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GitOrigin-RevId: f7d09ed033907b893638a8eb4148efa11c0f09a6
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MediaPipe Team
2020-11-04 19:09:58 -05:00
committed by chuoling
parent a8d6ce95c4
commit f96eadd6df
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docs/solutions/hands.md
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@@ -55,13 +55,21 @@ frame, and only when the landmark model could no longer identify hand presence
is palm detection invoked to relocalize the hand.
The pipeline is implemented as a MediaPipe
[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt),
which internally utilizes a
[palm/hand detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/subgraphs/hand_detection_gpu.pbtxt),
a
[hand landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/subgraphs/hand_landmark_gpu.pbtxt)
and a
[renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/subgraphs/renderer_gpu.pbtxt).
[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt)
that uses a
[hand landmark tracking subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt)
from the
[hand landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark),
and renders using a dedicated
[hand renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_gpu.pbtxt).
The
[hand landmark tracking subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt)
internally uses a
[hand landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_gpu.pbtxt)
from the same module and a
[palm detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_gpu.pbtxt)
from the
[palm detection module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection).
Note: To visualize a graph, copy the graph and paste it into
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
@@ -146,34 +154,11 @@ to visualize its associated subgraphs, please see
* iOS target:
[`mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/handtrackinggpu/BUILD)
#### With Multi-hand Support
* Graph:
[`mediapipe/graphs/hand_tracking/multi_hand_tracking_mobile.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/multi_hand_tracking_mobile.pbtxt)
* Android target:
[(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1Wk6V9EVaz1ks_MInPqqVGvvJD01SGXDc)
[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/multihandtrackinggpu:multihandtrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/multihandtrackinggpu/BUILD)
* iOS target:
[`mediapipe/examples/ios/multihandtrackinggpu:MultiHandTrackingGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/multihandtrackinggpu/BUILD)
There are two key differences between this graph and that in the
[main example](#main-example) (which handles only one hand):
1. There is a `NormalizedRectVectorHasMinSize` calculator, that checks if in
input vector of `NormalizedRect` objects has a minimum size equal to `N`. In
this graph, if the vector contains fewer than `N` objects,
`MultiHandDetection` subgraph runs. Otherwise, the `GateCalculator` doesn't
send any image packets to the `MultiHandDetection` subgraph. This way, the
main graph is efficient in that it avoids running the costly hand detection
step when there are already `N` hands in the frame.
2. The `MergeCalculator` has been replaced by the `AssociationNormRect`
calculator. This `AssociationNormRect` takes as input a vector of
`NormalizedRect` objects from the `MultiHandDetection` subgraph on the
current frame, and a vector of `NormalizedRect` objects from the
`MultiHandLandmark` subgraph from the previous frame, and performs an
association operation between these objects. This calculator ensures that
the output vector doesn't contain overlapping regions based on the specified
`min_similarity_threshold`.
Tip: Maximum number of hands to detect/process is set to 2 by default. To change
it, for Android modify `NUM_HANDS` in
[MainActivity.java](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/MainActivity.java),
and for iOS modify `kNumHands` in
[HandTrackingViewController.mm](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/handtrackinggpu/HandTrackingViewController.mm).
#### Palm/Hand Detection Only (no landmarks)
@@ -187,8 +172,6 @@ There are two key differences between this graph and that in the
### Desktop
#### Main Example
* Running on CPU
* Graph:
[`mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt)
@@ -196,22 +179,101 @@ There are two key differences between this graph and that in the
[`mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/hand_tracking/BUILD)
* Running on GPU
* Graph:
[`mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt)
[`mediapipe/graphs/hand_tracking/hand_tracking_desktop_live_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/hand_tracking_desktop_gpu.pbtxt)
* Target:
[`mediapipe/examples/desktop/hand_tracking:hand_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/hand_tracking/BUILD)
#### With Multi-hand Support
Tip: Maximum number of hands to detect/process is set to 2 by default. To change
it, in the graph file modify the option of `ConstantSidePacketCalculator`.
* Running on CPU
* Graph:
[`mediapipe/graphs/hand_tracking/multi_hand_tracking_desktop_live.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/multi_hand_tracking_desktop_live)
* Target:
[`mediapipe/examples/desktop/multi_hand_tracking:multi_hand_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/multi_hand_tracking/BUILD)
* Running on GPU
* Graph:
[`mediapipe/graphs/hand_tracking/multi_hand_tracking_mobile.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/hand_tracking/multi_hand_tracking_mobile.pbtxt)
* Target:
[`mediapipe/examples/desktop/multi_hand_tracking:multi_hand_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/multi_hand_tracking/BUILD)
### 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/hands_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/hands.py and copy it over. -->
```python
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
# For static images:
hands = mp_hands.Hands(
static_image_mode=True,
max_num_hands=2,
min_detection_confidence=0.7)
for idx, file in enumerate(file_list):
# Read an image, flip it around y-axis for correct handedness output (see
# above).
image = cv2.flip(cv2.imread(file), 1)
# Convert the BGR image to RGB before processing.
results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
# Print handedness and draw hand landmarks on the image.
print('handedness:', results.multi_handedness)
if not results.multi_hand_landmarks:
continue
annotated_image = image.copy()
for hand_landmarks in results.multi_hand_landmarks:
print('hand_landmarks:', hand_landmarks)
mp_drawing.draw_landmarks(
annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
cv2.imwrite(
'/tmp/annotated_image' + str(idx) + '.png', cv2.flip(image, 1))
hands.close()
# For webcam input:
hands = mp_hands.Hands(
min_detection_confidence=0.7, min_tracking_confidence=0.5)
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 = hands.process(image)
# Draw the hand annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
mp_drawing.draw_landmarks(
image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
cv2.imshow('MediaPipe Hands', image)
if cv2.waitKey(5) & 0xFF == 27:
break
hands.close()
cap.release()
```
Tip: Use command `deactivate` to exit the Python virtual environment.
### Web