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| from tensorflow import keras | |
| from keras.models import Sequential | |
| from keras.models import load_model | |
| from keras.models import model_from_json | |
| from keras.utils import img_to_array | |
| import keras.utils as image | |
| import cv2 | |
| import numpy as np | |
| import os | |
| from django_app.settings import BASE_DIR | |
| model = Sequential() | |
| model = model_from_json(open( | |
| os.path.join(BASE_DIR,'model/model_4layer_2_2_pool.json'), "r").read()) | |
| model.load_weights(os.path.join( | |
| BASE_DIR,'model/model_4layer_2_2_pool.h5')) | |
| class_labels = {0: 'Angry', 1: 'Disgust', 2: 'Fear', | |
| 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'} | |
| classes = list(class_labels.values()) | |
| face_classifier = cv2.CascadeClassifier(os.path.join( | |
| BASE_DIR,'model/haarcascade_frontalface.xml')) | |
| # camera = cv2.VideoCapture(0) | |
| def text_on_detected_boxes(text, text_x, text_y, image, font_scale=1, | |
| font=cv2.FONT_HERSHEY_SIMPLEX, | |
| FONT_COLOR=(0, 0, 0), | |
| FONT_THICKNESS=2, | |
| rectangle_bgr=(0, 255, 0)): | |
| (text_width, text_height) = cv2.getTextSize( | |
| text, font, fontScale=font_scale, thickness=2)[0] | |
| box_coords = ((text_x-10, text_y+4), (text_x + | |
| text_width+10, text_y - text_height-5)) | |
| cv2.rectangle(image, box_coords[0], | |
| box_coords[1], rectangle_bgr, cv2.FILLED) | |
| cv2.putText(image, text, (text_x, text_y), font, | |
| fontScale=font_scale, color=FONT_COLOR, thickness=FONT_THICKNESS) | |
| def face_detector_image(img): | |
| """ | |
| Обнаружение лиц на изображении. | |
| Args: | |
| img (numpy array): Исходное изображение. | |
| Returns: | |
| tuple: (rects, allfaces, img) - координаты лиц, обрезанные лица и изображение с рамками. | |
| """ | |
| gray = cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY) | |
| faces = face_classifier.detectMultiScale(gray, 1.3, 5) | |
| if faces == (): | |
| return (0, 0, 0, 0), np.zeros((48, 48), np.uint8), img | |
| allfaces = [] | |
| rects = [] | |
| for (x, y, w, h) in faces: | |
| cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) | |
| roi_gray = gray[y:y + h, x:x + w] | |
| roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA) | |
| allfaces.append(roi_gray) | |
| rects.append((x, w, y, h)) | |
| return rects, allfaces, img | |
| def emotionImage(imgPath): | |
| img = cv2.imread(BASE_DIR + '/media/' + imgPath) | |
| rects, faces, image = face_detector_image(img) | |
| i = 0 | |
| for face in faces: | |
| roi = face.astype("float") / 255.0 | |
| roi = img_to_array(roi) | |
| roi = np.expand_dims(roi, axis=0) | |
| preds = model.predict(roi)[0] | |
| label = class_labels[preds.argmax()] | |
| label_position = ( | |
| rects[i][0] + int((rects[i][1] / 2)), abs(rects[i][2] - 10)) | |
| i += 1 | |
| # Отрисовка текста и рамок | |
| text_on_detected_boxes( | |
| label, label_position[0], label_position[1], image) | |
| precentages = dict(zip(classes, preds*100)) | |
| return image, precentages, label | |
| def emotionImageFromArray(img_array): | |
| """ | |
| Обрабатывает изображение и возвращает результат обработки. | |
| Args: | |
| img_array (numpy array): Исходное изображение (numpy array). | |
| Returns: | |
| tuple: (image, precentages, label) | |
| - image: Изображение с рамками и текстом эмоций. | |
| - precentages: Вероятности каждой эмоции. | |
| - label: Определенная эмоция. | |
| """ | |
| rects, faces, image = face_detector_image(img_array) | |
| i = 0 | |
| for face in faces: | |
| roi = face.astype("float") / 255.0 | |
| roi = img_to_array(roi) | |
| roi = np.expand_dims(roi, axis=0) | |
| preds = model.predict(roi)[0] | |
| label = class_labels[preds.argmax()] | |
| label_position = ( | |
| rects[i][0] + int((rects[i][1] / 2)), abs(rects[i][2] - 10)) | |
| i += 1 | |
| # Отрисовка текста и рамок | |
| text_on_detected_boxes( | |
| label, label_position[0], label_position[1], image) | |
| precentages = dict(zip(classes, preds*100)) | |
| return image, precentages, label | |
| # def face_detector_video(img): | |
| # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| # faces = face_classifier.detectMultiScale(gray, 1.3, 5) | |
| # if faces is (): | |
| # return (0, 0, 0, 0), np.zeros((48, 48), np.uint8), img | |
| # for (x, y, w, h) in faces: | |
| # cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), thickness=2) | |
| # roi_gray = gray[y:y + h, x:x + w] | |
| # roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA) | |
| # return (x, w, y, h), roi_gray, img | |
| # def emotionVideo(): | |
| # while True: | |
| # ret, frame = camera.read() | |
| # rect, face, image = face_detector_video(frame) | |
| # if np.sum([face]) != 0.0: | |
| # roi = face.astype("float") / 255.0 | |
| # roi = img_to_array(roi) | |
| # roi = np.expand_dims(roi, axis=0) | |
| # preds = model.predict(roi)[0] | |
| # label = class_labels[preds.argmax()] | |
| # label_position = (rect[0] + rect[1]//50, rect[2] + rect[3]//50) | |
| # text_on_detected_boxes(label, label_position[0], label_position[1], image) | |
| # fps = camera.get(cv2.CAP_PROP_FPS) | |
| # cv2.putText(image, str(fps),(5, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) | |
| # else: | |
| # cv2.putText(image, "No Face Found", (5, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2) | |
| # ret, buffer = cv2.imencode('.jpg', image) | |
| # frame = buffer.tobytes() | |
| # yield (b'--frame\r\n' | |
| # b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n') | |
| # def gen_frames(): | |
| # while True: | |
| # success, frame = camera.read() | |
| # if not success: | |
| # cv2.putText(image, "No Face Found", (5, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2) | |
| # break | |
| # else: | |
| # gray_img= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| # faces_detected = face_classifier.detectMultiScale(gray_img, 1.32, 5) | |
| # for (x,y,w,h) in faces_detected: | |
| # cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),thickness=7) | |
| # roi_gray=gray_img[y:y+w,x:x+h] | |
| # roi_gray=cv2.resize(roi_gray,(48,48)) | |
| # img_pixels = image.img_to_array(roi_gray) | |
| # img_pixels = np.expand_dims(img_pixels, axis = 0) | |
| # img_pixels /= 255 | |
| # predictions = model.predict(img_pixels) | |
| # max_index = np.argmax(predictions[0]) | |
| # emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'] | |
| # predicted_emotion = emotions[max_index] | |
| # cv2.putText(frame, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2) | |
| # resized_img = cv2.resize(frame, (600, 400)) | |
| # ret, buffer = cv2.imencode('.jpg', frame) | |
| # frame = buffer.tobytes() | |
| # yield (b'--frame\r\n' | |
| # b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n') |