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import cv2
import numpy as np
from keras.models import load_model
from utils.datasets import get_labels
from utils.inference import detect_faces, apply_offsets, load_detection_model, load_image
from utils.preprocessor import preprocess_input
def most_frequent(List):
return max(set(List), key=List.count)
def get_most_frequent_emotion(dict_):
emotions = []
for frame_nmr in dict_.keys():
for face_nmr in dict_[frame_nmr].keys():
emotions.append(dict_[frame_nmr][face_nmr]['emotion'])
return most_frequent(emotions)
def process(imagePaths, output_filename='data/output/results.txt'):
detection_model_path = 'detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = 'emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')
emotion_offsets = (0, 0)
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
emotion_target_size = emotion_classifier.input_shape[1:3]
output = {}
for idx, image_path in enumerate(imagePaths):
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)
tmp = {}
for face_coordinates in faces:
face_key = tuple(face_coordinates)
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
tmp[face_key] = {'emotion': emotion_text, 'score': float(np.max(emotion_prediction))}
output[image_path] = tmp
# Save results to a text file
with open(output_filename, 'w') as file:
for image_path, faces_info in output.items():
file.write(f"{image_path}\n")
for face_key, info in faces_info.items():
file.write(f" {face_key}: {info}\n")
most_frequent_emotion = get_most_frequent_emotion(output)
return output, most_frequent_emotion
# This function can be used for processing a single image
def process_single_image(image):
detection_model_path = 'detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = 'emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')
emotion_offsets = (0, 0)
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
emotion_target_size = emotion_classifier.input_shape[1:3]
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
# Draw rectangle around face and label with predicted emotion
(x, y, w, h) = face_coordinates
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.putText(image, emotion_text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
return image
if __name__ == "__main__":
# This is just for testing purposes
test_image_paths = ['path_to_test_image1.jpg', 'path_to_test_image2.jpg']
output, most_frequent_emotion = process(test_image_paths)
print(f"Most frequent emotion: {most_frequent_emotion}")
for key in output.keys():
print(f"Image: {key}")
print(output[key])
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