Spaces:
Running
Running
| from . import face_recognition_model | |
| import numpy as np | |
| import cv2 | |
| from matplotlib import pyplot as plt | |
| import torch | |
| # In the below line,remove '.' while working on your local system. However Make sure that '.' is present before face_recognition_model while uploading to the server, Do not remove it. | |
| from .face_recognition_model import * | |
| from PIL import Image | |
| import base64 | |
| import io | |
| import os | |
| import joblib | |
| import pickle | |
| from joblib import load | |
| # Add more imports if required | |
| ########################################################################################################################################### | |
| # Caution: Don't change any of the filenames, function names and definitions # | |
| # Always use the current_path + file_name for refering any files, without it we cannot access files on the server # | |
| ########################################################################################################################################### | |
| # Current_path stores absolute path of the file from where it runs. | |
| current_path = os.path.dirname(os.path.abspath(__file__)) | |
| #1) The below function is used to detect faces in the given image. | |
| #2) It returns only one image which has maximum area out of all the detected faces in the photo. | |
| #3) If no face is detected,then it returns zero(0). | |
| def detected_face(image): | |
| eye_haar = current_path + '/haarcascade_eye.xml' | |
| face_haar = current_path + '/haarcascade_frontalface_default.xml' | |
| face_cascade = cv2.CascadeClassifier(face_haar) | |
| eye_cascade = cv2.CascadeClassifier(eye_haar) | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| faces = face_cascade.detectMultiScale(gray, 1.3, 5) | |
| face_areas=[] | |
| images = [] | |
| required_image=0 | |
| for i, (x,y,w,h) in enumerate(faces): | |
| face_cropped = gray[y:y+h, x:x+w] | |
| face_areas.append(w*h) | |
| images.append(face_cropped) | |
| required_image = images[np.argmax(face_areas)] | |
| required_image = Image.fromarray(required_image) | |
| return required_image | |
| #1) Images captured from mobile is passed as parameter to the below function in the API call. It returns the similarity measure between given images. | |
| #2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function. | |
| #3) Define an object to your siamese network here in the function and load the weight from the trained network, set it in evaluation mode. | |
| #4) Get the features for both the faces from the network and return the similarity measure, Euclidean,cosine etc can be it. But choose the Relevant measure. | |
| #5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function | |
| #Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function | |
| def get_similarity(img1, img2): | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| det_img1 = detected_face(img1) | |
| det_img2 = detected_face(img2) | |
| if(det_img1 == 0 or det_img2 == 0): | |
| det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)) | |
| det_img2 = Image.fromarray(cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)) | |
| face1 = trnscm(det_img1).unsqueeze(0) | |
| face2 = trnscm(det_img2).unsqueeze(0) | |
| ########################################################################################## | |
| ##Example for loading a model using weight state dictionary: ## | |
| ## feature_net = light_cnn() #Example Network ## | |
| ## model = torch.load(current_path + '/siamese_model.t7', map_location=device) ## | |
| ## feature_net.load_state_dict(model['net_dict']) ## | |
| ## ## | |
| ##current_path + '/<network_definition>' is path of the saved model if present in ## | |
| ##the same path as this file, we recommend to put in the same directory ## | |
| ########################################################################################## | |
| ########################################################################################## | |
| feature_net = Siamese() | |
| ckpt = torch.load(current_path + '/siamese_model.t7', map_location=device) | |
| # model_path = current_path + "/Hackathon-setup/siamese_model.t7" | |
| feature_net.load_state_dict(ckpt['net_dict']) | |
| # model.eval() | |
| with torch.no_grad(): | |
| output1, output2 = feature_net(face1.to(device), face2.to(device)) | |
| # Calculate similarity measure - for instance, using cosine similarity | |
| euclidean_distance = F.pairwise_distance(output1, output2) | |
| return euclidean_distance.item() | |
| # ckpt = torch.load(current_path + "/Hackathon-setup/siamese_model.t7", map_location=device) | |
| # # YOUR CODE HERE, load the model | |
| # similarity_measure = ckpt(face1.to(device), face2.to(device)) | |
| # # YOUR CODE HERE, return similarity measure using your model | |
| # return similarity_measure | |
| # Load the Siamese network model | |
| #1) Image captured from mobile is passed as parameter to this function in the API call, It returns the face class in the string form ex: "Person1" | |
| #2) The image is passed to the function in base64 encoding, Code to decode the image provided within the function | |
| #3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode | |
| #4) Perform necessary transformations to the input(detected face using the above function). | |
| #5) Along with the siamese, you need the classifier as well, which is to be finetuned with the faces that you are training | |
| ##Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function | |
| def get_face_class(img1): | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| det_img1 = detected_face(img1) | |
| if(det_img1 == 0): | |
| det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)) | |
| ##YOUR CODE HERE, return face class here | |
| ##Hint: you need a classifier finetuned for your classes, it takes o/p of siamese as i/p to it | |
| ##Better Hint: Siamese experiment is covered in one of the labs | |
| face1 = trnscm(det_img1).unsqueeze(0) | |
| feature_net = Siamese().to(device) | |
| model = torch.load(current_path + '/siamese_model.t7', map_location=device) ## | |
| feature_net.load_state_dict(model['net_dict']) | |
| feature_net.eval() | |
| output1, output2 = feature_net(face1.to(device), face1.to(device)) | |
| output1 = output1.detach().numpy() | |
| clf_model = load(current_path + '/clf_model.joblib') | |
| label = clf_model.predict(output1) | |
| return label |