simplified code

deepface/detectors/MediapipeWrapper.py

@@ -1,77 +1,49 @@
 
 from deepface.detectors import FaceDetector
 
-
 # Link - https://google.github.io/mediapipe/solutions/face_detection
 
 def build_model():
-    import mediapipe as mp
+    import mediapipe as mp #this is not a must dependency. do not import it in the global level.
     mp_face_detection = mp.solutions.face_detection
-    # Build a face detector
-    # min_detection_confidence - "A filter to analyse the training photographs"
-    face_detection =  mp_face_detection.FaceDetection( min_detection_confidence=0.6)
+    face_detection =  mp_face_detection.FaceDetection( min_detection_confidence=0.7)
     return face_detection
 
-def detect_face(face_detector, img, align=True):
-    import mediapipe as mp
-    import re
-    #mp_face_detection = mp.solutions.face_detection
-    #mp_drawing = mp.solutions.drawing_utils
+def detect_face(face_detector, img, align = True):
+    import mediapipe as mp #this is not a must dependency. do not import it in the global level.
     resp = []
+
+    img_width = img.shape[1]; img_height = img.shape[0]
+
     results = face_detector.process(img)
-    original_size = img.shape
-    target_size = (300, 300)
-    # First face , than eye
-    #print(results.detections)
-    if  results.detections:
+
+    if results.detections:
         for detection in results.detections:
-            #mp_drawing.draw_detection(img, detection)
-            #print(detection)
-            # detected_face is the cropped image that is then passed forward to the Regognizer
-            '''
-            DETECTION - 
-            Collection of detected faces, where each face is represented as a detection proto message that contains 
-            a bounding box and 6 key points (right eye, left eye, nose tip, mouth center, right ear tragion, and left
-            ear tragion). The bounding box is composed of xmin and width (both normalized to [0.0, 1.0] by the
-            image width) and ymin and height (both normalized to [0.0, 1.0] by the image height). Each key point
-            is composed of x and y, which are normalized to [0.0, 1.0] by the image width and height
-            respectively.
-            '''
-            # Bounding Box
-            x = re.findall('xmin: (..*)',str(detection))
-            y = re.findall('ymin: (..*)',str(detection))
-            h = re.findall('height: (..*)',str(detection))
-            w = re.findall('width: (..*)',str(detection))
-            # Eye Locations
-            reye_x = re.findall('x: (..*)',str(detection))[0]
-            leye_x = re.findall('x: (..*)',str(detection))[1]
-            reye_y = re.findall('y: (..*)', str(detection))[0]
-            leye_y = re.findall('y: (..*)', str(detection))[1]
-            # Detections are normalized by the mediapipe API, thus they need to be multiplied
-            # Extra tweaking done to improve accuracy
-            x = (float(x[0]) * original_size[1])
-            y = (float(y[0]) * original_size[0]-15)
-            h = (float(h[0]) * original_size[0]+10)
-            w = (float(w[0]) * original_size[1]+10)
-            reye_x = (float(reye_x) * original_size[1])
-            leye_x = (float(leye_x) * original_size[1])
-            reye_y = (float(reye_y) * original_size[0])
-            leye_y = (float(leye_y) * original_size[0])
-            if float(x) and float(y) > 0:
-                detected_face = img[int(y):int(y + h), int(x):int(x + w)]
-                img_region = [int(x), int(y), int(w), int(h)]
+
+            confidence = detection.score
+
+            bounding_box = detection.location_data.relative_bounding_box
+            landmarks = detection.location_data.relative_keypoints
+
+            x = int(bounding_box.xmin * img_width)
+            w = int(bounding_box.width * img_width)
+            y = int(bounding_box.ymin * img_height)
+            h = int(bounding_box.height * img_height)
+
+            right_eye = (int(landmarks[0].x * img_width), int(landmarks[0].y * img_height))
+            left_eye = (int(landmarks[1].x * img_width), int(landmarks[1].y * img_height))
+            #nose = (int(landmarks[2].x * img_width), int(landmarks[2].y * img_height))
+            #mouth = (int(landmarks[3].x * img_width), int(landmarks[3].y * img_height))
+            #right_ear = (int(landmarks[4].x * img_width), int(landmarks[4].y * img_height))
+            #left_ear = (int(landmarks[5].x * img_width), int(landmarks[5].y * img_height))
+
+            if x > 0 and y > 0:
+                detected_face = img[y:y+h, x:x+w]
+                img_region = [x, y, w, h]
+
                 if align:
-                    left_eye=(leye_x,leye_y)
-                    right_eye=(reye_x,reye_y)
-                    #print(left_eye)
-                    #print(right_eye)
                     detected_face = FaceDetector.alignment_procedure(detected_face, left_eye, right_eye)
+
                 resp.append((detected_face,img_region))
-            else:
-                continue
-
-            #print("Yahoo")
+
     return resp
-
-
-#face_detector = FaceDetector.build_model('mediapipe')