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import cv2 | |
import numpy as np | |
import tensorflow as tf | |
from tensorflow.keras.utils import custom_object_scope | |
class CustomMSE(tf.keras.losses.Loss): | |
def __init__(self, name="custom_mse"): | |
super().__init__(name=name) | |
def call(self, y_true, y_pred): | |
return tf.reduce_mean(tf.square(y_true - y_pred), axis=-1) | |
def predict(image): | |
model = tf.keras.models.load_model("v1model.h5") | |
img = cv2.imread(image) | |
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') | |
faces_rect = haar_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=9) | |
men_count = 0 | |
women_count = 0 | |
for (x, y, w, h) in faces_rect: | |
cropped_img = img[y:y+h, x:x+w] | |
resized_img = cv2.resize(cropped_img,(64,64)) | |
image_array = np.expand_dims(resized_img,axis=0) | |
prediction = model.predict(image_array) | |
if prediction[0][0] > 0.5: | |
ans = "Men" | |
men_count+=1 | |
else: | |
ans = "Women" | |
women_count+=1 | |
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), thickness=2) | |
cv2.putText(img, ans, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) | |
rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
return rgb_img,men_count,women_count | |