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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.
# Keep '.' before face_recognition_model while uploading to the server
from .face_recognition_model import *
from PIL import Image
import base64
import io
import os
import joblib
import pickle
# 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__))
# -------------------------
# Face Detection
# -------------------------
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
# -------------------------
# Compute Similarity
# -------------------------
def get_similarity(img1, img2):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Detect faces
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))
# Transform images
face1 = trnscm(det_img1).unsqueeze(0).to(device)
face2 = trnscm(det_img2).unsqueeze(0).to(device)
# -------------------------
# Load Siamese Model
# -------------------------
model_path = current_path + '/siamese_model.t7'
checkpoint = torch.load(model_path, map_location=device)
feature_net = Siamese().to(device)
feature_net.load_state_dict(checkpoint['net_dict'])
feature_net.eval()
# -------------------------
# Compute similarity (Euclidean distance)
# -------------------------
with torch.no_grad():
output1, output2 = feature_net(face1, face2)
euclidean_distance = F.pairwise_distance(output1, output2)
# Convert distance to similarity score (0–1)
similarity_score = 1 / (1 + euclidean_distance.item())
return round(similarity_score, 3)
# -------------------------
# Get Face Class
# -------------------------
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))
# Transform image
face = trnscm(det_img1).unsqueeze(0).to(device)
# Load Siamese model
model_path = current_path + '/siamese_model.t7'
checkpoint = torch.load(model_path, map_location=device)
feature_net = Siamese().to(device)
feature_net.load_state_dict(checkpoint['net_dict'])
feature_net.eval()
# -------------------------
# Use Siamese + classifier to predict class (if classifier exists)
# -------------------------
# If you have a trained classifier that takes embeddings from Siamese as input:
# classifier = <your classifier>
# with torch.no_grad():
# embedding = feature_net.forward_once(face)
# pred = classifier(embedding)
# predicted_class = classes[pred.argmax(dim=1).item()]
# Since classifier is not trained here, return placeholder
predicted_class = "YET TO BE CODED"
return predicted_class
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