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Computer Vision-Problem Set 6 Image Classification Solved
In this problem set you will be implementing face recognition using PCA, (Ada)Boosting, and the Viola-Jones algorithm.
Learning Objectives
● Learn the advantages and disadvantages of PCA, Boosting, and Viola-Jones.
● Learn how face detection/recognition work, explore the pros & cons of various techniques applied to this problem area.
● Identify the inherent challenges of working on these face detection methods.
Problem Overview
Methods to be used: In this assignment you will be implementing PCA, Boosting, and HaarFeatures algorithms from scratch. Unlike previous problem sets, you will be coding them without using OpenCV functions dedicated to solve the problem.
Obtaining the Starter Files:
Obtain the starter code from canvas under files.
Programming Instructions
Your main programming task is to complete the api described in the file ps6.py. The driver program experiment.py helps to illustrate the intended use and will output images to verify your results. Additionally there is a file ps6_test.py that will test your implementation.
Assignment Overview
1. PCA
Principal component analysis (PCA) is a technique that converts a set of attributes into a smaller set of attributes, thus reducing dimensionality. Applying PCA to a dataset of face images generates eigenvalues and eigenvectors, in this context called eigenfaces. The generated eigenfaces can be used to represent any of the original faces. By only keeping the eigenfaces with the largest eigenvalues (and accordingly reducing the dimensionality) we can quickly perform face recognition.
In this part we will use PCA to identify people in face images. We will use the Yalefaces dataset, and will try to recognize each individual person.
a. Loading images: As part of learning how to process this type of input, you will complete the function load_images. Read each image in the images_files variable, resize it to the dimensions provided, and create a data structure X that contains all resized images. Each row of this array is a flattened image (see np.flatten).
You will also need the labels each image belongs to. Each image file has been named using the following format: subject##.xyz.png. We will use the number in ## as our label (“01” - 1, “02” - 2, etc.). Create a list of labels that match each image in X using the filename strings.
Next, use the X array to obtain the mean face ( μ ) by averaging each column. You will then use the resulting structure to reshape it to a 2D array and later save it as an image.
Code:
- load_images(folder, size=(32, 32))
Report:
- Mean face image: ps6-1-a-1.png
b. PCA: Now that we have the data points in X and the mean μ , we can go ahead and calculate the eigenvectors and eigenvalues to determine the vectors with largest covariance. See Eigenfaces for
Recognition for more details. Using the equation from the lectures:
N
uT Σu where Σ = M1 ∑(xi − μ)(xi − μ)T i=1
You need to find the eigenvectors u. Luckily there is a function in Numpy linalg.eigh that can do this using Σ as an input.
Select the top 10 eigenvectors (in descending order) according to their eigenvalues and save them. These are our 10 eigenfaces.
Code:
- pca(X, k)
Report:
- Top 10 eigenfaces: ps6-1-b-1.png
c. Face Recognition (classification): Now that we have the PCA reduction method ready, let’s continue with a simple (naive) classification method.
First, we need to split the data into a training and a test set. You will perform this operation in the split_dataset function. Next, the training stage is defined as obtaining the eigenvectors from using the training data. Each image face in the training and test set is then projected to the “face space” using these vectors.
Finally, find the eigenface in the training set that is closest to each projected face in the test set. We will use the label that belongs to the training eigenface as our predicted class.
Your task here is to do a quick analysis of these results and include them in your report.
Code:
- split_dataset(X, y, p)
Report:
- Analyze the accuracy results over multiple iterations. Do these “predictions” perform better than randomly selecting a label between 1 and 15? Are there any changes in accuracy if you try low values of k? How about high values? Does this algorithm improve changing the split percentage p?
2. Boosting
In this part we will classify face images based on the dataset's labeled gender [female or male]. You may find the contents on page 663 of Szeliski’s (2010) book useful for this section (available on T-Square/Resources). Recall from the lectures that the idea behind boosting is to use a combination of “weak classifiers”. Each weak classifier's vote is weighted based on its accuracy.
The Boosting class will contain the methods behind this algorithm. You will use the class WeakClassifier as a classifier ( h(x)) which is implemented to predict based on threshold values. Boosting creates a classifier H(x) which is the combination of simple weak classifiers.
Complete the Boosting.train() function with the Adaboost algorithm (modified for this problem set) :
Input: Positive (1) and negative (-1) training examples along with their labels
Initialize all the weights to wi ← N1 , where N is the number of training examples.
For each training stage j = 1...M :
a) Renormalize the weights so they sum up to 1
b) Instantiate the weak classifier h with the training data and labels.
Train the classifier h
Get predictions h(x) for all training examples x ∈ Xtrain
c) Find εj = ∑wi for weights where h(xi) =/ yi
i
αj = 12ln( 1−εjεj )
d) Calculate
e) If ε is greater than a (typically small) threshold:
Update the weights: wi ← wie−yiαjhj(xi)
Else:
Stop the for loop
After obtaining a collection of weak classifiers, you can use them to predict a class label implementing the following equation in the Boosting.predict() function:
H(x) = sign[∑ αj hj (x)]
j
Return an array of predictions the same length as the number of rows in X .
Additionally you will complete the Boosting.evaluate() function, which returns the number of correct and incorrect predictions after the training phase using Xtrain and ytrain already stored in the class.
a. Using the Faces94 dataset, split the dataset into training ( Xtrain ) and testing ( Xtest ) data with their respective labels ( ytrain and ytest ). Perform the following tasks:
1. Establish a baseline to see how your classifier performs. Create predicted labels by selecting N random numbers ∈ {− 1, 1} where N is the number of training images. Report this method’s accuracy (as a percentage): 100 * correct / total .
2. Train WeakClassifier using the training data and report its accuracy percentage.
3. Train Boosting.train() for num_iterations and report the training accuracy percentage by calling Boosting.evaluate().
4. Do the same for the testing data. Create predicted labels by selecting N random numbers ∈ {− 1, 1} where N is the number of testing images. Report its accuracy percentage.
5. Use the trained WeakClassifier to predict the testing data and report its accuracy.
6. Use the trained Boosting classifier to predict the testing data and report its accuracy.
Code:
- ps6.Boosting.train()
- ps6.Boosting.predict(X)
- ps6.Boosting.evaluate()
Report:
- Text Answer: Report the average accuracy over 5 iterations. In each iteration, load and split the dataset, instantiate a Boosting object and obtain its accuracy.
- Text Answer: Analyze your results. How do the Random, Weak Classifier, and Boosting perform? Is there any improvement when using Boosting? How do your results change when selecting different values for num_iterations? Does it matter the percentage of data you select for training and testing (explain your answers showing how each accuracy changes).
3. Haar-like Features
In this section you will work with Haar-like features which are normally used in image classifications. These can act to encode ad-hoc domain knowledge that is difficult to learn using just pixel data. We will be using five types of features: two-horizontal, two-vertical, three-horizontal, three-vertical, and four-rectangle. You may want to review the contents in Lesson 8C-L2 and the Viola-Jones paper. In this section you will complete the HaarFeature class.
a. You will start by generating grayscale image arrays that contain these features. Create the arrays based on the parameters passed when instantiating a HaarFeature object. These parameters are: - type: sets the type of feature among two_horizontal, two_vertical, three_horizontal, three_vertical, and four_square (see examples below).
- position: represents the top left corner (row, col) of the feature in the image.
- size: (height, width) of the area the feature will occupy.
Complete the function HaarFeatures.preview(). You will return an array that represents the Haar features, much like each of the five shown above. These Haar feature arrays should be based on the parameters used to instantiate each Haar feature. Notice that, for visualization purposes, the background must be black (0), the area of addition white (255), and the area of subtraction gray (126). Note that the area occupied by a feature should be evenly split into its component areas - three-horizontal should be split into 3 evenly sized areas, four-square should be split into 4 evenly sized areas (in other words, divide the width and height evenly using int division).
Code: Functions in HaarFeatures.preview()
Report: Using 200x200 arrays.
- Input: type = two_horizontal; position = (25, 30); size = (50, 100) Output: ps6-3-a-1.png
- Input: type = two_vertical; position = (10, 25); size = (50, 150) Output: ps6-3-a-2.png
- Input: type = three_horizontal; position = (50, 50); size = (100, 50) Output: ps6-3-a-3.png
- Input: type = three_vertical; position = (50, 125); size = (100, 50) Output: ps6-3-a-4.png
- Input: type = four_square; position = (50, 25); size = (100, 150) Output: ps6-3-a-5.png
b. As you may recall from the lectures, the features presented above represent areas that would either add or subtract the image area depending on the region “colors”. The white area will represents an addition and the gray area a subtraction. In order to follow the class content, we will work with Integral images. The integral image at location x, y contains the sum of the pixels above and to the left of x, y , inclusive. Complete the function ps6.convert_images_to_integral_images.
Code: ps6.convert_images_to_integral_images(images)
c. Notice that the step above will help us find the score of a Haar feature when it is applied to a certain image. Remember we are interested in the sum of the pixels in each region. Using the procedure explained in the lectures you will compute the sum of the pixels within a rectangle by adding and subtracting rectangular regions. Use the example from the Viola-Jones paper:
Assume the image above is an array ii returned as an integral image. The sum of pixels in D can be obtained by sum(D) = ii(4) − ii(2) − ii(3) + ii(1) where each number corresponds to the pixel position shown in the image. If D was a white rectangle in a Haar feature you will use it as + D , if it was a gray rectangle then it becomes − D .
Important note: Points 1, 2, and 3 in the example above, do not belong to the rectangle D. They cover the areas just before the one in D. Test your approach with the result obtained from D = sum(D) + sum(A) - sum(B) - sum(C).
Complete HaarFeatures.evaluate(ii) obtaining the scores of each available feature type. The base code maps the feature type strings to the following tuples (you will see this in the comments): - "two_horizontal": (2, 1)
- "two_vertical": (1, 2)
- "three_horizontal": (3, 1)
- "three_vertical": (1, 3)
- "four_square": (2, 2)
Code: HaarFeatures.evaluate(ii)
Report:
- Text Answer: How does working with integral images help with computation time? Give some examples comparing this method and np.sum.
4. Viola-Jones
In this part we will use the features obtained in the previous section and apply them in face recognition. Haar-like features can be used to train classifiers restricted to using a single feature. The results from this process can be then applied in the boosting algorithm explained in the Viola-Jones paper. We will use a dataset of images that contain faces (pos/ directory) and refer to them as positive examples. Additionally, use the dataset of images in the neg/ directory which contain images of other objects and refer to them as negative examples. Code the boosting algorithm in the ViolaJones class.
First, we will be using images resized to 24x24 pixels. This set of images are converted to integral images using the function you coded above. Instantiate a ViolaJones object using the positive images, negative images, and the integral images. ViolaJones(train_pos, train_neg, integral_images). Notice that the class contains one attribute to store Haar Features (self.haarFeatures) and another one for classifiers (self.classifiers).
We have provided a function createHaarFeatures that generates a large amount of features within a 24x24 window. This is why it is important to use integral images in this process.
Use the Boosting algorithm in the Viola-Jones paper (labeled Table 1) as a reference. You can find a summary of it below adapted to this problem set.
For each integral image, evaluate each available Haar feature and store these values.
Initialize the positive and negative weights wpos = 21p wneg = 21n where p and n are the number of negative and positive images respectively. Stack them into one list wt
For t = 1,...,T : T : number of classifiers (hypotheses) 1. Normalize the weights, wt,i
wt, i ← n
∑ wt, j j=1
2. Instantiate a and train a classifier hj using the VJ_Classifier class.
For each feature, j , obtain the error evaluated with respect to wt :
εj = ∑wi for cases where hj(xi) =/ yi
i
εj can be obtained from the error attribute in VJ_Classifier
Train hj using the class function train() . This will fix the classifier that returns lowest error εt . 3. Append hj to the self.classifiers attribute.
4. Update the weights:
wt, i ← wt, iβt1−ei βt = 1ε−tεt ei = negative if ht(xi) == yi , ei = positive otherwise
5. Calculate:
αt = log(β1t )
Append it to the self.alphas .
Using the best classifier ht , a strong classifier is defined as: H(x) =
T T
● Positive label if t=1 t=1 t
● Negative label otherwise
a. Complete ViolaJones.train(num_classifiers) with the algorithm shown above. We have provided an initial step that generates a 2D array with all the feature scores per image. Use this when you instantiate a VJ_Classifier along with the labels and weights. After calling the weak classifier’s train function you can obtain its lowest error ε by using the ‘error’ attribute.
Code: ViolaJones.train(num_classifiers)
b. Complete ViolaJones.predict(images) implementing the strong classifier H(x) definition.
Code: ViolaJones.predict(images)
Report:
- Output: ps6-4-b-1.png and ps6-4-b-2.png which correspond to the first two Haar features selected during the training process.
- Text Answer: Report the classifier accuracy both the training and test sets with a number of classifiers set to 5. What do the selected Haar features mean? How do they contribute in identifying faces in an image?
c. Now that the ViolaJones class is complete, you will create a function that is able to identify faces in a given image. In case you haven’t noticed we’re using images that are intended to solve the problem below. The negative directory contains patches of a scene where there is people in it except for their target face(s). We are using this data to bias the classifier to find a face on a specific scene in order to reduce computation time (you will need a larger amount of positive and negative examples for a more robust classifier).
Use a 24x24 sliding window and check if it is identified as a face. If this is the case, draw a 24 x 24 rectangle to highlight positive match. You should be able to only find the man’s face. To that extent you will need to define a positive and negative datasets for the face detection. You can choose images from the pos/ and neg/ datasets or build your own from the man.jpeg image by selecting subimages.
Code: ViolaJones.faceDetection(image, filename=None)
Report: Use the following input images and return a copy with the highlighted face regions.
- Input: man.jpeg. Output: ps4-4-c-1.png
Learning Objectives
● Learn the advantages and disadvantages of PCA, Boosting, and Viola-Jones.
● Learn how face detection/recognition work, explore the pros & cons of various techniques applied to this problem area.
● Identify the inherent challenges of working on these face detection methods.
Problem Overview
Methods to be used: In this assignment you will be implementing PCA, Boosting, and HaarFeatures algorithms from scratch. Unlike previous problem sets, you will be coding them without using OpenCV functions dedicated to solve the problem.
Obtaining the Starter Files:
Obtain the starter code from canvas under files.
Programming Instructions
Your main programming task is to complete the api described in the file ps6.py. The driver program experiment.py helps to illustrate the intended use and will output images to verify your results. Additionally there is a file ps6_test.py that will test your implementation.
Assignment Overview
1. PCA
Principal component analysis (PCA) is a technique that converts a set of attributes into a smaller set of attributes, thus reducing dimensionality. Applying PCA to a dataset of face images generates eigenvalues and eigenvectors, in this context called eigenfaces. The generated eigenfaces can be used to represent any of the original faces. By only keeping the eigenfaces with the largest eigenvalues (and accordingly reducing the dimensionality) we can quickly perform face recognition.
In this part we will use PCA to identify people in face images. We will use the Yalefaces dataset, and will try to recognize each individual person.
a. Loading images: As part of learning how to process this type of input, you will complete the function load_images. Read each image in the images_files variable, resize it to the dimensions provided, and create a data structure X that contains all resized images. Each row of this array is a flattened image (see np.flatten).
You will also need the labels each image belongs to. Each image file has been named using the following format: subject##.xyz.png. We will use the number in ## as our label (“01” - 1, “02” - 2, etc.). Create a list of labels that match each image in X using the filename strings.
Next, use the X array to obtain the mean face ( μ ) by averaging each column. You will then use the resulting structure to reshape it to a 2D array and later save it as an image.
Code:
- load_images(folder, size=(32, 32))
Report:
- Mean face image: ps6-1-a-1.png
b. PCA: Now that we have the data points in X and the mean μ , we can go ahead and calculate the eigenvectors and eigenvalues to determine the vectors with largest covariance. See Eigenfaces for
Recognition for more details. Using the equation from the lectures:
N
uT Σu where Σ = M1 ∑(xi − μ)(xi − μ)T i=1
You need to find the eigenvectors u. Luckily there is a function in Numpy linalg.eigh that can do this using Σ as an input.
Select the top 10 eigenvectors (in descending order) according to their eigenvalues and save them. These are our 10 eigenfaces.
Code:
- pca(X, k)
Report:
- Top 10 eigenfaces: ps6-1-b-1.png
c. Face Recognition (classification): Now that we have the PCA reduction method ready, let’s continue with a simple (naive) classification method.
First, we need to split the data into a training and a test set. You will perform this operation in the split_dataset function. Next, the training stage is defined as obtaining the eigenvectors from using the training data. Each image face in the training and test set is then projected to the “face space” using these vectors.
Finally, find the eigenface in the training set that is closest to each projected face in the test set. We will use the label that belongs to the training eigenface as our predicted class.
Your task here is to do a quick analysis of these results and include them in your report.
Code:
- split_dataset(X, y, p)
Report:
- Analyze the accuracy results over multiple iterations. Do these “predictions” perform better than randomly selecting a label between 1 and 15? Are there any changes in accuracy if you try low values of k? How about high values? Does this algorithm improve changing the split percentage p?
2. Boosting
In this part we will classify face images based on the dataset's labeled gender [female or male]. You may find the contents on page 663 of Szeliski’s (2010) book useful for this section (available on T-Square/Resources). Recall from the lectures that the idea behind boosting is to use a combination of “weak classifiers”. Each weak classifier's vote is weighted based on its accuracy.
The Boosting class will contain the methods behind this algorithm. You will use the class WeakClassifier as a classifier ( h(x)) which is implemented to predict based on threshold values. Boosting creates a classifier H(x) which is the combination of simple weak classifiers.
Complete the Boosting.train() function with the Adaboost algorithm (modified for this problem set) :
Input: Positive (1) and negative (-1) training examples along with their labels
Initialize all the weights to wi ← N1 , where N is the number of training examples.
For each training stage j = 1...M :
a) Renormalize the weights so they sum up to 1
b) Instantiate the weak classifier h with the training data and labels.
Train the classifier h
Get predictions h(x) for all training examples x ∈ Xtrain
c) Find εj = ∑wi for weights where h(xi) =/ yi
i
αj = 12ln( 1−εjεj )
d) Calculate
e) If ε is greater than a (typically small) threshold:
Update the weights: wi ← wie−yiαjhj(xi)
Else:
Stop the for loop
After obtaining a collection of weak classifiers, you can use them to predict a class label implementing the following equation in the Boosting.predict() function:
H(x) = sign[∑ αj hj (x)]
j
Return an array of predictions the same length as the number of rows in X .
Additionally you will complete the Boosting.evaluate() function, which returns the number of correct and incorrect predictions after the training phase using Xtrain and ytrain already stored in the class.
a. Using the Faces94 dataset, split the dataset into training ( Xtrain ) and testing ( Xtest ) data with their respective labels ( ytrain and ytest ). Perform the following tasks:
1. Establish a baseline to see how your classifier performs. Create predicted labels by selecting N random numbers ∈ {− 1, 1} where N is the number of training images. Report this method’s accuracy (as a percentage): 100 * correct / total .
2. Train WeakClassifier using the training data and report its accuracy percentage.
3. Train Boosting.train() for num_iterations and report the training accuracy percentage by calling Boosting.evaluate().
4. Do the same for the testing data. Create predicted labels by selecting N random numbers ∈ {− 1, 1} where N is the number of testing images. Report its accuracy percentage.
5. Use the trained WeakClassifier to predict the testing data and report its accuracy.
6. Use the trained Boosting classifier to predict the testing data and report its accuracy.
Code:
- ps6.Boosting.train()
- ps6.Boosting.predict(X)
- ps6.Boosting.evaluate()
Report:
- Text Answer: Report the average accuracy over 5 iterations. In each iteration, load and split the dataset, instantiate a Boosting object and obtain its accuracy.
- Text Answer: Analyze your results. How do the Random, Weak Classifier, and Boosting perform? Is there any improvement when using Boosting? How do your results change when selecting different values for num_iterations? Does it matter the percentage of data you select for training and testing (explain your answers showing how each accuracy changes).
3. Haar-like Features
In this section you will work with Haar-like features which are normally used in image classifications. These can act to encode ad-hoc domain knowledge that is difficult to learn using just pixel data. We will be using five types of features: two-horizontal, two-vertical, three-horizontal, three-vertical, and four-rectangle. You may want to review the contents in Lesson 8C-L2 and the Viola-Jones paper. In this section you will complete the HaarFeature class.
a. You will start by generating grayscale image arrays that contain these features. Create the arrays based on the parameters passed when instantiating a HaarFeature object. These parameters are: - type: sets the type of feature among two_horizontal, two_vertical, three_horizontal, three_vertical, and four_square (see examples below).
- position: represents the top left corner (row, col) of the feature in the image.
- size: (height, width) of the area the feature will occupy.
Complete the function HaarFeatures.preview(). You will return an array that represents the Haar features, much like each of the five shown above. These Haar feature arrays should be based on the parameters used to instantiate each Haar feature. Notice that, for visualization purposes, the background must be black (0), the area of addition white (255), and the area of subtraction gray (126). Note that the area occupied by a feature should be evenly split into its component areas - three-horizontal should be split into 3 evenly sized areas, four-square should be split into 4 evenly sized areas (in other words, divide the width and height evenly using int division).
Code: Functions in HaarFeatures.preview()
Report: Using 200x200 arrays.
- Input: type = two_horizontal; position = (25, 30); size = (50, 100) Output: ps6-3-a-1.png
- Input: type = two_vertical; position = (10, 25); size = (50, 150) Output: ps6-3-a-2.png
- Input: type = three_horizontal; position = (50, 50); size = (100, 50) Output: ps6-3-a-3.png
- Input: type = three_vertical; position = (50, 125); size = (100, 50) Output: ps6-3-a-4.png
- Input: type = four_square; position = (50, 25); size = (100, 150) Output: ps6-3-a-5.png
b. As you may recall from the lectures, the features presented above represent areas that would either add or subtract the image area depending on the region “colors”. The white area will represents an addition and the gray area a subtraction. In order to follow the class content, we will work with Integral images. The integral image at location x, y contains the sum of the pixels above and to the left of x, y , inclusive. Complete the function ps6.convert_images_to_integral_images.
Code: ps6.convert_images_to_integral_images(images)
c. Notice that the step above will help us find the score of a Haar feature when it is applied to a certain image. Remember we are interested in the sum of the pixels in each region. Using the procedure explained in the lectures you will compute the sum of the pixels within a rectangle by adding and subtracting rectangular regions. Use the example from the Viola-Jones paper:
Assume the image above is an array ii returned as an integral image. The sum of pixels in D can be obtained by sum(D) = ii(4) − ii(2) − ii(3) + ii(1) where each number corresponds to the pixel position shown in the image. If D was a white rectangle in a Haar feature you will use it as + D , if it was a gray rectangle then it becomes − D .
Important note: Points 1, 2, and 3 in the example above, do not belong to the rectangle D. They cover the areas just before the one in D. Test your approach with the result obtained from D = sum(D) + sum(A) - sum(B) - sum(C).
Complete HaarFeatures.evaluate(ii) obtaining the scores of each available feature type. The base code maps the feature type strings to the following tuples (you will see this in the comments): - "two_horizontal": (2, 1)
- "two_vertical": (1, 2)
- "three_horizontal": (3, 1)
- "three_vertical": (1, 3)
- "four_square": (2, 2)
Code: HaarFeatures.evaluate(ii)
Report:
- Text Answer: How does working with integral images help with computation time? Give some examples comparing this method and np.sum.
4. Viola-Jones
In this part we will use the features obtained in the previous section and apply them in face recognition. Haar-like features can be used to train classifiers restricted to using a single feature. The results from this process can be then applied in the boosting algorithm explained in the Viola-Jones paper. We will use a dataset of images that contain faces (pos/ directory) and refer to them as positive examples. Additionally, use the dataset of images in the neg/ directory which contain images of other objects and refer to them as negative examples. Code the boosting algorithm in the ViolaJones class.
First, we will be using images resized to 24x24 pixels. This set of images are converted to integral images using the function you coded above. Instantiate a ViolaJones object using the positive images, negative images, and the integral images. ViolaJones(train_pos, train_neg, integral_images). Notice that the class contains one attribute to store Haar Features (self.haarFeatures) and another one for classifiers (self.classifiers).
We have provided a function createHaarFeatures that generates a large amount of features within a 24x24 window. This is why it is important to use integral images in this process.
Use the Boosting algorithm in the Viola-Jones paper (labeled Table 1) as a reference. You can find a summary of it below adapted to this problem set.
For each integral image, evaluate each available Haar feature and store these values.
Initialize the positive and negative weights wpos = 21p wneg = 21n where p and n are the number of negative and positive images respectively. Stack them into one list wt
For t = 1,...,T : T : number of classifiers (hypotheses) 1. Normalize the weights, wt,i
wt, i ← n
∑ wt, j j=1
2. Instantiate a and train a classifier hj using the VJ_Classifier class.
For each feature, j , obtain the error evaluated with respect to wt :
εj = ∑wi for cases where hj(xi) =/ yi
i
εj can be obtained from the error attribute in VJ_Classifier
Train hj using the class function train() . This will fix the classifier that returns lowest error εt . 3. Append hj to the self.classifiers attribute.
4. Update the weights:
wt, i ← wt, iβt1−ei βt = 1ε−tεt ei = negative if ht(xi) == yi , ei = positive otherwise
5. Calculate:
αt = log(β1t )
Append it to the self.alphas .
Using the best classifier ht , a strong classifier is defined as: H(x) =
T T
● Positive label if t=1 t=1 t
● Negative label otherwise
a. Complete ViolaJones.train(num_classifiers) with the algorithm shown above. We have provided an initial step that generates a 2D array with all the feature scores per image. Use this when you instantiate a VJ_Classifier along with the labels and weights. After calling the weak classifier’s train function you can obtain its lowest error ε by using the ‘error’ attribute.
Code: ViolaJones.train(num_classifiers)
b. Complete ViolaJones.predict(images) implementing the strong classifier H(x) definition.
Code: ViolaJones.predict(images)
Report:
- Output: ps6-4-b-1.png and ps6-4-b-2.png which correspond to the first two Haar features selected during the training process.
- Text Answer: Report the classifier accuracy both the training and test sets with a number of classifiers set to 5. What do the selected Haar features mean? How do they contribute in identifying faces in an image?
c. Now that the ViolaJones class is complete, you will create a function that is able to identify faces in a given image. In case you haven’t noticed we’re using images that are intended to solve the problem below. The negative directory contains patches of a scene where there is people in it except for their target face(s). We are using this data to bias the classifier to find a face on a specific scene in order to reduce computation time (you will need a larger amount of positive and negative examples for a more robust classifier).
Use a 24x24 sliding window and check if it is identified as a face. If this is the case, draw a 24 x 24 rectangle to highlight positive match. You should be able to only find the man’s face. To that extent you will need to define a positive and negative datasets for the face detection. You can choose images from the pos/ and neg/ datasets or build your own from the man.jpeg image by selecting subimages.
Code: ViolaJones.faceDetection(image, filename=None)
Report: Use the following input images and return a copy with the highlighted face regions.
- Input: man.jpeg. Output: ps4-4-c-1.png
1 file (20.6MB)
