Create Face-Emotion-Detection-GUI.py

Author: rogers-cyber

141-Face-Emotion-Detection-GUI/Face-Emotion-Detection-GUI.py

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+import tkinter as tk
+from tkinter import filedialog, messagebox
+from PIL import Image, ImageTk
+import cv2
+import numpy as np
+import ttkbootstrap as tb
+from ttkbootstrap.constants import *
+from tensorflow.keras.models import load_model
+from collections import deque
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+import tensorflow as tf
+import datetime
+
+# ---------------- GPU CHECK ----------------
+gpus = tf.config.list_physical_devices('GPU')
+if gpus:
+    tf.config.experimental.set_memory_growth(gpus[0], True)
+
+# ---------------- EMOTION MODEL ----------------
+emotion_labels = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
+model = load_model("fer2013_mini_XCEPTION.102-0.66.hdf5", compile=False)
+
+# ---------------- FACE DETECTOR ----------------
+face_cascade = cv2.CascadeClassifier(
+    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+)
+
+class EmotionApp:
+    def __init__(self, root):
+        self.root = root
+        self.root.title("Advanced Face Emotion Detection")
+        self.root.geometry("1100x850")
+        self.root.resizable(False, False)
+
+        tb.Style("superhero")
+
+        # ---------------- Controls ----------------
+        control = tb.Frame(root)
+        control.pack(pady=10)
+
+        tb.Button(control, text="Start Webcam", bootstyle=SUCCESS, command=self.start).grid(row=0, column=0, padx=5)
+        tb.Button(control, text="Stop Webcam", bootstyle=DANGER, command=self.stop).grid(row=0, column=1, padx=5)
+        tb.Button(control, text="Save Screenshot", bootstyle=WARNING, command=self.save_snapshot).grid(row=0, column=2, padx=5)
+
+        # ---------------- Labels ----------------
+        self.emotion_label = tb.Label(root, text="Emotion: None", font=("Arial", 16))
+        self.emotion_label.pack()
+
+        # ---------------- Confidence Bar ----------------
+        self.conf_bar = tb.Progressbar(root, length=400, bootstyle=SUCCESS)
+        self.conf_bar.pack(pady=5)
+
+        # ---------------- Video ----------------
+        self.video_label = tb.Label(root)
+        self.video_label.pack(pady=10)
+
+        # ---------------- Emotion History Graph ----------------
+        self.fig, self.ax = plt.subplots(figsize=(6, 3))
+        self.ax.set_ylim(0, 1)
+        self.ax.set_title("Emotion Confidence History")
+        self.line, = self.ax.plot([], [])
+
+        self.canvas = FigureCanvasTkAgg(self.fig, master=root)
+        self.canvas.get_tk_widget().pack()
+
+        self.history = deque(maxlen=50)
+
+        # ---------------- Variables ----------------
+        self.cap = None
+        self.running = False
+        self.last_frame = None
+
+    # ---------------- Start ----------------
+    def start(self):
+        if self.running:
+            return
+        self.cap = cv2.VideoCapture(0)
+        if not self.cap.isOpened():
+            messagebox.showerror("Error", "Webcam not accessible")
+            return
+        self.running = True
+        self.update()
+
+    # ---------------- Stop ----------------
+    def stop(self):
+        self.running = False
+        if self.cap:
+            self.cap.release()
+        self.video_label.config(image="")
+        self.emotion_label.config(text="Emotion: None")
+        self.conf_bar["value"] = 0
+
+    # ---------------- Update Frame ----------------
+    def update(self):
+        if not self.running:
+            return
+
+        ret, frame = self.cap.read()
+        if not ret:
+            return
+
+        self.last_frame = frame.copy()
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+        for (x, y, w, h) in faces:
+            face = gray[y:y+h, x:x+w]
+            face = cv2.resize(face, (48, 48))
+            face = face / 255.0
+            face = np.reshape(face, (1, 48, 48, 1))
+
+            preds = model.predict(face, verbose=0)[0]
+            idx = np.argmax(preds)
+            emotion = emotion_labels[idx]
+            confidence = preds[idx]
+
+            self.emotion_label.config(text=f"Emotion: {emotion}")
+            self.conf_bar["value"] = confidence * 100
+
+            self.history.append(confidence)
+            self.line.set_data(range(len(self.history)), list(self.history))
+            self.ax.set_xlim(0, 50)
+            self.canvas.draw()
+
+            cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 2)
+            cv2.putText(frame, f"{emotion} ({confidence:.2f})",
+                        (x,y-10), cv2.FONT_HERSHEY_SIMPLEX,
+                        0.8, (0,255,0), 2)
+
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        img = Image.fromarray(frame)
+        img = img.resize((800, 500), Image.Resampling.LANCZOS)
+        imgtk = ImageTk.PhotoImage(img)
+        self.video_label.imgtk = imgtk
+        self.video_label.config(image=imgtk)
+
+        self.root.after(30, self.update)
+
+    # ---------------- Save Snapshot ----------------
+    def save_snapshot(self):
+        if self.last_frame is None:
+            return
+        path = filedialog.asksaveasfilename(
+            defaultextension=".jpg",
+            filetypes=[("JPEG", "*.jpg"), ("PNG", "*.png")]
+        )
+        if path:
+            cv2.imwrite(path, self.last_frame)
+            messagebox.showinfo("Saved", f"Snapshot saved:\n{path}")
+
+# ---------------- Main ----------------
+if __name__ == "__main__":
+    root = tb.Window(themename="superhero")
+    EmotionApp(root)
+    root.mainloop()