Create app.py

app.py

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+import gradio as gr
+from scipy.spatial import distance as dist
+from imutils import face_utils
+import numpy as np
+import imutils
+import time
+import dlib
+import cv2
+from keras.preprocessing.image import img_to_array
+from keras.models import load_model
+
+# Define global variables
+points = []
+emotion_classifier = None
+
+def eye_brow_distance(leye, reye):
+    global points
+    distq = dist.euclidean(leye, reye)
+    points.append(int(distq))
+    return distq
+
+def emotion_finder(faces, frame):
+    global emotion_classifier
+    EMOTIONS = ["angry", "disgust", "scared", "happy", "sad", "surprised", "neutral"]
+    x, y, w, h = face_utils.rect_to_bb(faces)
+    frame = frame[y:y + h, x:x + w]
+    roi = cv2.resize(frame, (64, 64))
+    roi = roi.astype("float") / 255.0
+    roi = img_to_array(roi)
+    roi = np.expand_dims(roi, axis=0)
+    preds = emotion_classifier.predict(roi)[0]
+    emotion_probability = np.max(preds)
+    label = EMOTIONS[preds.argmax()]
+    return label
+
+def normalize_values(points, disp):
+    normalized_value = abs(disp - np.min(points)) / abs(np.max(points) - np.min(points))
+    stress_value = np.exp(-(normalized_value))
+    return stress_value
+
+def stress(video_path, duration):
+    global points, emotion_classifier
+    detector = dlib.get_frontal_face_detector()
+    predictor_path = "Stress_detection/shape_predictor_68_face_landmarks.dat"
+    predictor = dlib.shape_predictor(predictor_path)
+    emotion_classifier = load_model("Stress_detection/XCEPTION.102-0.66.hdf5", compile=False)
+    
+    # Open video file
+    cap = cv2.VideoCapture(video_path)
+    points = []
+    stress_labels = []
+    start_time = time.time()
+
+    while True:
+        current_time = time.time()
+        if current_time - start_time >= duration:
+            break
+
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        frame = cv2.flip(frame, 1)
+        frame = imutils.resize(frame, width=500, height=500)
+
+        (lBegin, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eyebrow"]
+        (rBegin, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eyebrow"]
+
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        try:
+            detections = detector(gray, 0)
+            for detection in detections:
+                emotion = emotion_finder(detection, gray)
+                shape = predictor(gray, detection)
+                shape = face_utils.shape_to_np(shape)
+
+                leyebrow = shape[lBegin:lEnd]
+                reyebrow = shape[rBegin:rEnd]
+
+                distq = eye_brow_distance(leyebrow[-1], reyebrow[0])
+                stress_value = normalize_values(points, distq)
+
+                # Determine stress label for this frame
+                if emotion in ['scared', 'sad', 'angry'] and stress_value >= 0.75:
+                    stress_label = 'stressed'
+                else:
+                    stress_label = 'not stressed'
+
+                # Store stress label in list
+                stress_labels.append(stress_label)
+
+        except Exception as e:
+            print(f'Error: {e}')
+
+    cap.release()
+
+    # Count occurrences of 'stressed' and 'not stressed'
+    stressed_count = stress_labels.count('stressed')
+    not_stressed_count = stress_labels.count('not stressed')
+
+    # Determine which label occurred more frequently
+    if stressed_count > not_stressed_count:
+        most_frequent_label = 'stressed'
+    else:
+        most_frequent_label = 'not stressed'
+
+    return stressed_count, not_stressed_count, most_frequent_label
+
+def gradio_interface(video, duration):
+    stressed_count, not_stressed_count, most_frequent_label = stress(video, duration)
+    return f"Stressed frames: {stressed_count}", f"Not stressed frames: {not_stressed_count}", f"Most frequent state: {most_frequent_label}"
+
+# Define Gradio interface for Heart and Stress Measurement
+gr.Interface(
+    fn=gradio_interface,
+    inputs=[gr.Video(label="Upload a video file"), gr.Number(value=30, label="Duration (seconds)")],
+    outputs="json",
+    title="Heart Rate and Stress Measurement"
+).launch(server_name="0.0.0.0")
+