who you are”
To visualize this, take a look at the following toy example where I have plotted the “fluffiness” of animals along the x-axis and the lightness of their coat on the y-axis:
Here we can see there are two categories of images and that each of the data points within each respective category are grouped relatively close together in an n-dimensional space. Our dogs tend to have dark coats which are not very fluffy while our cats have very light coats that are extremely fluffy.
This implies that the distance between two data points in the red circle is much smaller than the distance between a data point in the red circle and a data point in the blue circle.
In order to apply the k-nearest Neighbor classification, we need to define a distance metric or similarity function. Common choices include the Euclidean distance:
And the Manhattan/city block distance:
Other distance metrics/similarity functions can be used depending on your type of data (the chi-squared distance is often used for distributions [i.e., histograms]). In today’s blog post, for the sake of simplicity, we’ll be using the Euclidean distance to compare images for similarity.
Implementing k-NN for image classification with Python
Now that we’ve discussed what the k-NN algorithm is, along with what dataset we’re going to apply it to, let’s write some code to actually perform image classification using k-NN.
Open up a new file, name it knn_classifier.py , and let’s get coding:
# import the necessary packages from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split from imutils import paths import numpy as np import argparse import imutils import cv2 import os
We start off on Lines 2-9 by importing our required Python packages. If you haven’t already installed the scikit-learn library, then you’ll want to follow these instructions and install it now.
Note: This blog post has been updated to be compatible with the future scikit-learn==0.20 where sklearn.cross_validation has been replaced by sklearn.model_selection .
Secondly, we’ll be using the imutils library, a package that I have created to store common computer vision processing functions. If you do not have imutils installed, you’ll want to do that now:
$ pip install imutils
Next, we are going to define two methods to take an input image and convert it to a feature vector, or a list of numbers that quantify the contents of an image. The first method can be seen below:
def image_to_feature_vector(image, size=(32, 32)): # resize the image to a fixed size, then flatten the image into # a list of raw pixel intensities return cv2.resize(image, size).flatten()
The image_to_feature_vector method is an extremely naive function that simply takes an input image and resizes it to a fixed width and height (size ), and then flattens the RGB pixel intensities into a single list of numbers.
This means that our input image will be shrunk to 32 x 32 pixels, and given three channels for each Red, Green, and Blue component respectively, our output “feature vector” will be a list of 32 x 32 x 3 = 3,072 numbers.
Strictly speaking, the output of image_to_feature_vector is not a true “feature vector” since we tend to think of “features” and “descriptors” as abstract quantifications of the image contents.
Furthermore, utilizing raw pixel intensities as inputs to machine learning algorithms tends to yield poor results as even small changes in rotation, translation, viewpoint, scale, etc., can dramatically influence the image itself (and thus your output feature representation).
Note: As we’ll find out in later tutorials, Convolutional Neural Networks obtain fantastic results using raw pixel intensities as inputs — but this is because they learn a robust set of discriminating filters during the training process.
We then define our second method, this one called extract_color_histogram :
def extract_color_histogram(image, bins=(8, 8, 8)): # extract a 3D color histogram from the HSV color space using # the supplied number of `bins` per channel hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) hist = cv2.calcHist([hsv], [0, 1, 2], None, bins, [0, 180, 0, 256, 0, 256]) # handle normalizing the histogram if we are using OpenCV 2.4.X if imutils.is_cv2(): hist = cv2.normalize(hist) # otherwise, perform "in place" normalization in OpenCV 3 (I # personally hate the way this is done else: cv2.normalize(hist, hist) # return the flattened histogram as the feature vector return hist.flatten()
As the name suggests, this function accepts an input image and constructs a color histogram to characterize the color distribution of the image.
First, we convert our image to the HSV color space on Line 19. We then apply the cv2.calcHist function to compute a 3D color histogram for the image (Lines 20 and 21). You can read more about computing color histograms in this post. You might also be interested in applying color histograms to image search engines and in general, how to compate color histograms for similarity.
Given our computed hist , we then normalize it, taking care to use the appropriate cv2.normalize function signature based on our OpenCV version (Lines 24-30).
Given 8 bins for each of the Hue, Saturation, and Value channels respectively, our final feature vector is of size 8 x 8 x 8 = 512, thus our image is characterized by a 512-d feature vector.
Next, let’s parse our command line arguments:
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help="path to input dataset")
ap.add_argument("-k", "--neighbors", type=int, default=1,
help="# of nearest neighbors for classification")
ap.add_argument("-j", "--jobs", type=int, default=-1,
help="# of jobs for k-NN distance (-1 uses all available cores)")
args = vars(ap.parse_args())
We require only one command line argument, followed by two optional ones, each of which are detailed below:
--dataset: This is the path to our input Kaggle Dogs vs. Cats dataset directory.--neighbors: Here we can supply the number of nearest neighbors that are taken into account when classifying a given data point. We’ll default this value to one, meaning that an image will be classified by finding its closest neighbor in an n–dimensional space and then taking the label of the closest image. In next week’s post, I’ll demonstrate how to automatically tune k for optimal accuracy.--jobs: Finding the nearest neighbor for a given image requires us to compute the distance from our input image to every other image in our dataset. This is clearly a O(N) operation that scales linearly. For larger datasets, this can become prohibitively slow. In order to speedup the process, we can distribute the computation of nearest neighbors across multiple processors/cores of our machine. Setting--jobsto-1ensures that all processors/cores are used to help speedup the classification process.
Note: We can also speedup the k-NN classifier by utilizing specialized data structures such as kd-trees or Approximate Nearest Neighbor algorithms such as FLANN or Annoy. In practice, these algorithms can reduce nearest neighbor search to approximately O(log N); however, for the sake of simplicity in this post, we’ll perform an exhaustive nearest neighbor search.
We are now ready to prepare our images for feature extraction:
# grab the list of images that we'll be describing
print("[INFO] describing images...")
imagePaths = list(paths.list_images(args["dataset"]))
# initialize the raw pixel intensities matrix, the features matrix,
# and labels list
rawImages = []
features = []
labels = []
Line 47 grabs the paths to all 25,000 training images from disk.
We then initialize three lists (Lines 51-53) to store the raw image pixel intensities (the 3072-d feature vector), another to store the histogram features (the 512-d feature vector), and finally the class labels themselves (either “dog” or “cat”).
Let’s move on to extracting features from our dataset:
# loop over the input images
for (i, imagePath) in enumerate(imagePaths):
# load the image and extract the class label (assuming that our
# path as the format: /path/to/dataset/{class}.{image_num}.jpg
image = cv2.imread(imagePath)
label = imagePath.split(os.path.sep)[-1].split(".")[0]
# extract raw pixel intensity "features", followed by a color
# histogram to characterize the color distribution of the pixels
# in the image
pixels = image_to_feature_vector(image)
hist = extract_color_histogram(image)
# update the raw images, features, and labels matricies,
# respectively
rawImages.append(pixels)
features.append(hist)
labels.append(label)
# show an update every 1,000 images
if i > 0 and i % 1000 == 0:
print("[INFO] processed {}/{}".format(i, len(imagePaths)))
We start looping over our input images on Line 56. Each image is loaded from disk and the class label is extracted from the imagePath (Lines 59 and 60).
We apply the image_to_feature_vector and extract_color_histogram functions on Lines 65 and 66 — these functions are used to extract our feature vectors from the input image .
Given our features labels, we then update the respective rawImages , features , and labels lists on Lines 70-72.
Finally, we display an update to our terminal to inform us on feature extraction progress every 1,000 images (Lines 75 and 76).
You might be curious how much memory our rawImages and features matrices take up — the following code block will tell us when executed:
# show some information on the memory consumed by the raw images
# matrix and features matrix
rawImages = np.array(rawImages)
features = np.array(features)
labels = np.array(labels)
print("[INFO] pixels matrix: {:.2f}MB".format(
rawImages.nbytes / (1024 * 1000.0)))
print("[INFO] features matrix: {:.2f}MB".format(
features.nbytes / (1024 * 1000.0)))
We start by converting our lists to NumPy arrays. We then use the .nbytes attribute of the NumPy array to display the number of megabytes of memory the representations utilize (about 75MB for the raw pixel intensities and 50MB for the color histograms. This implies that we can easily store our features in main memory).
Next, we need to take our data partition it into two splits — one for training and another for testing:
# partition the data into training and testing splits, using 75% # of the data for training and the remaining 25% for testing (trainRI, testRI, trainRL, testRL) = train_test_split( rawImages, labels, test_size=0.25, random_state=42) (trainFeat, testFeat, trainLabels, testLabels) = train_test_split( features, labels, test_size=0.25, random_state=42)
Here we’ll be using 75% of our data for training and the remaining 25% for testing the k-NN algorithm.
Let’s apply the k-NN classifier to the raw pixel intensities:
# train and evaluate a k-NN classifer on the raw pixel intensities
print("[INFO] evaluating raw pixel accuracy...")
model = KNeighborsClassifier(n_neighbors=args["neighbors"],
n_jobs=args["jobs"])
model.fit(trainRI, trainRL)
acc = model.score(testRI, testRL)
print("[INFO] raw pixel accuracy: {:.2f}%".format(acc * 100))
Here we instantiate the KNeighborsClassifier object from the scikit-learn library using the supplied number of --neighbors and --jobs .
We then “train” our model by making a call to .fit on Line 99, followed by evaluation on the testing data on Line 100.
In a similar fashion, we can also train and evaluate a k-NN classifier on our histogram representations:
# train and evaluate a k-NN classifer on the histogram
# representations
print("[INFO] evaluating histogram accuracy...")
model = KNeighborsClassifier(n_neighbors=args["neighbors"],
n_jobs=args["jobs"])
model.fit(trainFeat, trainLabels)
acc = model.score(testFeat, testLabels)
print("[INFO] histogram accuracy: {:.2f}%".format(acc * 100))
k-NN image classification results
To test our k-NN image classifier, make sure you have downloaded the source code to this blog post using the “Downloads” form found at the bottom of this tutorial.
The Kaggle Dogs vs. Cats dataset is included with the download.
From there, just execute the following command:
$ python knn_classifier.py --dataset kaggle_dogs_vs_cats
At first, you’ll see that our images are being described and quantified via the image_to_feature_vector and extract_color_histogram functions:
This process shouldn’t take longer than 1-3 minutes depending on the speed of your machine.
After the feature extraction process is complete, we can see some information on the size (in MB) of our feature representations:
The raw pixel features take up 75MB while the color histograms require only 50MB of RAM.
Finally, the k-NN algorithm is trained and evaluated for both the raw pixel intensities and color histograms:
As the figure above demonstrates, by utilizing raw pixel intensities we were able to reach 54.42% accuracy. On the other hand, applying k-NN to color histograms achieved a slightly better 57.58% accuracy.
In both cases, we were able to obtain > 50% accuracy, demonstrating there is an underlying pattern to the images for both raw pixel intensities and color histograms.
However, that 57% accuracy leaves much to be desired.
And as you might imagine, color histograms aren’t the best way to distinguish between a dog and a cat:
- There are brown dogs. And there are brown cats.
- There are black dogs. And there are black cats.
- And certainly a dog and cat could appear in the same environment (such as a house, park, beach, etc.) where the background color distributions are similar.
Because of this, utilizing strictly color is not a great choice for characterizing the difference between dogs and cats — but that’s okay. The purpose of this blog post was simply to introduce the concept of image classification using the k-NN algorithm.
We can easily apply methods to obtain higher accuracy. And as we’ll see, utilizing Convolutional Neural Networks we can achieve > 95% accuracy without much effort — but I’ll save that for a future discussion once we better understand image classification.
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Can we do better?
You might be wondering, can we do better than the 57% classification accuracy?
You’ll notice that I’ve used only k=1 in this example, implying that only one nearest neighbor is considered when classifying each image. How would the results change if I used k=3 or k=5?
And how about the choice in distance metric — would the Manhattan/City block distance be a better choice?
How about changing both the value of k and distance metric at the same time?
Would classification accuracy improve? Get worse? Stay the same?
The fact is that nearly all machine learning algorithms require a bit of tuning to obtain optimal results. In order to determine the optimal set of values for these model variables, we apply a process called hyperparameter tuning, which is exactly the topic of next week’s blog post.
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Summary
In this blog post, we reviewed the basics of image classification using the k-NN algorithm. We then applied the k-NN classifier to the Kaggle Dogs vs. Cats dataset to identify whether a given image contained a dog or a cat.
Utilizing only the raw pixel intensities of the input image images, we obtained 54.42% accuracy. And by using color histograms, we achieved a slightly better 57.58% accuracy. Since both of these results are > 50% (we should expect to get 50% accuracy simply by random guessing), we can ascertain that there is an underlying pattern in the raw pixels/color histograms that can be used to discriminate dogs vs. cats (although 57% accuracy is quite poor).
That raises the question: “Is it possible to obtain > 57% classification accuracy using k-NN? If so, how?”
The answer is hyperparameter tuning — which is exactly what we’ll be covering in next week’s blog post.
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Great Post, Thanks for such a useful knowledge 😀
Thanks Navdeep! 🙂
Is there any way to save the classifier so rather than having to run the test each time and wait each time, I can just load it and run it?
Certainly, just use
cPickleto write your model to file:f = open("model.cpickle", "wb") f.write(cPickle.dumps(model)) f.close()And then load it again:
model = cPickle.loads(open("model.cpickle", "rb").read())could you give me a good link explain cpickle.dumps?
The cpickle.dumps function serializes the Python object to disk so you can read it from disk using a separate script (likely to perform prediction). If you’re using Python 3 you should instead use “pickle.dumps” (notice no “c” in the function).
If you encounter a Python function you have not used before I would first recommend reading the docs.
Awesome tutorial !!! 🙂
I think this post would be a kickstarter for a newbie
thanks adrian
Amazing article and well thought pace of content. Thanks for sharing. Looking forward for more.
Thanks for the feedback Ashish, I’m glad you liked it!
Adrian – I must say, you are helping the community in a great way !! Kudos for your effort and time. I just needed an advice on how to install SciPy on Windows Python. I have Python 2.7 on my Machine. I downloaded scipy-0.18.0.zip for windows from one of the websites but don’t know where to go thereafter..Any suggestion would be appreciated.
Hey Manik — in general, I don’t recommend using Windows for computer vision development. I personally haven’t used Windows in many years, so I’m not sure regarding your exact situation. I’ll leave this comment here for another reader to answer.
Download from this site for your python version:
http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
Then from your windows command line go to location of the downloads i.e use cd
Finally:pip install full name of the module(scipy)
It needs numpy+mkl follow the same procedure to install,I think it will help!
I suggest you to download WinPython or anaconda or simply try pip install scipy
Who can elaborate on what is really the right hand side of line 51, the raw images array initializer? I see a square there. What is the intended python code? Thanks!
2nd thing: Line 11 and other parts of this program are EXACTLY the kind of code I was looking for, for other reasons. Particularly, this code shows me how to load a machine learning dataset from genuine image files! I am so happy now. Thanks for writing this code to get me bootstrapped with machine learning on images, real ones like jpegs! Not prepackaged images contained in some weird monolithic singleton file!
Now if I could just find a rosetta stone (or nice person) to explain the full python meaning of the square at line 51!
Hey George — what exactly is your question regarding Line 51? Line 51 simply initializes a list. We append to list another set of lists: the raw pixel intensities.
I thought the same as you for a moment but the problem is how the code is displayed, that is not a square but two square parenthesis [ ]…
Hello
Thanks for this great tutorial, was awesome! I am learning alot of things.
Well, I would like to ask you a few questions, can you please help?
I was doing your tutorial and I saw a point:
imagePaths = list(paths.list_images(path))
The folder needs to be train1 folder or test1 folder? When I downloaded catsvsdogs from Kaggle I am getting this two folders. I don’t know what I use. Can you help me?
How I use Manhattan distance? can you give me a example?
Thanks for hlelp
You can use the Manhattan distance by switching the
metricin KNeighborsClassifier to be “cityblock”.As for your images, you should have a separate directory for each class. One directory for “dog” and another directory for “cat”.
Hey Adrian,
Just wondering where the best place is to start machine learning and deep learning as a complete newb like me.
Love your work, Matt
If you’re starting from scratch, the absolute best place to learn computer vision, machine learning, and deep learning is inside the PyImageSearch Gurus course. The course covers advanced algorithms, but also takes the time to explain the basics.
Great post for beginners, it covers all the workflow from preprocessing and analyzing. Thanks!
Thanks Johnny, I’m glad the post helped! 🙂
Sorry for this silly question but how can I now classify a single image ?? 😛
You would use the
.predictmethod of themodel:prediction = model.predict(hist)Where
histis the histogram of your input image.For more details on how to use OpenCV + Python to classify the contents of an image please refer to my book Practical Python and OpenCV and the PyImageSearch Gurus course.
Hi Adrian, the dataset comes pre-split from kaggle. In train and test1 zips. Both contain 25k and 12.5k images respectively. Does the code in this blog post take the pre-split into account or should I combine train and test from kaggle into one folder and let your code do the split?
The code from this blog post only uses the training data from Kaggle and then partitions into two respective training and testing splits. If you wanted to train a model and submit the results from Kaggle you would use the pre-split zip files from the official Kaggle challenge.
It’s been awhile since I looked at the “test1” directory, but I wouldn’t recommend combining them. They likely use different image path structures, and if so, that will break the code that extracts the label from the image path.
Hi Adrian,
Thankyou for this tutorial, it is really helpful.
I do have doubts in lines 37, 39 & 41. I added the path for the “train” dataset. But I’m not sure what to add for “–neighbors” and “–jobs”
The
--neighborsis the number of nearest neighbors in the k-NN algorithm. Please read this post again to understand this important variable. As for--jobs, I would leave this as -1 which uses all available processors on your system.how to classify more than one class of images ?
The k-NN algorithm handles classifying more than one class of image. Perhaps I’m misunderstanding your question?
To classify more than one class of images which is better SVM or KNN?
It’s entirely dependent on your dataset, what features you’re extracting, etc. You normally would apply cross-validation to both models and determine which one is better. There is no “one size fits all” solution to computer vision and machine learning.
can I test with other dataset… how and where to give the data set path…error:–dataset
This specific example assumes your directory structure and image file paths follow a particular pattern:
/path/to/dataset/{class}.{image_num}.jpgProvided that your dataset follows this pattern, you can use the exact same code.
In either case, I would suggest you work through either Practical Python and OpenCV or the PyImageSearch Gurus course so you can learn how to apply machine learning to computer vision datasets.
In which terminal have you run? I am using Windows…
Use the terminal/command line utility inside Windows.
Hello Adrian,
I have used the knn classifier for car logo recognition in one of your tutorials, but when i am using sliding window technique for recognising the ‘make’ of a given car from its image , the output depends on size of sliding window and a wrong result is obtained if i change the size of sliding window . Can you help me generalise the size of sliding window.
Do you have any example images of what you are working with? That will better help me point you in the right direction.
For what it’s worth, I’ll be covering how to recognize the make + model of a vehicle inside my book, Deep Learning for Computer Vision with Python.
Be sure to take a look!
A very useful tutorial i like it so much
Ap = argparse.ArgumentParser() I am not understanding what to put in the parameters of –dataset and help=”path to dataset” options. And should I merge the testing1 and training folder into one folder and name it as kaggle-dogs-vs-cats and then the code will split it into training and testing dataset. Thanking you.
If you are having trouble understanding the argument parsing code, please refer to this tutorial. It will help you understand how to parse command line arguments with Python.
As for your second question, no, do not merge the testing1 and training folder. Use only the training folder (the code will automatically perform a training/testing split for you).
How do we know what features were picked up by the classifier to build the model??
The k-NN algorithm doesn’t “pick” any features. It simply computes the distance between the feature vectors.
Hello Adrian
I tried implementing your code on my dataset however i keep getting an error
“ValeuError:setting an array element with sequence”
kindly suggest what to do
Which line threw the error. Please be as descriptive as possible. If you do not provide both the line number and error it’s hard for myself or other readers to provide you with any suggestions.
I have an issue to solve. In some case for example we have two class (eg. Cat and Dog) but some time an user can put a Bird into program to test and the application always gives an answe (of course it is wrong ) and sometime with a very high confidence (eg. 0.9). I want to know how to prevent this case in the real work? Is there any technique for that ? Thank !
Keep in mind that machine learning algorithms aren’t magic. They apply a very specific set of rules. If the bird image is closest to a non-bird image in a Euclidean space than the k-NN algorithm is going to return the closest label. The k-NN algorithm doesn’t care whether the label is right or wrong, it’s just applying a very specific set of rules. I’m not sure what you mean by “prevent” this scenario so perhaps it would be best to limit the control of what the user can upload, but that’s certainly not ideal.
Thank @Adrian for your reply. In fact, I have an issue to classify some types of document. There are a lot of types (about 50 types and millions documents) but my client want to take out only some types automatically. I used deep-learning, it is not difficult to classify if one image in the set of types that I trained but I can’t classify correctly if one type of doc not in the set so that the app can not classify automatically the documents that we desire. And I think we met this issue in many real world app.
Can you help with plotting the results out with matplotlib
I want to merge Local Binary Patterns descriptor with KNN as its classifier. Is that possible with the algorithm above ?
After all, This is a good and interesting tutorial. I learned a lot from your posts. Thank you !
Yes, although I would recommend using this Local Binary Patterns tutorial where we use a Linear SVM. You can easily swap in a k-NN classifier from the Linear SVM with only a few lines.
Additionally, if you’re interested in learning more about image classification using machine learning algorithms, take a look at the PyImageSearch Gurus course where I discuss them both in detail.
Dear Adrian
I needed to link (classify) field-sampled forest biomass and Landsat satellite image using knn. I have no idea of python. I have access to commercial GIS remote sensing software like IDRISI/TerrSet, ENVI, ERDAS Imagine and ArcGIS. Could you show me the steps I should follow to do that in any of these software? I needed it very very urgently please. I would highly appreciate your kind effort.
Hey Red — I do not have any experience with the software you mentioned. This blog uses the Python programming language to write software to understand the contents of an image. I’m sorry I could not be of help here.
hi adrian thank you i implement knn by my self
http://cs231n.github.io/assignments2017/assignment1/
Thanks Adrian for your great effort. I am so confused in KNN to classify images. What I understand that in training phase we store all training images and its corresponding labels. And in testing phase we compare or apply Euclidean distance measure between the test image and all other training images and then we have score for that, Is this right? what is the role of K = 1 to determine the class of the test image?
The role of the “K” value is the number of nearest neighbors to consider when making the classification. Setting K=1 will use the label of the closest vector (according to the distance metric) to label the test image. If we set K=3 then the 3 closest neighbors (again, according to the distance metric) will be used to “vote” on which label they believe the test image is.
Hello Adrian
I’m doing a project on Bird species identification from an image and using the dataset ”Caltech-UCSD Birds-200-2011 (CUB200-2011). It contains about 200 bird species. I’m not getting how to start or where to start from”. I’m getting confused with 200 classes . I have implemented this cat vs dog project but not getting how should I use this code as reference for my project. So please, can you suggest me something, that I can use to get starter for my project. I want to classify the input image using k-nn algorithm.
I am extremely thankful to you for your blog.
The first step is to modify Line 60 to extract the class label from the image path. I don’t remember the exact directory structure of the USCD Birds dataset so you might need to do some debugging.
For what it’s worth, I demonstrate how to work with image datasets, parse class labels, and build your own machine learning + deep learning models inside my book, Deep Learning for Computer Vision with Python. I believe this would be an excellent starting point for you on the project.
I am getting an error when I am testing a single image. Can anyone help me ASAP.
What is the exact error you are getting? If your error includes a lot of terminal output create a GitHub Gist and link to it from your comment.
Hello Adrian,
what changes to the code should I make, if I want to run this script in spyder instead of terminal?
I’m not familiar with the Sypder IDE but if it’s anything like PyCharm you need to change the “Project Interpreter” if you are using a Python virtual environment. You should also replace any command line arguments with hardcoded values. If you’re new to command line arguments refer to this tutorial.
Hi Adrian, I am grateful for your tutorial. Can I ask you one question, with this technique of features extraction is it suitable to use another algorithm such as Support Vector Machine or Random Forest instead of kNN ( using scikit-learn). Thanks for your help.
Absolutely!
Hi Adrian, can I use this technique for other classification purpose such as car an non-car classification in parking lot. Thank you very much or your help.
That really depends on your image dataset. I would actually recommend trying to train a HOG + Linear SVM detector to detect car/non-car parking spots.
Hi Adrian, I would like to recognize (or classify) several different types of object in the same image by using sift (or cnn)
I actually cover both SIFT recognition and deep learning recognition inside the PyImageSearch Gurus course. I would suggest starting there.
I managed to get through all the steps although when I run the program I get this error
usage: knn_classifier.py [-h] -d DATASET [-k NEIGHBORS] [-j JOBS]
knn_classifier.py: error: argument -d/–dataset is required
I don’t seem to know how to fix it
Make sure you educate yourself onc command line arguments. From there you’ll be okay.
Hi Adrian, I’m relatively new to python. If I wanted to edit the code to classify using hist and pixels (or some other feature) to improve accuracy how would I go about that?
Exactly which extracted feature you use is heavily dependent on your dataset and what exactly what you are trying to quantify with the image. If you’re new to Python and the world of computer vision and OpenCV I would suggest you work through Practical Python and OpenCV. The book is a gentle guide to learning the fundamentals of computer vision and I have no doubt it will help you as well.
Hey Adrian, I have 2 doubts:
1. Can you explain why we use argument parsing? Can I run the same code without argument parsing? If yes, how to do so?
2. How can I run the same data set through an SVM classifier?
1. I cover argparse and command line arguments in this guide.
2. Refer to the scikit-learn SVM documentation as it will show you how to use the SVM classifier.
First of all thanks Adrian Sir for awesome tutorial.
I want to know how i can apply predictions on other images by using this trained model ?
And 2nd thing i want to train model for 3 different classes .
Is it possible using this code ?
1. You can use the
model.predictfunction to make predictions on images outside the original dataset. Just extract color histograms for the new images and pass them through the “predict” method.2. Yes, but you would need to organize your images better. The default dogs vs. cats dataset doesn’t include subdirectories for better organization. Take a look at this post for an example of how I like to organize my datasets.
Thanks Sir…
Thank you for you great post. I actually bought the Imagenet bundle of your book and I have just started with the KNN example.
I have one question regarding KNN representation of data. I know to plot a point on a graph, it must have both x and y axis cordinates.
If the pixels are flattened, it goes to say they are vectors now and have only 1 dimension. How is this plotted Sir?
I may be misunderstanding this concept but I will be glad to recieve your input on this, please. Thank you.
Thank you for picking up a copy of the ImageNet Bundle, Rockson! I hope you are enjoying it!
As far as your question, we don’t plot k-NN data. The example I included here was just a visualization for you to see how data points can be represented in a Euclidean space (and how we can compute the distance between them).
sir,for all the input images i am getting the same histogram accuracy . i have tried for different test case images but the histogram and raw pixel intensity accuracy is same (60%,50%). sir,please rectify the error.
I need the full coverage of deep learning and how it came and what is thr difference between deep learning and convolution neural network plz help it’s my seminar topic
I cover deep learning, CNNs, and their history inside Deep Learning for Computer Vision with Python. If it’s for an important project you should definitely consider investing in a copy.