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google-ai-edge / mediapipe

Cross-platform, customizable ML solutions for live and streaming media.
https://ai.google.dev/edge/mediapipe
Apache License 2.0
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Run hand_tracking_cpu in MacOS #894

Closed www516717402 closed 4 years ago

www516717402 commented 4 years ago

Great project. When I build this project in MacOS, hello example is running success. However, Try to run bazel run --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu for hand tracking example, Outputs log as follow:

bazel run --define MEDIAPIPE_DISABLE_GPU=1 \                 3259  15:41:23 
    mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu --verbose_failures
WARNING: Download from https://mirror.bazel.build/github.com/tensorflow/tensorflow/archive/7c09d15f9fcc14343343c247ebf5b8e0afe3e4aa.tar.gz failed: class com.google.devtools.build.lib.bazel.repository.downloader.UnrecoverableHttpException GET returned 404 Not Found
WARNING: Download from http://mirror.tensorflow.org/github.com/bazelbuild/rules_closure/archive/cf1e44edb908e9616030cc83d085989b8e6cd6df.tar.gz failed: class com.google.devtools.build.lib.bazel.repository.downloader.UnrecoverableHttpException GET returned 404 Not Found
DEBUG: Rule 'rules_cc' indicated that a canonical reproducible form can be obtained by modifying arguments sha256 = "a07fc8b90c22e42962142504438a84b31567976adcd9ee1a4c9f75d2e3396f8d"
DEBUG: Repository rules_cc instantiated at:
  no stack (--record_rule_instantiation_callstack not enabled)
Repository rule http_archive defined at:
  /private/var/tmp/_bazel_wujinyi/6feed3ec0af0d4774c44318da51ee31d/external/bazel_tools/tools/build_defs/repo/http.bzl:336:31: in <toplevel>
WARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/XNNPACK/archive/5cb16e7ace0fcdcab164af01620a606ba828a3be.zip failed: class com.google.devtools.build.lib.bazel.repository.downloader.UnrecoverableHttpException GET returned 404 Not Found
WARNING: Download from https://mirror.bazel.build/github.com/Maratyszcza/FP16/archive/3c54eacb74f6f5e39077300c5564156c424d77ba.zip failed: class com.google.devtools.build.lib.bazel.repository.downloader.UnrecoverableHttpException GET returned 404 Not Found
INFO: Analyzed target //mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu (0 packages loaded, 0 targets configured).
INFO: Found 1 target...
Target //mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu up-to-date:
  bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_cpu
INFO: Elapsed time: 0.249s, Critical Path: 0.00s
INFO: 0 processes.
INFO: Build completed successfully, 1 total action
INFO: Build completed successfully, 1 total action
I20200709 15:41:25.287240 421121472 demo_run_graph_main.cc:48]

 Get calculator graph config contents: # MediaPipe graph that performs hand tracking on desktop with TensorFlow Lite
# on CPU.
# Used in the example in
# mediapipie/examples/desktop/hand_tracking:hand_tracking_cpu.

# Images coming into and out of the graph.
input_stream: "input_video"
output_stream: "output_video"

# Caches a hand-presence decision fed back from HandLandmarkSubgraph, and upon
# the arrival of the next input image sends out the cached decision with the
# timestamp replaced by that of the input image, essentially generating a packet
# that carries the previous hand-presence decision. Note that upon the arrival
# of the very first input image, an empty packet is sent out to jump start the
# feedback loop.
node {
  calculator: "PreviousLoopbackCalculator"
  input_stream: "MAIN:input_video"
  input_stream: "LOOP:hand_presence"
  input_stream_info: {
    tag_index: "LOOP"
    back_edge: true
  }
  output_stream: "PREV_LOOP:prev_hand_presence"
}

# Drops the incoming image if HandLandmarkSubgraph was able to identify hand
# presence in the previous image. Otherwise, passes the incoming image through
# to trigger a new round of hand detection in HandDetectionSubgraph.
node {
  calculator: "GateCalculator"
  input_stream: "input_video"
  input_stream: "DISALLOW:prev_hand_presence"
  output_stream: "hand_detection_input_video"

  node_options: {
    [type.googleapis.com/mediapipe.GateCalculatorOptions] {
      empty_packets_as_allow: true
    }
  }
}

# Subgraph that detections hands (see hand_detection_cpu.pbtxt).
node {
  calculator: "HandDetectionSubgraph"
  input_stream: "hand_detection_input_video"
  output_stream: "DETECTIONS:palm_detections"
  output_stream: "NORM_RECT:hand_rect_from_palm_detections"
}

# Subgraph that localizes hand landmarks (see hand_landmark_cpu.pbtxt).
node {
  calculator: "HandLandmarkSubgraph"
  input_stream: "IMAGE:input_video"
  input_stream: "NORM_RECT:hand_rect"
  output_stream: "LANDMARKS:hand_landmarks"
  output_stream: "NORM_RECT:hand_rect_from_landmarks"
  output_stream: "HANDEDNESS:handedness"
  output_stream: "PRESENCE:hand_presence"
}

# Caches a hand rectangle fed back from HandLandmarkSubgraph, and upon the
# arrival of the next input image sends out the cached rectangle with the
# timestamp replaced by that of the input image, essentially generating a packet
# that carries the previous hand rectangle. Note that upon the arrival of the
# very first input image, an empty packet is sent out to jump start the
# feedback loop.
node {
  calculator: "PreviousLoopbackCalculator"
  input_stream: "MAIN:input_video"
  input_stream: "LOOP:hand_rect_from_landmarks"
  input_stream_info: {
    tag_index: "LOOP"
    back_edge: true
  }
  output_stream: "PREV_LOOP:prev_hand_rect_from_landmarks"
}

# Merges a stream of hand rectangles generated by HandDetectionSubgraph and that
# generated by HandLandmarkSubgraph into a single output stream by selecting
# between one of the two streams. The former is selected if the incoming packet
# is not empty, i.e., hand detection is performed on the current image by
# HandDetectionSubgraph (because HandLandmarkSubgraph could not identify hand
# presence in the previous image). Otherwise, the latter is selected, which is
# never empty because HandLandmarkSubgraphs processes all images (that went
# through FlowLimiterCaculator).
node {
  calculator: "MergeCalculator"
  input_stream: "hand_rect_from_palm_detections"
  input_stream: "prev_hand_rect_from_landmarks"
  output_stream: "hand_rect"
}

# Subgraph that renders annotations and overlays them on top of the input
# images (see renderer_cpu.pbtxt).
node {
  calculator: "RendererSubgraph"
  input_stream: "IMAGE:input_video"
  input_stream: "LANDMARKS:hand_landmarks"
  input_stream: "NORM_RECT:hand_rect"
  input_stream: "DETECTIONS:palm_detections"
  input_stream: "HANDEDNESS:handedness"
  output_stream: "IMAGE:output_video"
}

I20200709 15:41:25.296059 421121472 demo_run_graph_main.cc:54] Initialize the calculator graph.
I20200709 15:41:25.314254 421121472 demo_run_graph_main.cc:58] Initialize the camera or load the video.
I20200709 15:41:26.734478 421121472 demo_run_graph_main.cc:79] Start running the calculator graph.
I20200709 15:41:26.737205 421121472 demo_run_graph_main.cc:84] Start grabbing and processing frames.
INFO: Initialized TensorFlow Lite runtime.
ERROR: Could not open 'mediapipe/models/palm_detection.tflite'.
E20200709 15:41:27.144917 421121472 demo_run_graph_main.cc:149] Failed to run the graph: Graph has errors:
Calculator::Open() for node "[handdetectionsubgraph__TfLiteInferenceCalculator, TfLiteInferenceCalculator with output stream: handdetectionsubgraph__detection_tensors]" failed: ; Failed to load model from path.lculators/tflite/tflite_inference_calculator.cc:756)

Hope your reply.

www516717402 commented 4 years ago

Be solved by input param.