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| import cv2 | |
| import os | |
| import gradio as gr | |
| import numpy as np | |
| from keras.models import load_model | |
| from keras.utils import load_img, img_to_array | |
| from tensorflow.image import resize | |
| from PIL import Image | |
| #gradio interface | |
| def process_image(img): | |
| """ | |
| Parameters: | |
| img: np.ndarray | |
| an image (e.g., returned by cv2.imread) | |
| Returns: | |
| img: np.ndarray | |
| an image annotated with the bounding box and label | |
| """ | |
| cv2_face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + | |
| 'haarcascade_frontalface_default.xml') | |
| img = resize_img(img) | |
| img, _ = annotate_objects_in_image(img, | |
| cv2_face_cascade, | |
| model.labeldict, | |
| model.predictor) | |
| return(img) | |
| def annotate_objects_in_image(img, obj_cascade, labeldict, model): | |
| """ | |
| Parameters: | |
| img: np.ndarray | |
| an image (e.g., returned by cv2.imread) | |
| obj_cascade: cv2.CascadeClassifier | |
| OpenCV cascade classifier that can detect certain objects | |
| labeldict: dict | |
| a dictionary for decoding the model predictions, (e.g., {0:happy, 1:sad}) | |
| model: keras.engine.functional.Functional | |
| a Keras model instance (e.g., that is returned by keras.models.load_model) | |
| Returns: | |
| img: np.ndarray | |
| an image annotated with the bounding box and label | |
| emotion : str | |
| predicted emotion of the face image | |
| """ | |
| img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| objects = obj_cascade.detectMultiScale(img_gray, 1.1, 4) | |
| emotion = "" | |
| try: | |
| for (x, y, w, h) in objects: | |
| face = img[y-50:y+h+50, x-50:x+w+50] | |
| emotion, prob = predict_emotion_from_image(face, labeldict, model) | |
| font = cv2.FONT_HERSHEY_SIMPLEX | |
| cv2.putText(img, emotion, (x, y), font, 1, (0, 0, 255), 2, cv2.LINE_AA) | |
| cv2.rectangle(img, (x-25, y-25), (x+w+25, y+h+25), (0, 255, 0), 2) | |
| except Exception as ex: | |
| emotion = "" | |
| return img, emotion | |
| def predict_emotion_from_image (face_raw, labeldict, model): | |
| """ | |
| Parameters: | |
| face_raw: np.ndarray | |
| a square-like image of a human face (e.g., returned by cv2.imread) | |
| label_dict: dict | |
| a dictionary for decoding the model predictions, (e.g., {0:happy, 1:sad}) | |
| model: keras.engine.functional.Functional | |
| a Keras model instance (e.g., that is returned by keras.models.load_model) | |
| Returns: | |
| emotion: str | |
| predicted emotion of the face image | |
| prob: float | |
| percent probability of the predicted emotion | |
| """ | |
| face_res_arr = np.array(cv2.resize(face_raw, (48, 48))) | |
| face_res_arr_gray = face_res_arr/255 | |
| face_res_arr_gray_4dims = np.expand_dims(face_res_arr_gray, axis=0) | |
| prediction_vec = model.predict(face_res_arr_gray_4dims) | |
| prediction = np.argmax(prediction_vec) | |
| emotion = labeldict[prediction] | |
| prob = prediction_vec.max()*100 | |
| return (emotion,prob) | |
| def resize_img(img): | |
| """ | |
| Parameters: | |
| img: np.ndarray | |
| a potentially oversized image (e.g., returned by cv2.imread) | |
| Returns: | |
| img: np.ndarray | |
| a resized and potentially L-R flipped image | |
| """ | |
| if img.shape[0]> 960: | |
| img_AR = img.shape[0]/img.shape[1] | |
| #print(f'img shape: {img.shape}; aspect ratio: {img_AR}') | |
| img = cv2.resize(img, (640, int(np.round(640*img_AR)))) | |
| return img | |
| class ModelClass: | |
| def __init__(self,name='EDA_CNN.h5'): | |
| self.name = name | |
| self.predictor = load_model(os.path.join("models",modeltouse)) | |
| if name == "model_mobilenet_oncleandata_valacc078.h5": | |
| self.labeldict = {0: 'fear', 1: 'Angry', 2: 'Neutral', 3: 'Happy'} | |
| else: | |
| self.labeldict = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'} | |
| if name == "EDA_CNN.h5": | |
| self.channelno = 1 | |
| else: | |
| self.channelno = 3 | |
| #modeltouse = "EDA_CNN.h5" | |
| modeltouse = "MobileNet12blocks_wdgenaug_onrawdata_valacc063.h5" | |
| #modeltouse = "model_mobilenet_oncleandata_valacc078.h5" | |
| model = ModelClass(modeltouse) | |
| image_in = gr.inputs.Image() #shape=(48,48) | |
| image_out = gr.inputs.Image() | |
| examples = ['OnurH_CerenH.jpg', 'OnurA_CerenH.jpg'] | |
| #fname = 'Onur_happy.jpg' | |
| #image = cv2.imread(fname) | |
| #process_image(image, model) | |
| intf = gr.Interface(fn=process_image, inputs=image_in, outputs=image_out, examples=examples) | |
| intf.launch(inline=False) |