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 + '/' 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