Delete new.py

new.py

@@ -1,61 +0,0 @@
-# 
-
-
-import cv2
-import numpy as np
-import tensorflow as tf
-
-# Load the face detector with the correct file path
-face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-# Load the emotion model
-emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
-emotion_model = tf.keras.models.load_model("model_emotion.h5")
-emotion_model.load_weights("model_weights_new.h5")
-print("Loaded emotion model from disk")
-
-# Define the predict_img function
-def predict_img(frame):
-    # Resize the image
-    frame = cv2.resize(frame, (1280, 720))
-    num_faces = face_detector.detectMultiScale(frame, scaleFactor=1.3, minNeighbors=5)
-
-    # Draw bounding boxes and annotate the image
-    for (x, y, w, h) in num_faces:
-        cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
-        roi_gray_frame = frame[y:y + h, x:x + w]
-
-        # Preprocess the input image
-        resized_img = cv2.resize(roi_gray_frame, (48, 48))
-        gray_img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2GRAY)
-        input_img = np.expand_dims(gray_img, axis=-1)  # Add the channel dimension
-        input_img = np.expand_dims(input_img, axis=0)  # Add the batch dimension
-
-        # Normalize the image
-        input_img = input_img / 255.0
-
-        # Predict the emotions
-        emotion_prediction = emotion_model.predict(input_img)
-        maxindex = int(np.argmax(emotion_prediction))
-        emotion_label = emotion_dict[maxindex]
-
-        # Annotate the image with emotion label
-        cv2.putText(frame, emotion_label, (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
-
-    return frame
-
-# Capture video from webcam
-cap = cv2.VideoCapture(0)
-
-while True:
-    ret, frame = cap.read()
-    if not ret:
-        break
-
-    annotated_frame = predict_img(frame)
-    cv2.imshow('Emotion Detection', annotated_frame)
-
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
-
-cap.release()
-cv2.destroyAllWindows()