Upload app.py

app.py

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+
+import gradio as gr
+import cv2
+from deepface import DeepFace
+import numpy as np
+
+# For Colab-specific display
+from google.colab.patches import cv2_imshow
+
+# Function to predict emotion from an image
+def predict_emotion_image(image):
+    try:
+        # Convert the Gradio image (PIL format) to an OpenCV image
+        img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        
+        # Analyze the emotion using DeepFace
+        result = DeepFace.analyze(img, actions=['emotion'], enforce_detection=False)
+        
+        # Get the dominant emotion
+        dominant_emotion = result[0]['dominant_emotion']  # Access the first result if it's a list
+        
+        return f"Detected Emotion: {dominant_emotion}"
+    except Exception as e:
+        return f"Error in image emotion detection: {str(e)}"
+
+# Function to process video, detect faces, and predict emotions
+def predict_emotion_video(video):
+    try:
+        cap = cv2.VideoCapture(video)
+        
+        if not cap.isOpened():
+            return "Error: Unable to open video."
+        
+        # Initialize variables to store detected emotions
+        detected_emotions = []
+        
+        # Processing each frame of the video
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            # Convert frame to grayscale for face detection
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+            faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+            
+            for (x, y, w, h) in faces:
+                face = frame[y:y+h, x:x+w]
+                
+                try:
+                    result = DeepFace.analyze(face, actions=['emotion'], enforce_detection=False)
+                    dominant_emotion = result[0]['dominant_emotion']
+                    
+                    # Append detected emotion for the current frame
+                    detected_emotions.append(dominant_emotion)
+                    
+                    # Draw rectangle and label around face
+                    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+                    cv2.putText(frame, dominant_emotion, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+                
+                except Exception as e:
+                    print(f"Error analyzing face in video: {e}")
+            
+            # Display the frame in Colab
+            cv2_imshow(frame)
+            
+            # Break the loop if 'q' is pressed (ignored in Colab)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+        
+        # Release video resources
+        cap.release()
+        cv2.destroyAllWindows()
+        
+        # Combine detected emotions into a result string
+        result_text = "Video Emotion Results: " + ", ".join(detected_emotions) if detected_emotions else "No emotion detected."
+        
+        return result_text
+    
+    except Exception as e:
+        return f"Error in video emotion detection: {str(e)}"
+
+# Function to handle choice (image or video)
+def process_choice(choice, image=None, video=None):
+    if choice == "Image Emotion Detection":
+        return predict_emotion_image(image)
+    elif choice == "Video Emotion Detection":
+        return predict_emotion_video(video)
+    else:
+        return "Please select a valid option."
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='color: #e91e63;'>Image and Video Emotion Recognition</h1>")
+    
+    # Dropdown to select between image or video detection
+    choice = gr.Dropdown(["Image Emotion Detection", "Video Emotion Detection"], label="Choose Mode")
+    
+    # Inputs for image or video
+    image_input = gr.Image(type="pil", label="Upload Image", visible=False)
+    video_input = gr.Video(label="Upload Video", visible=False)
+    
+    # Function to show/hide inputs based on selection
+    def update_input(choice):
+        if choice == "Image Emotion Detection":
+            return gr.update(visible=True), gr.update(visible=False)
+        elif choice == "Video Emotion Detection":
+            return gr.update(visible=False), gr.update(visible=True)
+        return gr.update(visible=False), gr.update(visible=False)
+
+    # Update visibility of inputs
+    choice.change(fn=update_input, inputs=choice, outputs=[image_input, video_input])
+    
+    # Output
+    result_output = gr.Textbox(label="Emotion Detection Result")
+    
+    # Button to process the image or video
+    submit_btn = gr.Button("Analyze Emotion")
+    
+    # Connect button to function for processing choice
+    submit_btn.click(fn=process_choice, inputs=[choice, image_input, video_input], outputs=result_output)
+
+# Launch the Gradio app
+demo.launch()