Update app.py

app.py

@@ -1,5 +1,83 @@
+# import gradio as gr
+# import os
+# import cv2
+# import numpy as np
+# import imutils
+# from keras.preprocessing.image import img_to_array
+# from keras.models import load_model
+
+# # Load the pre-trained models and define parameters
+# detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml'
+# emotion_model_path = 'model4_0.83/model4_entire_model.h5'
+# face_detection = cv2.CascadeClassifier(detection_model_path)
+# emotion_classifier = load_model(emotion_model_path, compile=False)
+# EMOTIONS = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear', 'contempt', 'unknown']
+
+
+# # face_detector_mtcnn = MTCNN()
+# classifier = load_model(emotion_model_path)
+
+# def predict_emotion(frame):
+#     frame = imutils.resize(frame, width=300)
+#     gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+#     faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
+#                                             minNeighbors=5, minSize=(30, 30),
+#                                             flags=cv2.CASCADE_SCALE_IMAGE)
+
+#     for (fX, fY, fW, fH) in faces:
+#         # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
+#         # the ROI for classification via the CNN
+#         roi = gray[fY:fY + fH, fX:fX + fW]
+#         roi = cv2.resize(roi, (48, 48))
+#         roi = roi.astype("float") / 255.0
+#         roi = img_to_array(roi)
+#         roi = np.expand_dims(roi, axis=0)
+
+#         preds = emotion_classifier.predict(roi)[0]
+#         label = EMOTIONS[preds.argmax()]
+
+#         # Overlay a box over the detected face
+#         cv2.putText(frame, label, (fX, fY - 10),
+#                     cv2.FONT_HERSHEY_DUPLEX, 0.5, (238, 164, 64), 1, cv2.LINE_AA)
+#         cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH),
+#                       (238, 164, 64), 2)
+
+#     return frame
+
+
+
+
+
+# demo = gr.Interface(
+#     fn = predict_emotion,
+#     inputs = gr.Image(type="numpy"),
+#     outputs = gr.Image(),
+#     # gr.components.Image(label="Predicted Emotion"),
+#     # gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
+#     #flagging_options=["blurry", "incorrect", "other"],
+#     examples = [
+        
+#         os.path.join(os.path.dirname(__file__), "images/chandler.jpeg"),
+#         os.path.join(os.path.dirname(__file__), "images/janice.jpeg"),
+#         os.path.join(os.path.dirname(__file__), "images/joey.jpeg"),
+#         os.path.join(os.path.dirname(__file__), "images/phoebe.jpeg"),
+#         os.path.join(os.path.dirname(__file__), "images/rachel_monica.jpeg"),
+#         os.path.join(os.path.dirname(__file__), "images/ross.jpeg"),
+#         os.path.join(os.path.dirname(__file__), "images/gunther.jpeg")
+     
+#     ],
+#     title = "How are you feeling?",
+#     theme = "shivi/calm_seafoam"
+# )
+    
+
+
+# if __name__ == "__main__":
+#     demo.launch()
+    
+######################################################################################################################################################
+    
 import gradio as gr
-import os
 import cv2
 import numpy as np
 import imutils
@@ -13,64 +91,44 @@ face_detection = cv2.CascadeClassifier(detection_model_path)
 emotion_classifier = load_model(emotion_model_path, compile=False)
 EMOTIONS = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear', 'contempt', 'unknown']
 
-
-# face_detector_mtcnn = MTCNN()
-classifier = load_model(emotion_model_path)
-
 def predict_emotion(frame):
     frame = imutils.resize(frame, width=300)
-    gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
     faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
                                             minNeighbors=5, minSize=(30, 30),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
 
     for (fX, fY, fW, fH) in faces:
-        # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
-        # the ROI for classification via the CNN
         roi = gray[fY:fY + fH, fX:fX + fW]
         roi = cv2.resize(roi, (48, 48))
         roi = roi.astype("float") / 255.0
         roi = img_to_array(roi)
         roi = np.expand_dims(roi, axis=0)
-
         preds = emotion_classifier.predict(roi)[0]
         label = EMOTIONS[preds.argmax()]
-
-        # Overlay a box over the detected face
         cv2.putText(frame, label, (fX, fY - 10),
                     cv2.FONT_HERSHEY_DUPLEX, 0.5, (238, 164, 64), 1, cv2.LINE_AA)
         cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH),
                       (238, 164, 64), 2)
-
     return frame
 
+# Define Gradio interface
+inputs = [
+    gr.inputs.Image(type="numpy", label="Upload Image"),
+    gr.inputs.Video(type="numpy", label="Upload Video")  # Allow for video input
+]
+outputs = [
+    gr.outputs.Video(type="numpy", label="Processed Video"),  # Allow for video output
+    gr.Image()
+]
+examples = [
+    "images/chandler.jpeg",
+    "videos/input_video.mp4"
+]
 
+title = "Emotion Recognition"
+description = "Upload an image or a video, and the model will detect emotions in faces and overlay them on the output."
 
-
-
-demo = gr.Interface(
-    fn = predict_emotion,
-    inputs = gr.Image(type="numpy"),
-    outputs = gr.Image(),
-    # gr.components.Image(label="Predicted Emotion"),
-    # gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
-    #flagging_options=["blurry", "incorrect", "other"],
-    examples = [
-        
-        os.path.join(os.path.dirname(__file__), "images/chandler.jpeg"),
-        os.path.join(os.path.dirname(__file__), "images/janice.jpeg"),
-        os.path.join(os.path.dirname(__file__), "images/joey.jpeg"),
-        os.path.join(os.path.dirname(__file__), "images/phoebe.jpeg"),
-        os.path.join(os.path.dirname(__file__), "images/rachel_monica.jpeg"),
-        os.path.join(os.path.dirname(__file__), "images/ross.jpeg"),
-        os.path.join(os.path.dirname(__file__), "images/gunther.jpeg")
-     
-    ],
-    title = "How are you feeling?",
-    theme = "shivi/calm_seafoam"
-)
-    
-
-
-if __name__ == "__main__":
-    demo.launch()
+iface = gr.Interface(fn=predict_emotion, inputs=inputs, outputs=outputs,
+                     examples=examples, title=title, description=description)
+iface.launch()