Project import generated by Copybara.

GitOrigin-RevId: f7d09ed033907b893638a8eb4148efa11c0f09a6
2020-11-04 19:09:58 -05:00
parent a8d6ce95c4
commit f96eadd6df
250 changed files with 15261 additions and 4620 deletions
@@ -254,6 +254,99 @@ and for iOS modify `kNumFaces` in
 Tip: Maximum number of faces to detect/process is set to 1 by default. To change
 it, in the graph file modify the option of `ConstantSidePacketCalculator`.

+#### Python
+
+MediaPipe Python package is available on
+[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
+install mediapipe` on Linux and macOS, as described below and in this
+[colab](https://mediapipe.page.link/face_mesh_py_colab). If you do need to build
+the Python package from source, see
+[additional instructions](../getting_started/building_examples.md#python).
+
+Activate a Python virtual environment:
+
+```bash
+$ python3 -m venv mp_env && source mp_env/bin/activate
+```
+
+Install MediaPipe Python package:
+
+```bash
+(mp_env)$ pip install mediapipe
+```
+
+Run the following Python code:
+
+<!-- Do not change the example code below directly. Change the corresponding example in mediapipe/python/solutions/face_mesh.py and copy it over. -->
+
+```python
+import cv2
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_face_mesh = mp.solutions.face_mesh
+
+# For static images:
+face_mesh = mp_face_mesh.FaceMesh(
+    static_image_mode=True,
+    max_num_faces=1,
+    min_detection_confidence=0.5)
+drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
+for idx, file in enumerate(file_list):
+  image = cv2.imread(file)
+  # Convert the BGR image to RGB before processing.
+  results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+  # Print and draw face mesh landmarks on the image.
+  if not results.multi_face_landmarks:
+    continue
+  annotated_image = image.copy()
+  for face_landmarks in results.multi_face_landmarks:
+    print('face_landmarks:', face_landmarks)
+    mp_drawing.draw_landmarks(
+        image=annotated_image,
+        landmark_list=face_landmarks,
+        connections=mp_face_mesh.FACE_CONNECTIONS,
+        landmark_drawing_spec=drawing_spec,
+        connection_drawing_spec=drawing_spec)
+  cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', image)
+face_mesh.close()
+
+# For webcam input:
+face_mesh = mp_face_mesh.FaceMesh(
+    min_detection_confidence=0.5, min_tracking_confidence=0.5)
+drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
+cap = cv2.VideoCapture(0)
+while cap.isOpened():
+  success, image = cap.read()
+  if not success:
+    break
+
+  # Flip the image horizontally for a later selfie-view display, and convert
+  # the BGR image to RGB.
+  image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
+  # To improve performance, optionally mark the image as not writeable to
+  # pass by reference.
+  image.flags.writeable = False
+  results = face_mesh.process(image)
+
+  # Draw the face mesh annotations on the image.
+  image.flags.writeable = True
+  image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+  if results.multi_face_landmarks:
+    for face_landmarks in results.multi_face_landmarks:
+      mp_drawing.draw_landmarks(
+          image=image,
+          landmark_list=face_landmarks,
+          connections=mp_face_mesh.FACE_CONNECTIONS,
+          landmark_drawing_spec=drawing_spec,
+          connection_drawing_spec=drawing_spec)
+  cv2.imshow('MediaPipe FaceMesh', image)
+  if cv2.waitKey(5) & 0xFF == 27:
+    break
+face_mesh.close()
+cap.release()
+```
+
 ### Face Effect Example

 Face effect example showcases real-time mobile face effect application use case