fixed errors in docs
Signed-off-by: Pratyay Banerjee <putubanerjee23@gmail.com>
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@ -14,13 +14,8 @@ as the primary developer documentation site for MediaPipe as of April 3, 2023.*
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*This notice and web page will be removed on June 1, 2023.*
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----
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<br><br><br><br><br><br><br><br><br><br>
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<br><br><br><br><br><br><br><br><br><br>
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<br><br><br><br><br><br><br><br><br><br>
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--------------------------------------------------------------------------------
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<br/>
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## Live ML anywhere
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@ -53,7 +53,7 @@ calculators need only to be *thread-compatible* and not *thread-safe*.
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In order to enable one calculator to process multiple inputs in parallel, there
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are two possible approaches:
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1. Define multiple calulator nodes and dispatch input packets to all nodes.
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1. Define multiple calculator nodes and dispatch input packets to all nodes.
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2. Make the calculator thread-safe and configure its [`max_in_flight`] setting.
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The first approach can be followed using the calculators designed to distribute
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@ -95,12 +95,12 @@ while the application is running:
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The first approach has the advantage of leveraging [`CalculatorGraphConfig`]
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processing tools such as "subgraphs". The second approach has the advantage of
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allowing active calculators and packets to remain in-flight while settings
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change. Mediapipe contributors are currently investigating alternative approaches
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change. MediaPipe contributors are currently investigating alternative approaches
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to achieve both of these advantages.
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### How to process realtime input streams
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The mediapipe framework can be used to process data streams either online or
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The MediaPipe framework can be used to process data streams either online or
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offline. For offline processing, packets are pushed into the graph as soon as
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calculators are ready to process those packets. For online processing, one
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packet for each frame is pushed into the graph as that frame is recorded.
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@ -120,8 +120,8 @@ allows you to make use of automatic provisioning (see later section).
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To install applications on an iOS device, you need a provisioning profile. There
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are two options:
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1. Automatic provisioning. This allows you to build and install an app to your
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personal device. The provisining profile is managed by Xcode, and has to be
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1. Automatic provisioning. This allows you to build and install an app on your
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personal device. The provisioning profile is managed by Xcode, and has to be
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updated often (it is valid for about a week).
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2. Custom provisioning. This uses a provisioning profile associated with an
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@ -186,7 +186,7 @@ Profiles"`. If there are none, generate and download a profile on
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Note: if you had previously set up automatic provisioning, you should remove the
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`provisioning_profile.mobileprovision` symlink in each example's directory,
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since it will take precedence over the common one. You can also overwrite it
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with you own profile if you need a different profile for different apps.
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with your own profile if you need a different profile for different apps.
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1. Open `mediapipe/examples/ios/bundle_id.bzl`, and change the
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`BUNDLE_ID_PREFIX` to a prefix associated with your provisioning profile.
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@ -203,7 +203,7 @@ Note: When you ask Xcode to run an app, by default it will use the Debug
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configuration. Some of our demos are computationally heavy; you may want to use
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the Release configuration for better performance.
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Note: Due to an imcoptibility caused by one of our dependencies, MediaPipe
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Note: Due to an incompatibility caused by one of our dependencies, MediaPipe
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cannot be used for apps running on the iPhone Simulator on Apple Silicon (M1).
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Tip: To switch build configuration in Xcode, click on the target menu, choose
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@ -81,7 +81,7 @@ np.ndarray | mp::Matrix | create_ma
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Google Proto Message | Google Proto Message | create_proto(proto) | get_proto(packet)
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List\[Proto\] | std::vector\<Proto\> | n/a | get_proto_list(packet)
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It's not uncommon that users create custom C++ classes and and send those into
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It's not uncommon that users create custom C++ classes and send those into
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the graphs and calculators. To allow the custom classes to be used in Python
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with MediaPipe, you may extend the Packet API for a new data type in the
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following steps:
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@ -234,7 +234,7 @@ three stages: initialization and setup, graph run, and graph shutdown.
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output_packets.append(mp.packet_getter.get_str(packet)))
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```
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Option 2. Initialize a CalculatorGraph with with a binary protobuf file, and
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Option 2. Initialize a CalculatorGraph with a binary protobuf file, and
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observe the output stream(s).
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```python
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@ -75,7 +75,7 @@ previous frame as a guide to the object region on the current one. However,
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during fast movements, the tracker can lose the target, which requires the
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detector to re-localize it in the image. MediaPipe Holistic uses
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[pose](./pose.md) prediction (on every frame) as an additional ROI prior to
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reduce the response time of the pipeline when reacting to fast movements. This
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reducing the response time of the pipeline when reacting to fast movements. This
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also enables the model to retain semantic consistency across the body and its
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parts by preventing a mixup between left and right hands or body parts of one
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person in the frame with another.
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@ -143,7 +143,7 @@ The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks
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:----------------------------------------------------------------------------------------------: |
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*Fig 4. 33 pose landmarks.* |
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Optionally, MediaPipe Pose can predicts a full-body
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Optionally, MediaPipe Pose can predict a full-body
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[segmentation mask](#segmentation_mask) represented as a two-class segmentation
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(human or background).
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