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best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3b703591706ef7325381a29aacc66fdd849dfa57f8a47800c373ea6a187019a
+size 17392056

videotester.py

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+import gradio as gr
+
+import os
+import cv2
+import numpy as np
+from keras.preprocessing import image
+import warnings
+warnings.filterwarnings("ignore")
+from keras_preprocessing.image import ImageDataGenerator
+from keras.utils import img_to_array, load_img
+from keras.models import  load_model
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+# load model
+model = load_model(r'best_model.h5')
+
+face_haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+def analyze_emotion(frame):
+    gray_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    faces_detected = face_haar_cascade.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]  # cropping region of interest i.e. face area from  image
+        roi_gray = cv2.resize(roi_gray, (224, 224))
+        img_pixels = tf.keras.preprocessing.image.img_to_array(roi_gray)
+        img_pixels = np.expand_dims(img_pixels, axis=0)
+        img_pixels /= 255
+
+        predictions = model.predict(img_pixels)
+
+        # find max indexed array
+        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)
+
+    return frame[...,::-1]
+
+inputs = gr.inputs.Video(source="webcam")
+outputs = gr.outputs.Image(type="numpy")
+
+iface = gr.Interface(fn=analyze_emotion, inputs=inputs, outputs=outputs, title="Facial Emotion Analysis",
+             description="Detects emotions in real-time from webcam video input")
+
+iface.launch()