Upload new.py

new.py

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+# 
+
+
+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()