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Author: Niraj Dev Pandey

src/image_emotion_gender.py

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+import sys
+
+import cv2
+from keras.models import load_model
+import numpy as np
+
+from utils.datasets import get_labels
+from utils.inference import detect_faces
+from utils.inference import draw_text
+from utils.inference import draw_bounding_box
+from utils.inference import apply_offsets
+from utils.inference import load_detection_model
+from utils.inference import load_image
+from utils.preprocessor import preprocess_input
+
+# parameters for loading data and images
+image_path = 'IMG.jpg'
+detection_model_path = '../trained_models//haarcascade_frontalface_default.xml'
+emotion_model_path = '../trained_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
+gender_model_path = '../trained_models/simple_CNN.81-0.96.hdf5'
+emotion_labels = get_labels('fer2013')
+gender_labels = get_labels('imdb')
+font = cv2.FONT_HERSHEY_SIMPLEX
+
+# hyper-parameters for bounding boxes shape
+gender_offsets = (30, 60)
+gender_offsets = (10, 10)
+emotion_offsets = (20, 40)
+emotion_offsets = (0, 0)
+
+# loading models
+face_detection = load_detection_model(detection_model_path)
+emotion_classifier = load_model(emotion_model_path, compile=False)
+gender_classifier = load_model(gender_model_path, compile=False)
+
+# getting input model shapes for inference
+emotion_target_size = emotion_classifier.input_shape[1:3]
+gender_target_size = gender_classifier.input_shape[1:3]
+
+# loading images
+rgb_image = load_image(image_path, grayscale=False)
+gray_image = load_image(image_path, grayscale=True)
+gray_image = np.squeeze(gray_image)
+gray_image = gray_image.astype('uint8')
+
+faces = detect_faces(face_detection, gray_image)
+for face_coordinates in faces:
+    x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
+    rgb_face = rgb_image[y1:y2, x1:x2]
+
+    x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
+    gray_face = gray_image[y1:y2, x1:x2]
+
+    try:
+        rgb_face = cv2.resize(rgb_face, (gender_target_size))
+        gray_face = cv2.resize(gray_face, (emotion_target_size))
+    except:
+        continue
+
+    rgb_face = preprocess_input(rgb_face, False)
+    rgb_face = np.expand_dims(rgb_face, 0)
+    gender_prediction = gender_classifier.predict(rgb_face)
+    gender_label_arg = np.argmax(gender_prediction)
+    gender_text = gender_labels[gender_label_arg]
+
+    gray_face = preprocess_input(gray_face, True)
+    gray_face = np.expand_dims(gray_face, 0)
+    gray_face = np.expand_dims(gray_face, -1)
+    emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
+    emotion_text = emotion_labels[emotion_label_arg]
+
+    if gender_text == gender_labels[0]:
+        color = (0, 0, 255)
+    else:
+        color = (255, 0, 0)
+
+    draw_bounding_box(face_coordinates, rgb_image, color)
+    draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)
+    draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)
+
+bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
+cv2.imwrite('../images/predicted_test_image4.png', bgr_image)