Update app/apps/emotion_recognition/emotion_recognition.py

app/apps/emotion_recognition/emotion_recognition.py

@@ -1,203 +1,203 @@
-
-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')  
+
+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')