Transférer les fichiers vers 'mediapipe.py'

mediapipe.py/test3.py

@@ -0,0 +1,92 @@
+import cv2
+import mediapipe as mp
+
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_face_mesh = mp.solutions.face_mesh
+
+drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
+cap = cv2.VideoCapture('X:/video.mp4')
+
+R = 0
+G = 0
+B = 0
+red = green = blue = 0
+n = 0
+i = 0
+
+with mp_face_mesh.FaceMesh(
+    max_num_faces=1,
+    refine_landmarks=True,
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as face_mesh:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      # If loading a video, use 'break' instead of 'continue'.
+      continue
+    i += 1
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    img = cv2.resize(image, (640, 480))
+    image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    results = face_mesh.process(image)
+
+    # Draw the face mesh annotations on the image.
+    image.flags.writeable = False
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    if results.multi_face_landmarks:
+        for face_landmarks in results.multi_face_landmarks:
+            shape = image.shape
+            for landmark in face_landmarks.landmark:
+                n += 1
+                x = landmark.x
+                y = landmark.y
+                relative_x = int(x * shape[1])
+                relative_y = int(y * shape[0])
+                #cv2.circle()
+
+                if relative_x >= shape[0]:
+                    relative_x = shape[0]-1
+                if relative_y >= shape[1]:
+                    relative_y = shape[1]-1
+                B, G, R = img[relative_x, relative_y]
+                #cv2.circle(image, [relative_x, relative_y], 3, [int(B), int(G), int(R)], 3)
+                red += R
+                blue += B
+                green += G
+            color = [int(blue/n), int(green/n), int(red/n)]
+            print(color)
+            red = green = blue = n = 0
+            #cv2.rectangle(image, [400, 450], [450, 450], [255,255,255], 10)
+            #color_hex = '#'+str(hex(color[2])[2:])+str(hex(color[1])[2:])+str(hex(color[0])[2:])
+            #cv2.putText(img,str(i), [400,450], cv2.FONT_HERSHEY_PLAIN, 1, color, 2)
+            #cv2.text(image, [600, 400], )
+        mp_drawing.draw_landmarks(
+                image=image,
+                landmark_list=face_landmarks,
+                connections=mp_face_mesh.FACEMESH_TESSELATION,
+                landmark_drawing_spec=None,
+                connection_drawing_spec=mp_drawing_styles
+                .get_default_face_mesh_tesselation_style())
+        '''mp_drawing.draw_landmarks(
+            image=image,
+            landmark_list=face_landmarks,
+            connections=mp_face_mesh.FACEMESH_CONTOURS,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp_drawing_styles
+            .get_default_face_mesh_contours_style())
+        mp_drawing.draw_landmarks(
+            image=image,
+            landmark_list=face_landmarks,
+            connections=mp_face_mesh.FACEMESH_IRISES,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp_drawing_styles
+            .get_default_face_mesh_iris_connections_style())'''
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Face Mesh', cv2.flip(image, 1))
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+cap.release()