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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
## 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 = detected_face(img)
if face==0:
face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
# YOUR CODE HERE, return expression using your model
return "YET TO BE CODED" |