hAIr: A Hairstyle Recommender System Using Machine Learning
Sark Xing Ward de Groot Abstract Technical University of Eindhoven Technical University of Eindhoven Choosing a new hairstyle can be a difficult, impactful [email protected] [email protected] decision. Especially envisioning if a haircut would suit the individual is hard. With the analysis responses from Y izhou Liu Lara Leijtens facial recognition APIs and supervised machine Technical University of Eindhoven Technical University of Eindhoven learning, a relation between facial features and [email protected] [email protected] hairstyle is ought to be found in this project, so that a hairstyle recommender system, called “hAIr”, can be created. The system recommends hairstyles that suit the individual’s characteristics. This is based on a neural network learning algorithm, which is trained with features, extracted from 1060 images of people, relating to 53 different hairstyles. The trained network reaches an accuracy of 28.10% when validated with images that were not used for training. This can be improved by trying different combinations of input variables, or using a different conversion for the values that were gained from the APIs. It is also possible that the APIs are not completely accurate. A third possibility for improvement would be to use a different learning algorithm, such as k-Nearest Neighbors or naive Bayes.
Introduction about themselves, once they are presented with an Context option. To decide if they like the style or not can be Going to the hairdresser can come with difficult decision assessed by looking at another person that has that making. A new haircut is a decision that cannot be haircut. However, whether it will suit them personally is changed for at least six weeks. So, for this amount of hard to assess, so this is the challenge that is aimed at. time it is visible to everyone that a wrong decision has been made. Especially for occasions where a good Competition haircut is essential, such as a job interview, the wrong Currently there are several products in development haircut can badly influence a first impression. Not only that are similar to the envisioned product for this the impact of the decision makes it hard to choose, also project. However, not one of them is dominating the the large amount of options causes difficulty. There are market, so there is still room for competitors. so many different hairstyles that it can seem like an impossible decision to make. Face It [18] is an application that detects the shape of person’s face. Hereafter, the user has to provide At a barbershop or hairdresser there are often personal information, such as occupation. Based on magazines provided with examples of haircuts. these combined input features, a recommendation for However, these examples are not personalized. So hairstyle, including facial hair, is given. This differs from when it is boiled down to a selection that fits the the envisioned product, because the user does not need client’s style, it is not guaranteed that the haircuts will to provide any information other than a picture. This look good on the client. This is because a haircut can increases the ease of use. However, Face It has some look different on different people. An experienced additional features that could be added in later barber or hairdresser could give advice on this, but development of the envisioned product, such as a list of they are hard to find and often expensive. nearby hairdressers and recommended hair products.
Challenge The app Hair Color of ModiFace [14] focusses on hair The challenge that is ought to be tackled here is to color change. It gives a real time visualization of a make it easier for people to decide on a new hairstyle different hair color mapped on a person’s hair through by minimizing the risk and limiting the options. The live camera input. This could be interesting for system is meant for both men and women of all ages collaboration, since the product of this project does not that want to try something new, but do not know what focus on hair color, but on hair style. haircut would suit their facial features and do not have an experienced hairdresser who can advise them on the Both Dreambit [5] and FaceApp [7] have a functionality matter. Hairstyle does not only correlate with facial to map different hairstyles on images of the user’s face. features, but also with other elements, such as a This is not the area that is currently focused on, but the person’s clothing style or a person’s current hairstyle. technology would be interesting to apply in later However, this is often something that people do know development.
Approach if this has led to the correct target (supervised Envisioned solution learning). Depending if this is correct or not, the To complete the challenge, the envisioned solution is an network adjusts the weights of its next guess. The delta app that provides users with a limited amount of rule is applied so that the amount of adjustment is hairstyles which will all suit the user’s face. This will gradually descending. The delta rule is a special case of minimize the risk of choosing a hairstyle that does not the more general backpropagation algorithm that was look good and makes it easier to choose. It is expected used for this project. that it can be determined whether a hairstyle will look good or not on a person, based on facial features and Additionally, other than using a NN, the k-Nearest characteristics such as age and gender. However, this Neighborhood (kNN) algorithm could also be fitting for relation is not easily grasped in a formula or logic rule. this problem. The kNN classification maps every input Therefore, machine learning is used to find matches. in multidimensional space. The position is determined More specifically, supervised machine learning, because by the input features, in this case the shape of the face hairstyles have existing names and do not need to be or the skin color. This position is labeled with the categorized by the machine. target, in this case the hairstyle. When a user provides new input features, a position is again determined Learning algorithm based on their values. Since there is no label provided, As learning algorithm for the system, a Multi Layered the system will check which point or points are closest Perceptron (MLP) is chosen, which is widely used in to it. The amount of points that are taken into account many similar recommender cases to solve realistic is called the k-value, and it is usually a small integer problems [11][17]. An MLP is a type of Neural Network number. So, basically the system finds the person that (NN) that deals with non-linearly separable data. An the user looks like most and advises the same hairstyle artificial neural network is a learning algorithm as this person. consisting of nodes and weighted connections. The nodes are grouped in layers. For an MLP there is one Furthermore, naive Bayes could be used as a classifier input layer that contains the input variables, one or as well. It uses a probabilistic model to check multiple hidden layers and one output layer that independence between features and is based on Bayes’ contains the output targets. theorem. A simplified dependence applied to this case would be: what is the chance of a bobline hairstyle Each node from one layer is connected to all nodes in when a person has an oval face? This can be calculated the next layer. Training is done by using Neuroph [15], by multiplying the chance on a bobline hairstyle by the a lightweight Java neural network framework. During chance on an oval face when someone has a bobline the training, the network looks for the optimal hairstyle, and dividing the answer by the chance that a combination of weights. This works as following. The person has an oval face with any hairstyle. Repeating network takes one person’s input variables and target this for all possible independencies, and combinations, and guesses what the weights could be. Then, it checks
e.g. the chance on a bobline when a person has an oval attributes, the researchers of Hairstyle30k manually face ánd a pointy nose, results in a prediction model. coined and annotated 64 labels for different hairstyles.
However, amongst the researchers there is no Since all these images are labelled with the target data experience with these two latter algorithms. Therefore, (hairstyle), it is practical to use the same images as it would take a lot of time to find out how the three input. However, the exact input variables need to be algorithms work and design a test to compare the three specified. The aim is to relate facial features to the algorithms. Hence, a pilot test for the NN was hairstyles, so the facial features of the people in the conducted to investigate whether this learning 30k database need to be collected. To achieve this, algorithm would be fitted for the task at hand. there are several options available: 1) looking at the images and noting the features by hand, either done by Data acquisition the researchers or by experts (hairdressers), 2) To train a supervised AI, it must be fed with correct creating a machine learning algorithm that extracts the combinations of input as well as output variables. The features from the images, and 3) using an existing API input variables are facial features and the output that extracts the features. The third option was variables, the targets, are varying hairstyles. For this, selected because option 1 and 2 are time-consuming. there are several databases available. Figaro 1k [20], a Furthermore, based on the limited experience of the hairstyle dataset, consists of seven hairstyles (straight, researchers, 1 and 2 would not guarantee good results wavy, curly, kinky, braids, dreadlocks and short) with when performed by the researchers. 150 images for each hairstyle. For Figaro 1k, it includes only seven haircuts, which is not sufficient for training a Moreover, when new input is presented by the user, it Neural Network and too general to be recommended for would be easier to upload a photo than to write down customers. The human face and hair dataset from Open all your personal facial features. Since the average user Images V4 [16] (powered by Google AI) can provide does not have the experience. Thus, the input data to abundant and copyright-free images in the natural train the system is gathered in the same way the data settings. However, it does not provide hairstyle labels. is gathered in the use setting. The data used for this system is derived from the Hairstyle30k database [21], which is the largest dataset API SELECTION regarding the number of hairstyle classes within the As explained above, the input data was extracted from community. It contains 12,076 images, which are the images of the hairstyle database using an API. distributed under 64 labels. The hairstyle classification BetaFace API [2], an open API for face recognition, was method developed by Hairstyle30k was based on found initially and seemed perfectly fitted for the goal. people attributes and face attributes by using 1) hand- However, the accuracy on several elements, such as crafted visual features, such as SIFT [12] and LBP [19], age, was not very high. Therefore, a second search for 2) the recent deep features and 3) multi-task methods APIs was conducted. for learning facial attributes. According to the
There are several face recognition APIs available. All Combining these two API’s, enough relevant have their own specialties and limitations. To select information of the images is extracted to make a which one was best fitting for this case, multiple APIs recommendation. were considered: Lambda Face Recognition API [10], FaceX [8], Animetrics [1], Azure Face API [13], IBM Input variables visual recognition [9], Face++ [6], and DeepFace [3]. The features that were selected as input variables can Every API was evaluated on seven available input be viewed in table 2. These were determined based on options: gender, ethnicity, face characteristics, face the following reasons. General features, being gender landmarks, facial hair, glasses and age. These seven and age, were included because they are expected to were all believed to be in relation to the hairstyle of a deliver a common ground, as hairstyles are usually person. In table 1 can be viewed which categories were fitting for either men or women and associated with a enabled by which API. Azure Face API scores best, certain age. Facial hair was also included, since this since it includes most of the features. should match the hairstyle. As facial hair was not part of the hairstyle categories in the database, it is not part However, not one of the considered APIs was able to of the output recommendation. Glasses and make-up distinguish face characteristics, such as face shape. are features that could change on a day-to-day basis. Since it is the core element in the relation between face However, it is assumed that people who wear glasses and hairstyle, it was chosen to use a combination of or make-up do so consistently, and therefore these are Azure Face API and BetaFace API. The Azure Face API used as input variables. All other facial features are can detect faces in images and extract face-related features that are consistent for the user and that attributes needed for our recommender system. The cannot be changed easily. attributes include gender, age, facial hair and glasses. The API of BetaFace is used to retrieve information on There were more features extracted from the the face shapes detected in the dataset’s images. database’s images through the APIs. Most of them were either unrelated (e.g. blurry image), temporary Features Lambda FaceX Animetrics Azure IBM Face++ DeepFace Gender Ethnicity Face characteristics Face landmarks Facial hair Glasses Age
Table 1. API comparison based on seven features.
(clothing color), or on a non-dichotomous nominal scale 17 Heavy yes/no BetaFace that cannot be mapped trustworthy (e.g. race with a make-up value of Asian-middle-eastern, Asian, African American, 18 High yes/no BetaFace Hispanic, white, middle eastern, or other). A list of all cheekbones possible features that could be extracted through 19 Narrow eyes yes/no BetaFace Betaface and through Azure can be found in Appendix 1 20 Pale skin yes/no BetaFace and 2, respectively. These lists include the 21 Straight hair yes/no BetaFace corresponding values. 22 Wavy hair yes/no BetaFace # Element Value Mapping API 23 Chin size extra small, small, 0, 0.25, 0.5, BetaFace average, larger, 0.75, 1 1 Gender male/female 1, 0 Azure extra large 2 Age 0-100 age/100.0 Azure 24 Eye distance extra close, close, 0, 0.25, 0.5, BetaFace 3 Moustache 0-1 - Azure average, far, extra 0.75, 1 4 Beard 0-1 - Azure far 5 Sideburns 0-1 - Azure 25 Head width extra narrow, 0, 0.25, 0.5, BetaFace narrow, average, 0.75, 1 6 Glasses No glasses, 0, 0.5, 1 Azure wide, extra wide Reading glasses, Sunglasses 26 Mouth width extra small, small, 0, 0.25, 0.5, BetaFace average, larger, 0.75, 1 7 Oval face yes/no Boolean BetaFace extra large if yes: 0.5 + 8 5 o’clock yes/no confidence/2 BetaFace Table 2. All 26 features that were selected for training the NN, shadow including their corresponding values, the mapping method and if no: 0.5 - the API that was used to extract the features. 9 Arched yes/no confidence/2 BetaFace eyebrows It can be discussed whether wavy hair and straight hair 10 Bags under yes/no BetaFace as input features are chosen well, since they describe eyes the hairstyle, which is the targeted outcome. 11 Bangs yes/no BetaFace 12 Beard yes/no BetaFace Process 13 Big lips yes/no BetaFace Pilot test To see if the NN would be able to recognize hairstyles 14 Big nose yes/no BetaFace based on face features, a pilot test was designed. In 15 Bushy yes/no BetaFace case it would give positive results, this type of eyebrows algorithm would be used for the final system as well. If 16 Double chin yes/no BetaFace it would give negative results, the two other options, kNN and naive Bayes, would be explored.
For this test, two different people, Harry and Sam, were Data filtering selected. The features that were taken into account The data provided 64 different targets (hairstyles). were the roundness of their head, the whiteness of However, not all of these were fitting for the method their skin, whether they have glasses or not and used in this project. The following hairstyles were whether they have a beard or not. Both Harry and Sam eliminated, because there were less than 20 images had three possible hairstyles, one of which was the that could be recognized by both APIs. In most cases same for both. This can be viewed in figure 1. this was because the pictures were taken from the back of the head, since the hairstyle was only visible from Hundred Sams and Hundred Harrys were generated, the back. Therefore, no facial features could be seen. following the intervals shown in table 3. These 200 Following this reasoning, 11 hairstyles were eliminated: Figure 1. Examples of Sam (top ‘people’ were used to train an MLP in Neuroph. After long hair, devilock, ducktail, horseshoe flattop, rattail, row) with hairstyle 1, 2 and 3 and the system was trained, the result was validated by French twist, tonsure hair, quiff, fontange, Jeong-eun Harry (bottom row) with hairstyle 1, using newly generated Harrys and Sams. This showed Kim hair, and khokhol hair. Elimination of these 4 and 5. that the system could always recognize whether the hairstyles resulted in 53 usable hairstyles. person was a Sam or a Harry. The error rate of the training was 0.03, and in the validation with 10 Of each hairstyle, the first 20 images were selected for different Sam and Harry, the network proposed a fitting training the network. Pictures with more than one hairstyle. recognized face were deleted from the training data, because the hair style is only determined for one of the Person Roundness Whiteness Glasses Beard faces. These were replaced by the next picture (number 21) in the database. Sam Random Random 0 1 (0.75 – 0.95) (0.75 – 0.95) Neural network experiments Harry Random Random 1 0 The 26 inputs gathered from both API’s, combined with (0.25 – 0.45) (0.75 – 0.95) the filtered output class data of 53 outputs, formed the base of the MLP neural network. The amount of hidden Table 3. Features for 100 artificially generated Sams and 100 artificially generated Harrys. layers and their amount of nodes still needed to be defined. Therefore, the team explored which neural This showed that an MLP is able to categorize faces and network structure resulted in the most accurate and find a fitting hairstyle. Therefore, no more time was confident outcomes. Having more than one hidden wasted on trying out different algorithms, such as kNN layer within an MLP network does not influence these or naive Bayes, but all time was invested in developing outcomes [4]. That is why having multiple hidden a system using this learning algorithm. layers was eliminated. Then, the amount of nodes in the hidden layer was experimented with, as well as with the learning rule and amount of iterations. Within Neuroph Studio, all the different combinations were
tried out. The nodes of the hidden layer varied between recommendation shows the three best fitting results, 5, 10, 15, 20, 35, 40, 50, 75 to 100 nodes, all using according to the image that has been put in table 5. different learning rules. To prevent the outputs to be 0, a bias rule was applied within the inputs. Training validation results No. of recommendations Accuracy rate (%) Training 1 64.60 The amount of iterations was set to 50,000. Each NN 3 65.10 used the same dataset of 20 images per class (1060 5 66.30 images in total), to train the network. A learning rate of 0.2 and a momentum of 0.7 was applied for each NN. 10 73.00
These experiments showed that having 50 nodes in the Table 4. Accuracy rate related to number of recommendations hidden layer while using backpropagation as learning by validation with trained data. rule gave the lowest error rate (0.157), see figure 2. Trained network validation results
Validation No. of recommendations Accuracy rate (%) After all the networks completed training for 50,000 3 28.10 iterations, they were all validated using two different 5 41.20 methods. One was validating the systems by inputting 10 58.80 the used images, to see if the system was correctly trained. The other was utilizing three images of all 53 Table 5. Accuracy rate related to number of recommendations by validation with fresh (validation) data. hairstyles which were not used to train the system. In this way, the accuracy of the system can be measured. As seen in both tables 4 and 5, the accuracy of the
The validation of the network with our highest accuracy system increases when giving more recommendations. Figure 2. Total network error graph rate had a total network error of 0.18. This network This is a logical outcome, since giving more of NN training with 50 nodes and uses 22 input nodes, 50 nodes in the hidden layer, and recommendations would also imply having a higher backpropagation as learning rule, 53 output nodes. chance that the right hairstyle recommendation is showing results for the first 10.000 included as output. out of 50.000 iterations. The graph In order to validate whether the system had been continued descending slowly until an error of 0.157 was reached. trained correctly, the same images used to train the Results network were put into the system again. This method After all the training and validation tests, one final
showed an accuracy rate of 64.60% (table 4). neural network which has the highest accuracy rate was chosen for the recommender system ‘hAIr’. This Putting in unused, labeled images of the same dataset system is explained in this video: which was used to train the network showed a https://www.youtube.com/watch?v=H_Gx9_LJBZs&feat recommendation accuracy rate of 28.10%. The ure=youtu.be. This video shows the working prototype.
Concluding, we can say that our hairstyle recommender outputs of the API’s are used as input for our neural system has an accuracy rate of 28.10%. Even though network. this is not the desired outcome, it can be argued why the system shows these results. These are further Input conversion discussed in the section Discussion. Currently, the input features were converted linearly. However, a different conversion could have led to Implementation different results. For example, a Sigmoid function could Barber be applied. The Sigmoid function, or logistic curve, The primary implementation area of the system is at a converts a set of integers into a set of decimal numbers barbershop. A concept video has been made to show between 0 and 1. It would be wise to try this in future the envisioned implementation, as can be viewed here: development, to see if it improves the accuracy of the https://www.youtube.com/watch?v=aNsfy- network. b7bcY&feature=youtu.be. Note that this video does not show the working prototype. Different learning algorithm In hindsight, it seems that kNN or naive Bayes would Wigs have been better fitting to the problem. However, this Another field of implementation is to use the system in is still speculation. For future development, it is hospitals or wig stores. Because the system is focused important these options are explored, since the NN on facial features, recommendations can be provided does not currently reach the desired accuracy. The for people without hair as well. downside of kNN is that the training time will increase every time new data is added. This is only the case Discussion when a feedback loop is used. The accuracy of the trained network was 28.10%. This low success rate can be explained or improved by Future work several aspects. These are discussed in the following To improve the implementation and relevance of the paragraphs. system, several ideas are suggested for further development, as can be read underneath. Inaccuracy API As seen in the results section, our hairstyle Unfitting recommendations recommender system does not have a high accuracy Currently, the system does not consider a person’s levels for recommending the right hairstyle according to current hairstyle, so it is possible that a one’s facial features. We argue that the low accuracy recommendation is given that is not fitting at the could be caused by the inaccuracies of the API’s that moment. For example, a hairstyle that requires longer were used. The API gives an output, together with a hair than the person currently has. This can still be confidence. If the API outputs are of low confidence, it interesting to know, since the user can decide to wait influences the overall recommendations, since the until his/her hair has grown longer. There are other
possible mismatches, such as a difference in color or can influence the overall outputs. Whenever for hair type (straight or curls). This can be changed, by example a customer wants a hairstyle that has not coloring the hair or getting permanent curls or been defined yet by the labels in the dataset, a new straightening. However, this is more expensive than a label may be created. Real-time updating the system regular haircut. would make our system more connected.
There are several ways in which this can be solved. The Feedback loop system’s concept can be changed from a recommender Currently, the system functions with a NN that is to a matchmaking service, so that the user can select trained prior to use. In the future, when the system is preferred hairstyles, after which the system checks if in use, a feedback loop can be set up. Based on the the provided hairstyles match the facial features. hairstyle users select by clicking “I want this!” in the Another possibility is that the user can answer app, the network can be adjusted based on their facial questions about the preferred price of the hairstyle. features in relation to their desired hairstyle. When this is implemented, the neural network needs to be trained Style again. By doing so, trends in hairstyle will get The system is set up in such a way that it is very easy incorporated as well, since popular hairstyles will be and fast to use, because only one image is needed to chosen more often. receive a recommendation. Also, three options are recommended out of 53 options. This combination of Current hairstyle choices leads to a recommendation for the average An option is to take into account a person’s current person. Therefore, the system will not work for people haircut and recommend a new hairstyle based on with a distinctive style. To include those people in the people who changed their hairstyle and previously had target group, the style of the user can be gained from the same haircut. Currently, this was not integrated, user input apart from the image. This input is used in because data was not available to the researchers. addition to the facial features. Another option would be However, when data is gathered (e.g. from to let users select the hairstyles they are interested in barbershops) this might have a positive influence on and thereafter let the system recommend which is best the accuracy of the model. However, it can often be the fitting with their features. Moreover, additional case that people who go to the hairdresser want to hairstyles can be added to increase flexibility. keep the same hairstyle, because they are still happy with it. Since this is system is only meant for people Trends who want a change, either these data should be left The system as it is designed right now does not take out, or the data should be used in combination with a into account trends in hairstyles. However, it would be score of happiness about the current hairstyle. When interesting to look at how the system could develop using the system, users should also give such a score over time. Whenever our system is implemented at as input, as well as their current hairstyle. hairdressers, new data can be fed to the system, which
Acknowledgements 9. IBM: Watson visual recognition. Available from This project would not have been possible without the https://www.ibm.com/watson/services/visual- help of other parties. First of all, the hairstyle database recognition/. was made available to us by Yanwei Fu. Secondly, we 10. Lambda Labs: Face recognition API. Available from would like to thank our teachers and peer students for https://lambdalabs.com/face-recognition-api. their critical feedback, which helped us improve the 11. Sangjae Lee and Joon Yeon Choeh. 2014. Predicting product. Especially, we would like to thank our coach, the helpfulness of online reviews using multilayer Erik van der Spek, for his advice. Furthermore, we are perceptron neural networks. Expert Systems with thankful for expert input on the topic of Artificial Applications41, 6 (2014), 3041–3046. Intelligence from Bart Bussmann. Lastly, we are 12. David G. Lowe. 2004. Distinctive Image Features grateful for the hospitality of Marjan, who let us shoot from Scale-Invariant Keypoints. International our video at the hair salon ‘For Your Hair’. Journal of Computer Vision60, 2 (2004), 91–110. 13. Microsoft Azure: Face. Available from References https://azure.microsoft.com/en- 1. Animetrics: Face recognition. Available from us/services/cognitive-services/face/. http://animetrics.com/. 14. ModiFace: Hair Color. Available from https://itunes. 2. BetaFace: Open API for face recognition. Available apple.com/nl/app/hair-color/id485420312. from https://www.betafaceapi.com/. 15. Neuroph: Java neural network framework. Available 3. Deepface: Face detection, verification, recognition from http://neuroph.sourceforge.net/. and emotion analysis APIs. Available from 16. Open images dataset V4 + Extensions. Available https://deepface.ir/. from https://storage.googleapis.com/openimages/ 4. Doug (2018, July 22). How to choose the number of web/index.html. hidden layers and nodes in a feedforward neural 17. Octavio Salcedo Parra, Gustavo Garcia, and Brayan network? Message posted to Reyes. 2014. Traffic forecasting using a multi layer https://stats.stackexchange.com/q/1097. perceptron model. Proceedings of the 10th ACM 5. Dreambit. Retrieved from digitaltrends.com/ symposium on QoS and security for wireless and photography/dreambit-computer-vision. mobile networks - Q2SWinet 14(2014). 6. Face++: Cognitive services. Available from 18. Pallab Paul. 2017. Face It: The artificially intelligent https://www.faceplusplus.com/. hairstylist. Intel AI Academy. Retrieved from 7. FaceApp Inc: FaceApp, AI Face Editor. Available https://software.intel.com/en-us/articles/face-it- from https://itunes.apple.com/us/app/faceapp-ai- the-artificially-intelligent-hairstylist. face-editor/id1180884341. 19. Toon De Pessemier, Kris Vanhecke, and Luc 8. FaceX: Face recognition API for your apps. Available Martens. 2016. A scalable, high-performance from https://www.facex.io/. Algorithm for hybrid job recommendations. Proceedings of the Recommender Systems Challenge on - RecSys Challenge 16(2016).
20. Muhammed Umar Riaz, Michele Svanera, Sergio blurry yes, no Benini. 2017. Multi-class hair image database with brown hair yes, no ground truth: Figaro extension. Available from: http://projects.i-ctm.eu/it/progetto/figaro-1k. bushy eyebrows yes, no 21. Weidong Yin, Yanwei Fu, Yiqing Ma, Yu-Gang Jiang, chin size extra large, large, average, Tao Xiang, and Xiangyang Xue. 2017. Learning to small, extra small Generate and Edit Hairstyles. Proceedings of the chubby yes, no 2017 ACM on Multimedia Conference - MM 17 color background RGB hex color value, for (2017). example 4f3530
color clothes middle RGB hex color value Appendices Appendix 1: Possible classifiers and return values color clothes sides RGB hex color value Betaface API color eyes RGB hex color value From Betaface API, all possible classifiers with color hair RGB hex color value corresponding return values are listed. These values are retrieved from https://www.betafaceapi.com/ color mustache RGB hex color value wpa/index.php/documentation on 2 February 2019. color skin RGB hex color value double chin yes, no Classifier Return value eyebrows corners extra low, low, average, 5 o’clock shadow yes, no raised, extra raised age approximate age value eyebrows position extra low, low, average, high, arched eyebrows yes, no extra high eyebrows size extra thick, thick, average, attractive yes, no thin, extra thin bags under eyes yes, no eyes corners extra low, low, average, bald yes, no raised, extra raised eyes distance extra far, far, average, close, bangs yes, no extra close beard yes, no eyes position extra low, low, average, high, big lips yes, no extra high big nose yes, no eyes shape extra round, round, average, thin, extra thin black hair yes, no gender male, female blond hair yes, no glasses yes, no
glasses rim yes, no nose width extra wide, wide, average, narrow, extra narrow goatee yes, no oval face yes, no gray hair yes, no pale skin yes, no hair beard none, short, thick pointy nose yes, no hair color type black, blond, red, brown, brown light, not natural light, race asian-middle-eastern, asian, not natural african-american, hispanic, white, middle eastern, other hair forehead yes, no receding hairline yes, no hair length none, very short, short, average, long, very long rosy cheeks rosy cheeks hair mustache none, short, thick smile yes, no hair sides very thin, thin, average, thick sideburns yes, no hair top very short, short, average, straight hair yes, no thick, very thick teeth visible yes, no head shape extra heart, heart, average, rect, extra rect wavy hair yes, no head width extra narrow, narrow, wearing earrings yes, no average, wide, extra wide wearing hat yes, no heavy makeup yes, no wearing lipstick yes, no high cheekbones yes, no wearing necklace yes, no mouth corners extra low, low, average, raised, extra raised wearing necktie yes, no mouth height extra thick, thick, average, young yes, no thin, extra thin
mouth open yes, no Appendix 2: Possible classifiers with return values mouth width extra wide, wide, average, Microsoft Azure small, extra small In the table below, all possible classifiers in the mustache yes, no category “face attributes”, as retrieved from https://docs.microsoft.com/en-us/rest/api/ narrow eyes yes, no cognitiveservices/face/face/detectwithstream#faceattri nose shape extra straight, straight, butes on 2 February 2019, are listed with their average, triangle, extra corresponding return values. triangle
Classifier Return value accessories glasses, headWear, mask age 0 - 100 blur low, medium, high emotion anger, contempt, disgust, fear, happiness, neutral, sadness, surprise exposure 0 - 1 facial hair moustache, sideburns, beard gender male, female glasses noGlasses, readingGlasses, sunGlasses hair bald, hairColor, invisible headPose pitch, roll, yaw makeup boolean eye make-up, boolean lip make-up
noise 0 - 1 occlusion eyeOccluded, forheadOccluded, mouthOccluded smile 0 - 1 Appendix 3: All 64 hairstyles Source: Yin, Weidong, et al. "Learning to generate and edit hairstyles." Proceedings of the 2017 ACM on Multimedia Conference. ACM, 2017.
Appendix 4:Codes img_url.append('https://raw.githubusercontent.com/sarkrui PYTHON CODE FOR EXTRACTING DATA FROM MICROSOFT AZURE /Hairstyle60k/master/Dataset/' + hairstyle[i] + '/IMG_'+ str(j+1) + '.jpg') FACE API result = CF.face.detect(img_url[j], face_id=True, # coding: utf-8 landmarks=False,
attributes='age,gender,facialHair,glasses') # In[ ]: print (result)
result = json.dumps(result)
result = json.loads(result) import cognitive_face as CF f = csv.writer(open("test.csv", "a")) import time if result == []: import csv f.writerow([hairstyle[i], 'IMG_'+str(j+1), 0, import json 0, 0, 0, 0, 0])
else: hairstyle = ['Aaron_Kwok', 'Afro', 'Bald', 'Beehive', for result in result: 'Bob', 'Bouffant', 'Bowl_Cut', 'Bun', 'Caesar', f.writerow([hairstyle[i], 'Chonmage', 'Comb_Over', 'Cornrows', 'Crew_Cut', 'Crop', 'IMG_'+str(j+1), result["faceAttributes"]["gender"], 'Croydon_Facelift', 'Curly', 'Curly_Hair', result["faceAttributes"]["age"], 'Curtained_Hair', 'Cute_Ponytails', 'Devilock', result["faceAttributes"]["facialHair"]["moustache"], 'Dreadlocks', 'Ducktail', 'Emo_hair', 'Fauxhawk', result["faceAttributes"]["facialHair"]["beard"], 'Flattop', 'French_Braid', 'French_Twist', 'Hi-top_Fade', result["faceAttributes"]["facialHair"]["sideburns"], 'Hime_Cut', 'Horseshoe_Flattop', 'Induction_Cut', result["faceAttributes"]["glasses"]]) 'Jimmy_Lin_Hairstyle', 'Layered_Hair', time.sleep(3) 'Liberty_Spikes_Hair', 'Long_Hair', 'Medium-Length_Hair', 'Men_Pompadour', 'Men_With_Square_Angles', 'Mohawk', 'Mop-Top_Hair', 'Mullet', 'Odango_Hair', 'Pageboy', HELL SCRIPT FOR EXTRACTING DATA FROM ETAFACE 'Perm', 'Pixie_Cut', 'Ponytail', 'Quiff', 'Rattail', S B #!/bin/sh 'Razor_Cut', 'Ringlet', 'Shag', 'Shoulder-Length_Hair', cd "$(dirname "$0")" 'Side_Part', 'Slicked-back', 'Spiky_Hair',
'Tapered_Sides', 'The_Rachel', 'Tonsure_Hair', 'Updo', INDEX="1" 'Waist-Length_Hair', 'Wave_Hair'] num = [105, 386, 66, 383, 603, 111, 203, 404, 103, 40, declare -a STYLE=("Aaron_Kwok" "Afro" "Bald" "Beehive" 47, 328, 205, 109, 50, 62, 926, 351, 490, 21, 652, 19, "Bob" "Bouffant" "Bowl_Cut" "Bun" "Caesar" "Chonmage" 67, 84, 310, 162, 78, 243, 90, 22, 81, 51, 249, 118, 11, "Comb_Over" "Cornrows" "Crew_Cut" "Crop" 225, 99, 43, 104, 97, 184, 63, 259, 284, 499, 308, 22, "Croydon_Facelift" "Curly" "Curly_Hair" "Curtained_Hair" 30, 381, 117, 411, 461, 92, 178, 368, 52, 33, 32, 108, "Cute_Ponytails" "Devilock" "Dreadlocks" "Ducktail" 82, 254] "Emo_hair" "Fauxhawk" "Flattop" "French_Braid"
"French_Twist" "Hi-top_Fade" "Hime_Cut" KEY = '5f1a14d290b54640804ee44a94548c66' # Replace with "Horseshoe_Flattop" "Induction_Cut" "Jimmy_Lin_Hairstyle" a valid Subscription Key here. "Layered_Hair" "Liberty_Spikes_Hair" "Long_Hair" "Medium- CF.Key.set(KEY) Length_Hair" "Men_Pompadour" "Men_With_Square_Angles"
"Mohawk" "Mop-Top_Hair" "Mullet" "Odango_Hair" "Pageboy" BASE_URL = "Perm" "Pixie_Cut" "Ponytail" "Quiff" "Rattail" 'https://northeurope.api.cognitive.microsoft.com/face/v1. "Razor_Cut" "Ringlet" "Shag" "Shoulder-Length_Hair" 0' # Replace with your regional Base URL "Side_Part" "Slicked-back" "Spiky_Hair" "Tapered_Sides" CF.BaseUrl.set(BASE_URL) "The_Rachel" "Tonsure_Hair" "Updo" "Waist-Length_Hair"
"Wave_Hair") f = csv.writer(open("test.csv", "w")) declare -a COUNT=("105" "386" "66" "383" "603" "111" f.writerow(["Hairstyle", "Pic", "Gender", "Age", "203" "404" "103" "40" "47" "328" "205" "109" "50" "62" "Moustache", "Beard", "Sideburns", "Glasses"]) "926" "351" "490" "21" "652" "19" "67" "84" "310" "162"
"78" "243" "90" "22" "81" "51" "249" "118" "11" "225" for i in range(61): "99" "43" "104" "97" "184" "63" "259" "284" "499" "308" img_url = [] "22" "30" "381" "117" "411" "461" "92" "178" "368" "52" for j in range(num[i]): "33" "32" "108" "82" "254")
import org.neuroph.core.exceptions.*; for (( i = 0; i < 61; i++ )); do import org.neuroph.core.input.*; import org.neuroph.core.learning.error.*; import org.neuroph.core.learning.*; #Insert a opening bracket JSON Array import org.neuroph.core.learning.stop.*; printf "[\n" >> ${STYLE[$i]}.json import org.neuroph.core.transfer.*; import org.neuroph.nnet.*; import org.neuroph.nnet.comp.*; NUMBER="1" import org.neuroph.nnet.comp.layer.*; for (( j = 0; j < ${COUNT[$i]} ; j++ )); do import org.neuroph.nnet.comp.neuron.*; import org.neuroph.nnet.learning.*; #printf import org.neuroph.util.benchmark.*; "https://raw.githubusercontent.com/sarkrui/Hairstyle60k/m import org.neuroph.util.*; aster/Dataset/${STYLE[$i]}/IMG_$INDEX.jpg" import org.neuroph.util.io.*; curl -sS import org.neuroph.util.plugins.*; https://www.betafaceapi.com/api/v2/media -H "accept: import org.neuroph.util.random.*; application/json" -H "Content-Type: application/json" -d "{ \"api_key\": \"d45fd466-51e2-4701-8da8-04351c872236\", import java.util.*; \"file_uri\": \"https://raw.githubusercontent.com/sarkrui/Hairstyle60k/ String str = new String(); master/Dataset/${STYLE[$i]}/IMG_$NUMBER.jpg\", \"detection_flags\": \"classifiers,extended\"}" >> Table table; ${STYLE[$i]}.json Table validation; printf ${STYLE[$i]}_IMG_$NUMBER- int w = 0; printf INDEX_$INDEX"\n" printf ",\n" >> ${STYLE[$i]}.json String output[] = let "INDEX+=1" {"Aaron_Kwok","Afro","Bald","Beehive","Bob","Bouffant","B let "NUMBER+=1" owl_Cut","Bun","Caesar","Chonmage","Comb_Over","Cornrows" ,"Crew_Cut","Crop","Croydon_Facelift","Curly","Curly_Hair if [[ $INDEX -eq 499 ]]; then ","Curtained_Hair","Cute_Ponytails","Dreadlocks","Emo_hai afplay /System/Library/Sounds/Funk.aiff r","Fauxhawk","Flattop","French_Braid","Hi- read -p "Please reset your ip...Press top_Fade","Hime_Cut","Induction_Cut","Jimmy_Lin_Hairstyle [ENTER] to continute." ","Layered_Hair","Liberty_Spikes_Hair","Medium- INDEX="1" Length_Hair","Men_Pompadour","Men_With_Square_Angles","Mo fi hawk","Mop- Top_Hair","Mullet","Odango_Hair","Pageboy","Perm","Pixie_ done Cut","Ponytail","Razor_Cut","Ringlet","Shag","Shoulder- #Removing the comma one the last line Length_Hair","Side_Part","Slicked- sed -i "_bak" '$ s/,$//g' ${STYLE[$i]}.json back","Spiky_Hair","Tapered_Sides","The_Rachel","Updo","W aist-Length_Hair","Wave_Hair"}; #Insert a JSON Array closing bracket String[] topTen = new String[10]; printf "]\n" >> ${STYLE[$i]}.json float[] topData = new float[10]; done void setup() { exit size(1400,720); table = loadTable("Validation.csv", "header"); validation = new Table(); Java code for data validation validation.addColumn("Lable"); import org.neuroph.core.*; validation.addColumn("First Choice"); import org.neuroph.core.data.*; validation.addColumn("Second Choice"); import org.neuroph.core.data.norm.*; validation.addColumn("Third Choice"); import org.neuroph.core.data.sample.*; validation.addColumn("Fourth Choice"); import org.neuroph.core.events.*; validation.addColumn("Fifth Choice"); validation.addColumn("Sixth Choice");
validation.addColumn("Seventh Choice"); } validation.addColumn("Eighth Choice"); else if(n == 3){ validation.addColumn("Nineth Choice"); topTen[3] = output[i]; validation.addColumn("Tenth Choice"); topData[3] = (float)networkOutput[i]; n = 0; // load saved neural network } NeuralNetwork neuralNetwork1 = else if(n == 4){ NeuralNetwork.createFromFile(sketchPath("myMlPerceptron.n topTen[4] = output[i]; net")); topData[4] = (float)networkOutput[i]; n = 0; // set network input } for (TableRow row : table.rows()) { else if(n == 5){ w = w + 1; topTen[5] = output[i]; float[] data_row = new float[26]; topData[5] = (float)networkOutput[i]; for(int i = 0; i < 26; i++){ n = 0; data_row[i] = row.getFloat(str(i+1)); } } else if(n == 6){ topTen[6] = output[i]; neuralNetwork1.setInput(data_row[0],data_row[1],data_row[ topData[6] = (float)networkOutput[i]; 2],data_row[3],data_row[4],data_row[5],data_row[6],data_r n = 0; ow[7],data_row[8],data_row[9],data_row[10],data_row[11],d } ata_row[12],data_row[13],data_row[14],data_row[15],data_r else if(n == 7){ ow[16],data_row[17],data_row[18],data_row[19],data_row[20 topTen[7] = output[i]; ],data_row[21],data_row[22],data_row[23],data_row[24],dat topData[7] = (float)networkOutput[i]; a_row[25]); n = 0; } // calculate network else if(n == 8){ neuralNetwork1.calculate(); topTen[8] = output[i]; topData[8] = (float)networkOutput[i]; // get network output n = 0; double[] networkOutput = neuralNetwork1.getOutput(); } else if(n == 9){ str = Arrays.toString(networkOutput); topTen[9] = output[i]; topData[9] = (float)networkOutput[i]; // get the top ten hairstyle: n = 0; for(int i = 0; i < 53; i ++ ){ } int n = 0; else n = 0; for(int j = 0; j < 53; j ++){ } if(networkOutput[i] < networkOutput[j]) n ++; println("running time:" + w); } if(n == 0){ TableRow newRow = validation.addRow(); topTen[0] = output[i]; newRow.setString("Lable", output[(w-1)/3]); topData[0] = (float)networkOutput[i]; newRow.setString("First Choice", topTen[0]); n = 0; newRow.setString("Second Choice", topTen[1]); } newRow.setString("Third Choice", topTen[2]); else if(n == 1){ newRow.setString("Fourth Choice", topTen[3]); topTen[1] = output[i]; newRow.setString("Fifth Choice", topTen[4]); topData[1] = (float)networkOutput[i]; newRow.setString("Sixth Choice", topTen[5]); n = 0; newRow.setString("Seventh Choice", topTen[6]); } newRow.setString("Eighth Choice", topTen[7]); else if(n == 2){ newRow.setString("Nineth Choice", topTen[8]); topTen[2] = output[i]; newRow.setString("Tenth Choice", topTen[9]); topData[2] = (float)networkOutput[i]; n = 0; saveTable(validation, "data/new.csv");
} //-H "Content-Type: application/json" } //-H "Ocp-Apim-Subscription-Key: {subscription key}" //--data-ascii "{body}" void draw(){ textSize(30); import http.requests.*; fill(0); PrintWriter output; textAlign(CENTER); JSONArray msResponse; text("HAIR RECOMMENDATION", 1000, 50); JSONArray betafaceResponse; JSONObject faceAttributes; textSize(40); fill(0); textAlign(CENTER); float[] remapped = new float[20]; text(topTen[0]+": "+topData[0], 1000, 160); int remapped_gender = 0; text(topTen[1]+": "+topData[1], 1000, 290); float remapped_glasses = 0; text(topTen[2]+": "+topData[2], 1000, 420); float remapped_age; float sideburns = 0; textSize(10); float beard = 0; textAlign(LEFT); float moustache = 0; text(str, 580, 520, 800, 720); } void getMS() {
msResponse = new JSONArray(); JAVA CODE AS SHOWN AT OUR FINAL PRESENTATION faceAttributes = new JSONObject(); import org.neuroph.core.*; import org.neuroph.core.data.*; //Creates a json file in the sketch directory to store import org.neuroph.core.events.*; JSON response from Microsoft FaceAPI import org.neuroph.core.exceptions.*; output = createWriter("msResponse.json"); import org.neuroph.core.input.*; import org.neuroph.core.learning.error.*; //HTTP Request body import org.neuroph.core.learning.*; PostRequest post = new import org.neuroph.core.learning.stop.*; PostRequest("https://northeurope.api.cognitive.microsoft. import org.neuroph.core.transfer.*; com/face/v1.0/detect?returnFaceId=true&returnFaceLandmark import org.neuroph.eval.classification.*; s=false&returnFaceAttributes=gender,age,facialHair,glasse import org.neuroph.eval.*; s"); import org.neuroph.nnet.*; post.addHeader("Host", import org.neuroph.nnet.comp.*; "northeurope.api.cognitive.microsoft.com"); import org.neuroph.nnet.comp.layer.*; post.addHeader("Content-Type", "application/json"); import org.neuroph.nnet.comp.neuron.*; post.addHeader("Ocp-Apim-Subscription-Key", import org.neuroph.nnet.learning.*; "5f1a14d290b54640804ee44a94548c66"); import org.neuroph.nnet.learning.kmeans.*; import org.neuroph.nnet.learning.knn.*; post.addJson("{\"url\":\"https://raw.githubusercontent.co import org.neuroph.util.benchmark.*; m/sarkrui/Hairstyle60k/master/sample_3.jpg\"}"); import org.neuroph.util.*; post.send(); import org.neuroph.util.data.norm.*; import org.neuroph.util.data.sample.*; //Parse HTTP Response as a JSON Array import org.neuroph.util.io.*; JSONArray msResponse = import org.neuroph.util.plugins.*; parseJSONArray(post.getContent()); import org.neuroph.util.random.*; import http.requests.*; //Saves JSON Array to local for backup import java.util.*; saveJSONArray(msResponse, "data/msResponse.json");
//curl -v -X POST //Reads local JSON file "https://westcentralus.api.cognitive.microsoft.com/face/v //msResponse = loadJSONArray("data/msResponse.json"); 1.0/detect?returnFaceId=true&returnFaceLandmarks=false&re turnFaceAttributes={string}"
//curl -sS https://www.betafaceapi.com/api/v2/media -H //"Gender","Age","Moustache","Beard","Sideburns","Glasses "accept: application/json" -H "Content-Type: " application/json" -d "{ \"api_key\": \"d45fd466-51e2- 4701-8da8-04351c872236\", \"file_uri\": //Expands JSONArray msResponse[0] \"https://raw.githubusercontent.com/sarkrui/Hairstyle60k/ JSONObject msObject = msResponse.getJSONObject(0); master/Dataset/${STYLE[$i]}/IMG_$INDEX.jpg\", \"detection_flags\": \"classifiers\"}" //Expands JSONObject faceAttributes //https://raw.githubusercontent.com/sarkrui/Hairstyle60k/ JSONObject faceAttributes = master/Dataset/Curly/IMG_39.jpg msObject.getJSONObject("faceAttributes"); //import http.requests.*;
//Expands JSONObject facialHair void getBetaface() { JSONObject facialHair = faceAttributes.getJSONObject("facialHair"); betafaceResponse = new JSONArray();
//Expands JSONObject faceAttributes //Declares the reading order from tags String glasses = faceAttributes.getString("glasses"); int[] readingOrder = {27, 0, 2, 4, 6, 7, 8, 9, 14, 16, String gender = faceAttributes.getString("gender"); 22, 23, 26, 28, 35, 36, 44, 54, 66, 69}; int age = faceAttributes.getInt("age"); PostRequest post = new PostRequest("https:" + //Retrieves a String value from "//www.betafaceapi.com/api/v2/media"); sideburns = facialHair.getFloat("sideburns"); post.addHeader("accept", "application/json"); beard = facialHair.getFloat("beard"); post.addHeader("Content-Type", "application/json"); moustache = facialHair.getFloat("moustache"); post.addJson("{\"api_key\": \"d45fd466-51e2-4701-8da8- 04351c872236\",\"file_uri\": //gender: \"https://raw.githubusercontent.com/sarkrui/Hairstyle60k/ if (gender == "female") { master/sample_3.jpg\",\"detection_flags\": remapped_gender = 0; \"classifiers,extended\"}"); } else if (gender == "male") { post.send(); remapped_gender = 1; } //System.out.println(post.getContent() + "\n");
//glasses: //Parse HTTP Response as a JSON Array if (glasses == "noGlasses") { JSONObject betafaceResponse = remapped_glasses = 0; parseJSONObject(post.getContent()); } else if (glasses == "ReadingGlasses") { remapped_glasses = 0.5; //Saves JSON Array to local } else { saveJSONObject(betafaceResponse, remapped_glasses = 1; "data/betafaceResponse.json"); } //Expands JSONObject media //age: JSONObject media = remapped_age = age/100.0; betafaceResponse.getJSONObject("media");
//JSONObject faceAttributes = //Store JSONArray faces MS_Object.getJSONObject(1); JSONArray faces = media.getJSONArray("faces"); //String Age = faceAttributes.getString("glasses"); //String Moustache = //Expands JSONArray faces faceAttributes.getString("moustache"); JSONObject face = faces.getJSONObject(0); println(remapped_gender, "\n", remapped_age, "\n", moustache, "\n", beard, "\n", sideburns, "\n", //Expands JSONObject face remapped_glasses); JSONArray tags = face.getJSONArray("tags"); } //float[] remapped = new float[20];
for (int i = 0; i < 20; i++) { } else if (value == "extra far") { remapped[i] = 1; //Expands JSONArray tags } else { JSONObject feature = remapped[i] = confidence; tags.getJSONObject(readingOrder[i]); }
//Expands JSONObject feature println(name, remapped[i]); float confidence = feature.getFloat("confidence"); } String name = feature.getString("name"); } String value = feature.getString("value"); // image on GitHub String url = if (value == "yes") { "https://raw.githubusercontent.com/sarkrui/Hairstyle60k/m remapped[i] = 0.5 + confidence/2.0; aster/sample_3.jpg"; } else if (value == "no") { PImage webImg; remapped[i] = 0.5 - confidence/2.0; String str = new String(); } else { remapped[i] = confidence; PImage one; } PImage two; PImage three; if (value == "extra small") { remapped[i] = 0; // the three output labels with highest output will be } else if (value == "small") { shown remapped[i] = 0.25; String output_category[] = {"Aaron_Kwok", "Afro", "Bald", } else if (value == "average") { "Beehive", "Bob", "Bouffant", "Bowl_Cut", "Bun", remapped[i] = 0.5; "Caesar", "Chonmage", "Comb_Over", "Cornrows", } else if (value == "big") { "Crew_Cut", "Crop", "Croydon_Facelift", "Curly", remapped[i] = 0.75; "Curly_Hair", "Curtained_Hair", "Cute_Ponytails", } else if (value == "extra big") { "Dreadlocks", "Emo_hair", "Fauxhawk", "Flattop", remapped[i] = 1; "French_Braid", "Hi-top_Fade", "Hime_Cut", } else { "Induction_Cut", "Jimmy_Lin_Hairstyle", "Layered_Hair", remapped[i] = confidence; "Liberty_Spikes_Hair", "Medium-Length_Hair", } "Men_Pompadour", "Men_With_Square_Angles", "Mohawk", "Mop-Top_Hair", "Mullet", "Odango_Hair", "Pageboy", if (value == "extra narrow") { "Perm", "Pixie_Cut", "Ponytail", "Razor_Cut", "Ringlet", remapped[i] = 0; "Shag", "Shoulder-Length_Hair", "Side_Part", "Slicked- } else if (value == "narrow") { back", "Spiky_Hair", "Tapered_Sides", "The_Rachel", remapped[i] = 0.25; "Updo", "Waist-Length_Hair", "Wave_Hair"}; } else if (value == "average") { String[] topThree = new String[3]; remapped[i] = 0.5; float[] topData = new float[3]; } else if (value == "wide") { remapped[i] = 0.75; void setup() { } else if (value == "extra wide") { size(1400, 720); remapped[i] = 1; webImg = loadImage(url, "jpg"); } else { remapped[i] = confidence; getMS(); } getBetaface();
if (value == "extra close") { // load saved neural network remapped[i] = 0; NeuralNetwork neuralNetwork1 = } else if (value == "close") { NeuralNetwork.createFromFile(sketchPath("SarkNN_Win.nnet" remapped[i] = 0.25; )); } else if (value == "average") { remapped[i] = 0.5; // set network input } else if (value == "far") { neuralNetwork1.setInput(remapped_gender, remapped_age, remapped[i] = 0.75; moustache, beard, sideburns, remapped_glasses,
remapped[0], remapped[1], remapped[2], remapped[3], textSize(50); remapped[4], remapped[5], remapped[6], remapped[7], fill(0); remapped[8], remapped[9], remapped[10], remapped[11], textAlign(CENTER); remapped[12], remapped[13], remapped[14], remapped[15], text("HAIR RECOMMENDATION", 1000, 50); remapped[16], remapped[17], remapped[18], remapped[19]); // calculate network textSize(40); neuralNetwork1.calculate(); fill(0); textAlign(CENTER); // get network output text(topThree[0]+": "+topData[0], 1000, 160); double[] networkOutput = neuralNetwork1.getOutput(); text(topThree[1]+": "+topData[1], 1000, 290); System.out.println(" Output: " + text(topThree[2]+": "+topData[2], 1000, 420); Arrays.toString(networkOutput) ); str = Arrays.toString(networkOutput); textSize(10); textAlign(LEFT); for (int i = 0; i < 53; i ++ ) { text(str, 580, 520, 800, 720); int n = 0; } for (int j = 0; j < 53; j ++) { if (networkOutput[i] < networkOutput[j]) n ++; //Tab postrequest } package http.requests; if (n == 0) { topThree[0] = output_category[i]; import java.io.File; topData[0] = (float)networkOutput[i]; import java.io.IOException; n = 0; import java.nio.file.Files; } else if (n == 1) { import java.nio.file.Path; topThree[1] = output_category[i]; import java.nio.file.Paths; topData[1] = (float)networkOutput[i]; import java.util.ArrayList; n = 0; import java.util.HashMap; } else if (n == 2) { import java.util.Iterator; topThree[2] = output_category[i]; import java.util.List; topData[2] = (float)networkOutput[i]; import java.util.Map.Entry; n = 0; } else n = 0; import org.apache.http.Header; } import org.apache.http.HttpEntity; import org.apache.http.HttpHeaders; for (int i = 0; i < 3; i ++) { import org.apache.http.HttpResponse; println(topThree[i]); import org.apache.http.NameValuePair; } import } org.apache.http.client.entity.UrlEncodedFormEntity; import void draw() { org.apache.http.client.methods.HttpEntityEnclosingRequest //image(img, 0, 0, 540, 720); Base; image(webImg, 0, 0, 540, 720); import org.apache.http.client.methods.HttpPost; import org.apache.http.client.methods.HttpPut; one = loadImage(topThree[0]+".jpg"); import org.apache.http.entity.ByteArrayEntity; two = loadImage(topThree[1]+".jpg"); import org.apache.http.entity.StringEntity; three = loadImage(topThree[2]+".jpg"); import org.apache.http.entity.mime.MultipartEntity; import org.apache.http.entity.mime.content.FileBody; image(one, float(600), float(120), float(100), import org.apache.http.entity.mime.content.StringBody; float(100)); import org.apache.http.impl.client.DefaultHttpClient; image(two, float(600), float(250), float(100), import org.apache.http.message.BasicHeader; float(100)); import org.apache.http.message.BasicNameValuePair; image(three, float(600), float(380), float(100), import org.apache.http.util.EntityUtils; float(100)); public class PostRequest
{ try { String url; this.binary = Files.readAllBytes(path); ArrayList
public PostRequest(String url) public void addFile(String name, File f) { { nameFilePairs.put(name,f); this(url, "ISO-8859-1"); } } public void addFile(String name, String path) { public PostRequest(String url, String encoding) File f = new File(path); { nameFilePairs.put(name,f); this.url = url; } this.encoding = encoding; nameValuePairs = new ArrayList
public void addData(byte[] binary) { public void send() addData(null, binary); { } try { public void addData(String contentType, byte[] binary) DefaultHttpClient httpClient = new { DefaultHttpClient(); if (contentType != null) { HttpEntityEnclosingRequestBase httpPost; addHeader(HttpHeaders.CONTENT_TYPE, contentType); } // you can specify this is a PUT request. this.binary = binary; everything else is a POST. } if (method != null && method.equalsIgnoreCase("PUT")) { public void addDataFromFile(String fullPathname) { httpPost = new HttpPut(url); addDataFromFile(null, fullPathname); } else { } httpPost = new HttpPost(url); public void addDataFromFile(String contentType, String } fullPathname) { if (contentType != null) { if (!nameValuePairs.isEmpty()) { addHeader(HttpHeaders.CONTENT_TYPE, contentType); httpPost.setEntity(new } UrlEncodedFormEntity(nameValuePairs, encoding)); Path path = Paths.get(fullPathname); } System.out.println("Path: " + path.toAbsolutePath()); // add binary
else if (binary != null) { httpPost.setEntity(new ByteArrayEntity(binary)); ______} ____ */ // add json else if (json != null) { public String getContent() httpPost.setEntity(new StringEntity(json)); { } return this.content; // file handling } else if (!nameFilePairs.isEmpty()) { MultipartEntity mentity = new MultipartEntity(); public String getHeader(String name) Iterator
// add the headers to the request if (!headers.isEmpty()) { for (Header header : headers) { httpPost.addHeader(header); } }
response = httpClient.execute(httpPost); HttpEntity entity = response.getEntity(); this.content = EntityUtils.toString(response.getEntity());
if( entity != null ) EntityUtils.consume(entity);
httpClient.getConnectionManager().shutdown();
// Clear it out for the next time nameValuePairs.clear(); nameFilePairs.clear(); headers.clear(); json = null; binary = null; method = null;
} catch( Exception e ) { e.printStackTrace(); } }
/* Getters