import numpy as np import cv2 from matplotlib import pyplot as plt import torch from torch.autograd import Variable # 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 import torch.nn.functional as F # 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 ## ########################################################################################## ########################################################################################## # YOUR CODE HERE, load the model print(torch.__version__) #torch.load('./siamese_model.t7',map_location='cpu') file = open(current_path + "/siamese_model.t7") file.seek(0, os.SEEK_END) print("Size of file is :", file.tell(), "bytes") feature_net = Siamese() # ## model = torch.load(current_path + "/siamese_model.t7", map_location="cpu") ## #model = torch.load(current_path + "/siamese_model.t7") feature_net.load_state_dict(model["net_dict"]) ## ## # YOUR CODE HERE, return similarity measure using your model feature_net.eval() print('face1.type', type(face1)) output1,output2 = feature_net(face1,face2) normalized_face1 = F.normalize(output1, dim=1) normalized_face2 = F.normalize(output2, dim=1) euclidean_distance = F.pairwise_distance(normalized_face1, normalized_face2) #euclidean_distance = F.pairwise_distance(output1, output2) #pairwise - more distance means less similarity #cosine similarity - more means more similarity btwn 2 arrays # Use euclidean similarity to measure the similarity between given two images euc_similarity = euclidean_distance.item() # cos_similarity1 = torch.nn.functional.cosine_similarity(output1, output2) # print('cos_similarity1',cos_similarity1) # cos_similarity = cos_similarity1.item() # return cos_similarity return euc_similarity # 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") classes = ['person1','person2','person6','person7'] det_img1 = detected_face(img1) if det_img1 == 0: det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)) img1 = trnscm(det_img1).unsqueeze(0) feature_net = Siamese() # ## feature_classifier = MLPClassifier(input_size=5, hidden_size=2048, num_classes=4) model = torch.load(current_path + "/siamese_model.t7", map_location="cpu") ## feature_net.load_state_dict(model["net_dict"]) ## #classifier model_classifier = torch.load(current_path + "/MLP_Image_Classifier.t7", map_location="cpu") feature_classifier.load_state_dict(model_classifier["classfier_dict"]) ## #evaluation feature_classifier.eval() feature_net.eval() ##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 representations = feature_net.forward_once(img1) representations = representations.to("cpu") outputs = feature_classifier(representations) _, predicted = torch.max(outputs.data, 1) ##Better Hint: Siamese experiment is covered in one of the labs return predicted.item()