Update app.py

app.py

@@ -43,7 +43,7 @@ def eye_aspect_ratio(eye):
 
 def detect_eyes(landmarks, img, threshold):
     lm = landmarks
-    eyes = np.array(lm[0][0][36:48], np.int32)
+    eyes = np.array(lm[36:48], np.int32)
 
     left_eye = eyes[0:6]
     right_eye = eyes[6:12]
@@ -60,23 +60,35 @@ def detect_eyes(landmarks, img, threshold):
 
 def proc_image(img, detector):
     detected_faces = detector.detect_faces(img)
-    if (len(detected_faces[0]) < 1):
+    faces_detected = len(detected_faces[0])
+    if ( faces_detected < 1):
         return img
+    
     detected_landmarks = detector.detect_landmarks(img, detected_faces)
+    assert len(detected_landmarks[0]) == faces_detected, "Number of faces and landsmarks are mismatched!"
+
+    is_eye_open = [detect_eyes(face, img, 0.20) for face in detected_landmarks[0]]
+    eye_dict = {True: "eyes open", False: "eyes closed"}
+
     detected_emotions = detector.detect_emotions(img, detected_faces, detected_landmarks)
-    is_eye_open = detect_eyes(detected_landmarks, img, 0.12)
-    eye_dict = {True: "Eyes Open", False: "Eyes Closed"}
+    assert len(detected_emotions[0]) == faces_detected, "Number of faces and emotions are mismatched!"
 
     em = detected_emotions[0]
     em_labels = em.argmax(axis=1)
 
-    for face, label in zip(detected_faces[0], em_labels):
+
+
+    for face, has_open_eyes, label in zip(detected_faces[0], (eye_dict[eyes] for eyes in is_eye_open), em_labels):
         (x0, y0, x1, y1, p) = face
-        img = cv.rectangle(img, (int(x0), int(y0)), (int(x1), int(y1)), color = (0, 0, 255), thickness = 3)
-        cv.putText(img, FEAT_EMOTION_COLUMNS[label], (int(x0)-10, int(y0)-10), fontFace = 0, color = (0, 0, 255), thickness = 2, fontScale = 1)
-        cv.putText(img, eye_dict[is_eye_open], (0, 25), fontFace = 0, color = (0, 0, 255), thickness = 2, fontScale = 1)
-        
-        return img
+        res_scale = img.shape[0]/704
+        cv.rectangle(img, (int(x0), int(y0)), (int(x1), int(y1)), color = (0, 0, 255), thickness = 3)
+        cv.putText(img, FEAT_EMOTION_COLUMNS[label], (int(x0)-10, int(y1+25*res_scale*1.5)), fontFace = 0, color = (0, 255, 0), thickness = 2, fontScale = res_scale)
+        cv.putText(img, f"{faces_detected } face(s) found", (0, int(25*res_scale*1.5)), fontFace = 0, color = (0, 255, 0), thickness = 2, fontScale = res_scale)
+        cv.putText(img, has_open_eyes, (int(x0)-10, int(y0)-10), fontFace = 0, color = (0, 255, 0), thickness = 2, fontScale = res_scale)
+    return img
+    
+def extract_feat():
+    return [1,2,3,4,5]
     
 def image_processing(frame):
     return proc_image(img, detector) if recog else img
@@ -118,10 +130,10 @@ with stream:
         frame = image_select(
         label="Try the classifier on one of the provided examples!",
         images=[
-            "ex0.jpg",
             "ex1.jpg",
-            "ex2.jpg",
-            "ex3.jpg",
+            "ex4.jpg",
+            "ex5.jpg",
+            "ex6.jpg",
         ],
         use_container_width= False
         )