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a47c171 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | import numpy as np
import cv2
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 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__))
_expression_model = None
_expression_device = None
def _load_expression_model():
global _expression_model, _expression_device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if _expression_model is not None and _expression_device == device:
return _expression_model, _expression_device
model_candidates = [
'best_resnet18_expression.pt',
'exp_recognition_net.pt',
'exp_recognition_net.pth',
'exp_recognition_net.t7',
]
for file_name in model_candidates:
model_path = os.path.join(current_path, file_name)
if os.path.exists(model_path):
_expression_model = load_model(model_path, device, num_classes=len(classes))
_expression_device = device
return _expression_model, _expression_device
raise FileNotFoundError(
"Expression model checkpoint not found. Add best_resnet18_expression.pt "
f"to {current_path}."
)
#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)
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):
##########################################################################################
##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))
try:
model, device = _load_expression_model()
except FileNotFoundError as exc:
return str(exc)
face_tensor = trnscm(face).unsqueeze(0).to(device)
with torch.no_grad():
logits = model(face_tensor)
prediction = int(torch.argmax(logits, dim=1).item())
return classes.get(prediction, 'UNKNOWN').capitalize()
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