def upload():
file = request.files['image']
# Read image
image = read_image(file)
# Recognize faces
classifier_model_path = "models" + os.sep + "lotr_mlp_10c_recognizer.pickle"
label_encoder_path = "models" + os.sep + "lotr_mlp_10c_labelencoder.pickle"
faces = recognize_faces(image, classifier_model_path, label_encoder_path, detection_api_url=app.config["DETECTION_API_URL"])
# Draw detection rects
draw_rectangles(image, faces)
# Prepare image for html
to_send = prepare_image(image)
return render_template('index.html', face_recognized=len(faces)>0, num_faces=len(faces), image_to_show=to_send, init=True)
def upload():
file = request.files['image']
# Read image
image = read_image(file)
# Detect faces
faces = detect_faces_with_ssd(image)
# Draw detection rects
num_faces, image = draw_rectangles(image, faces)
# Prepare image for html
to_send = prepare_image(image)
return render_template('index.html', face_detected=len(faces)>0, num_faces=len(faces), image_to_show=to_send, init=True)
# extract embeddings
classes_dir = "images" + os.sep + "lotr" + os.sep + "train" + os.sep + "10_classes"
embeddings_path = "images" + os.sep + "lotr" + os.sep + "train" + os.sep + "embeddings.pickle"
extract_embeddings(classes_dir, embeddings_path)
# train nb classifier
classifier_model_path = "models" + os.sep + "lotr_nb_recognizer.pickle"
label_encoder_path = "models" + os.sep + "lotr_nb_le.pickle"
train_nb_model(embeddings_path, classifier_model_path, label_encoder_path)
# train svm classifier
classifier_model_path = "models" + os.sep + "lotr_svm_recognizer.pickle"
label_encoder_path = "models" + os.sep + "lotr_svm_le.pickle"
train_svm_model(embeddings_path, classifier_model_path, label_encoder_path)
# train mlp classifier
classifier_model_path = "models" + os.sep + "lotr_mlp_recognizer.pickle"
label_encoder_path = "models" + os.sep + "lotr_mlp_le.pickle"
train_mlp_model(embeddings_path, classifier_model_path, label_encoder_path)
# recognize face
file_path = "images" + os.sep + "lotr" + os.sep + "test" + os.sep + "raw" + os.sep + "legolas6.jpg"
image = cv2.imread(file_path)
image = imutils.resize(image, width=600)
recognitions = recognize_faces(image, classifier_model_path,
label_encoder_path)
draw_rectangles(image, recognitions)
cv2.imshow("recognition result", image)
cv2.waitKey()