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| 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 .exp_recognition_model import * | |
| from PIL import Image | |
| import base64 | |
| import io | |
| import os | |
| import pdb | |
| ## 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 Expression detected by your network. | |
| #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 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), this should return the Expression in string form ex: "Anger" | |
| #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_expression(img): | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| ########################################################################################## | |
| ##Example for loading a model using weight state dictionary: ## | |
| ## face_det_net = facExpRec() #Example Network ## | |
| ## model = torch.load(current_path + '/exp_recognition_net.t7', map_location=device) ## | |
| ## face_det_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 ## | |
| ########################################################################################## | |
| ########################################################################################## | |
| face_det_net = facExpRec() | |
| model = torch.load(current_path + '/face_expression.t7', map_location=device) | |
| face_det_net.load_state_dict(model['net_dict']) | |
| face = detected_face(img) | |
| if face == 0: | |
| face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)) | |
| with torch.no_grad(): | |
| face = trnscm(face) | |
| output = face_det_net(face) | |
| _, predicted = torch.max(output, 1) | |
| predicted_expression = classes[predicted.item()] | |
| # YOUR CODE HERE, return expression using your model | |
| return predicted_expression |