Project import generated by Copybara.

GitOrigin-RevId: f7d09ed033907b893638a8eb4148efa11c0f09a6

MANIFEST.in

@@ -7,5 +7,4 @@ include MANIFEST.in
 include README.md
 include requirements.txt
 
-recursive-include mediapipe/modules *.tflite *.txt
-recursive-include mediapipe/graphs *.binarypb
+recursive-include mediapipe/modules *.tflite *.txt *.binarypb

README.md

@@ -35,9 +35,9 @@ Object Detection
 []()                                                                                      | Android | iOS | Desktop | Python | Web | Coral
 :---------------------------------------------------------------------------------------- | :-----: | :-: | :-----: | :----: | :-: | :---:
 [Face Detection](https://google.github.io/mediapipe/solutions/face_detection)                   | ✅       | ✅   | ✅       |        | ✅   | ✅
-[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)                             | ✅       | ✅   | ✅       |        |     |
+[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)                             | ✅       | ✅   | ✅       | ✅      |     |
 [Iris](https://google.github.io/mediapipe/solutions/iris)                                       | ✅       | ✅   | ✅       |        | ✅   |
-[Hands](https://google.github.io/mediapipe/solutions/hands)                                     | ✅       | ✅   | ✅       |        | ✅   |
+[Hands](https://google.github.io/mediapipe/solutions/hands)                                     | ✅       | ✅   | ✅       | ✅      | ✅   |
 [Pose](https://google.github.io/mediapipe/solutions/pose)                                       | ✅       | ✅   | ✅       | ✅      | ✅   |
 [Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation)             | ✅       |     | ✅       |        | ✅   |
 [Object Detection](https://google.github.io/mediapipe/solutions/object_detection)               | ✅       | ✅   | ✅       |        |     | ✅
@@ -53,6 +53,19 @@ See also
 [MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)
 for ML models released in MediaPipe.
 
+## MediaPipe in 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 in:
+
+*   [MediaPipe Face Mesh](../solutions/pose.md#python) and
+    [colab](https://mediapipe.page.link/face_mesh_py_colab)
+*   [MediaPipe Hands](../solutions/pose.md#python) and
+    [colab](https://mediapipe.page.link/hands_py_colab)
+*   [MediaPipe Pose](../solutions/pose.md#python) and
+    [colab](https://mediapipe.page.link/pose_py_colab)
+
 ## MediaPipe on the Web
 
 MediaPipe on the Web is an effort to run the same ML solutions built for mobile

WORKSPACE

@@ -364,9 +364,9 @@ http_archive(
 )
 
 #Tensorflow repo should always go after the other external dependencies.
-# 2020-08-30
-_TENSORFLOW_GIT_COMMIT = "57b009e31e59bd1a7ae85ef8c0232ed86c9b71db"
-_TENSORFLOW_SHA256= "de7f5f06204e057383028c7e53f3b352cdf85b3a40981b1a770c9a415a792c0e"
+# 2020-10-30
+_TENSORFLOW_GIT_COMMIT = "84384703c0d8b502e33ff6fd7eefd219dca5ff8e"
+_TENSORFLOW_SHA256= "23fb322fc15a20f7a7838d9a31f8b16f60700a494ea654311a0aa8621769df98"
 http_archive(
     name = "org_tensorflow",
     urls = [

build_android_examples.sh

@@ -93,38 +93,40 @@ for app in ${apps}; do
 
     echo "=== Target: ${target}"
 
-    if [[ $install_only == false ]]; then
-      bazel_flags=("${default_bazel_flags[@]}")
-      bazel_flags+=(${target})
-      if [[ $strip == true ]]; then
-        bazel_flags+=(--linkopt=-s)
-      fi
-
-      if [[ ${app_name} == "templatematchingcpu" ]]; then
-        switch_to_opencv_4
-      fi
-      bazel "${bazel_flags[@]}"
-      cp -f "${bin}" "${apk}"
-      if [[ ${app_name} == "templatematchingcpu" ]]; then
-        switch_to_opencv_3
-      fi
-    fi
-
     if [[ ${app_name} == "objectdetection3d" ]]; then
-      orig_apk=${apk}
-      apk="${out_dir}/${target_name}_shoes.apk"
-      cp -f "${orig_apk}" "${apk}"
-      apks+=(${apk})
-
-      apk="${out_dir}/${target_name}_chairs.apk"
+      categories=("shoe" "chair" "cup" "camera" "shoe_1stage" "chair_1stage")
+      for category in ${categories[@]}; do
+        apk="${out_dir}/${target_name}_${category}.apk"
+        if [[ $install_only == false ]]; then
+          bazel_flags_extended=("${bazel_flags[@]}")
+          if [[ ${category} != "shoe" ]]; then
+            bazel_flags_extended+=(--define ${category}=true)
+          fi
+          echo "bazel ${bazel_flags_extended[@]}"
+          bazel "${bazel_flags_extended[@]}"
+          cp -f "${bin}" "${apk}"
+        fi
+        apks+=(${apk})
+      done
+    else
       if [[ $install_only == false ]]; then
-        bazel_flags+=(--define chair=true)
+        bazel_flags=("${default_bazel_flags[@]}")
+        bazel_flags+=(${target})
+        if [[ $strip == true ]]; then
+          bazel_flags+=(--linkopt=-s)
+        fi
+
+        if [[ ${app_name} == "templatematchingcpu" ]]; then
+          switch_to_opencv_4
+        fi
         bazel "${bazel_flags[@]}"
         cp -f "${bin}" "${apk}"
+        if [[ ${app_name} == "templatematchingcpu" ]]; then
+          switch_to_opencv_3
+        fi
       fi
+      apks+=(${apk})
     fi
-
-    apks+=(${apk})
   fi
 done
 

build_desktop_examples.sh

@@ -86,9 +86,7 @@ for app in ${apps}; do
       cp -f "${bin_dir}/${app}/"*"_cpu" "${out_dir}"
     fi
     if [[ $build_only == false ]]; then
-      if  [[ ${target_name} == "multi_hand_tracking" ]]; then
-        graph_name="hand_tracking/multi_hand_tracking"
-      elif  [[ ${target_name} == "object_tracking" ]]; then
+      if  [[ ${target_name} == "object_tracking" ]]; then
         graph_name="tracking/object_detection_tracking"
       elif [[ ${target_name} == "upper_body_pose_tracking" ]]; then
         graph_name="pose_tracking/upper_body_pose_tracking"

docs/getting_started/android_archive_library.md

@@ -135,6 +135,7 @@ each project.
         def camerax_version = "1.0.0-beta10"
         implementation "androidx.camera:camera-core:$camerax_version"
         implementation "androidx.camera:camera-camera2:$camerax_version"
+        implementation "androidx.camera:camera-lifecycle:$camerax_version"
     }
     ```
 

docs/getting_started/building_examples.md

@@ -427,45 +427,13 @@ Note: This currently works only on Linux, and please first follow
 
 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 in
-[Run in python interpreter](#run-in-python-interpreter) and in this
-[colab](https://mediapipe.page.link/mp-py-colab).
+install mediapipe` on Linux and macOS, as described in, for instance,
+[Python section in MediaPipe Pose](../solutions/pose.md#python) and in this
+[colab](https://mediapipe.page.link/pose_py_colab).
 
-### Run in Python interpreter
-
-Using [MediaPipe Pose](../solutions/pose.md) as an example:
-
-```bash
-# Activate a Python virtual environment.
-$ python3 -m venv mp_env && source mp_env/bin/activate
-
-# Install MediaPipe Python package
-(mp_env)$ pip install mediapipe
-
-# Run in Python interpreter
-(mp_env)$ python3
->>> import mediapipe as mp
->>> pose_tracker = mp.examples.UpperBodyPoseTracker()
-
-# For image input
->>> pose_landmarks, _ = pose_tracker.run(input_file='/path/to/input/file', output_file='/path/to/output/file')
->>> pose_landmarks, annotated_image = pose_tracker.run(input_file='/path/to/file')
-
-# For live camera input
-# (Press Esc within the output image window to stop the run or let it self terminate after 30 seconds.)
->>> pose_tracker.run_live()
-
-# Close the tracker.
->>> pose_tracker.close()
-```
-
-Tip: Use command `deactivate` to exit the Python virtual environment.
-
-### Building Python package from source
-
-Follow these steps only if you have local changes and need to build the Python
-package from source. Otherwise, we strongly encourage our users to simply run
-`pip install mediapipe`, more convenient and much faster.
+Follow the steps below only if you have local changes and need to build the
+Python package from source. Otherwise, we strongly encourage our users to simply
+run `pip install mediapipe`, more convenient and much faster.
 
 1.  Make sure that Bazel and OpenCV are correctly installed and configured for
     MediaPipe. Please see [Installation](./install.md) for how to setup Bazel

docs/getting_started/install.md

@@ -12,7 +12,7 @@ nav_order: 1
 {:toc}
 ---
 
-Note: To interoperate with OpenCV, OpenCV 3.x and above are preferred. OpenCV
+Note: To interoperate with OpenCV, OpenCV 3.x to 4.1 are preferred. OpenCV
 2.x currently works but interoperability support may be deprecated in the
 future.
 

docs/index.md

@@ -35,9 +35,9 @@ Object Detection
 []()                                                                                      | Android | iOS | Desktop | Python | Web | Coral
 :---------------------------------------------------------------------------------------- | :-----: | :-: | :-----: | :----: | :-: | :---:
 [Face Detection](https://google.github.io/mediapipe/solutions/face_detection)                   | ✅       | ✅   | ✅       |        | ✅   | ✅
-[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)                             | ✅       | ✅   | ✅       |        |     |
+[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)                             | ✅       | ✅   | ✅       | ✅      |     |
 [Iris](https://google.github.io/mediapipe/solutions/iris)                                       | ✅       | ✅   | ✅       |        | ✅   |
-[Hands](https://google.github.io/mediapipe/solutions/hands)                                     | ✅       | ✅   | ✅       |        | ✅   |
+[Hands](https://google.github.io/mediapipe/solutions/hands)                                     | ✅       | ✅   | ✅       | ✅      | ✅   |
 [Pose](https://google.github.io/mediapipe/solutions/pose)                                       | ✅       | ✅   | ✅       | ✅      | ✅   |
 [Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation)             | ✅       |     | ✅       |        | ✅   |
 [Object Detection](https://google.github.io/mediapipe/solutions/object_detection)               | ✅       | ✅   | ✅       |        |     | ✅
@@ -53,6 +53,19 @@ See also
 [MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)
 for ML models released in MediaPipe.
 
+## MediaPipe in 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 in:
+
+*   [MediaPipe Face Mesh](../solutions/pose.md#python) and
+    [colab](https://mediapipe.page.link/face_mesh_py_colab)
+*   [MediaPipe Hands](../solutions/pose.md#python) and
+    [colab](https://mediapipe.page.link/hands_py_colab)
+*   [MediaPipe Pose](../solutions/pose.md#python) and
+    [colab](https://mediapipe.page.link/pose_py_colab)
+
 ## MediaPipe on the Web
 
 MediaPipe on the Web is an effort to run the same ML solutions built for mobile

docs/solutions/face_mesh.md

@@ -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