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# This allows us to read the cities.json file. The format of the file
# is 'Javascript Object Notation' (JSON). It's a way of representing
# essential data structures, like lists and key-value pairs.

import json

# Our data will be in this format:
#    {
#        "city": "Beloit", 

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cmake -DPYTHON_DEFAULT_INTERPRETER=$(which python3) -DBUILD_opencv_python3=ON -DPYTHON3_LIBRARY=/opt/homebrew/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/lib/libpython3.9.dylib -DPYTHON3_INCLUDE_DIR=/opt/homebrew/Cellar/[email protected]/3.9.12/Frameworks/Python.framework/Versions/3.9/include/python3.9 ..

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diff --git a/src/runtime/opencl.cpp b/src/runtime/opencl.cpp
index 3fb98b4..4b5eca2 100644
--- a/src/runtime/opencl.cpp
+++ b/src/runtime/opencl.cpp

+} // extern C
+
+#include <map>
+struct kernel_cache_t { bool cached; cl_kernel kernel; inline kernel_cache_t() : cached(false) {} };
+std::map<const char*, kernel_cache_t> precompiled_kernels;

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Snippets collected/distilled from gists/blog posts/etc. Combined here for fellow web-searchers -- goal is to have an easy/minimal sink for in-app use, and then forward that stream in another process.

Read camera, push to UDP sink (usually from appsrc, here v4l2 camera):

$ gst-launch-1.0 -vvvv v4l2src ! 'video/x-raw, width=640, height=480, framerate=30/1' ! videoconvert ! x264enc pass=qual quantizer=20 tune=zerolatency ! rtph264pay ! udpsink port=1234

Visualize above:

$ gst-launch-1.0 -vvv udpsrc port=1234 ! "application/x-rtp,media=(string)video,clock-rate=(int)90000,encoding-name=(string)H264" ! rtph264depay ! h264parse ! decodebin ! videoconvert ! xvimagesink sync=false

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keytool -genkey -v -keystore my-release-key.keystore -alias alias_name -keyalg RSA -keysize 2048 -validity 10000
jarsigner -verbose -sigalg SHA1withRSA -digestalg SHA1 -keystore my-release-key.keystore some.apk alias_name

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bazel build tensorflow/tools/graph_transforms:transform_graph
bazel-bin/tensorflow/tools/graph_transforms/transform_graph --in_graph=../models/object_detection/ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb --out_graph=transformed_inference_graph.pb --inputs='image_tensor' --outputs='detection_boxes,detection_scores,detection_classes,num_detections' --transforms='
  add_default_attributes
  strip_unused_nodes(type=float)
  remove_nodes(op=CheckNumerics)
  fold_constants(ignore_errors=true)
  fold_batch_norms
  fold_old_batch_norms
  fuse_resize_pad_and_conv
  fuse_pad_and_conv

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layer {
  name: "Data1"
  type: "Data"
  top: "Data1"
  top: "Data2"
  transform_param {
    mean_file: "/home/zl499/caffe/examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "/home/zl499/caffe/examples/cifar10/cifar10_train_lmdb"

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name: "PVANET-lite"

# https://raw.githubusercontent.com/sanghoon/pva-faster-rcnn/master/models/pvanet/lite/original.pt

################################################################################
## Input
################################################################################

layer {
  name: 'input-data'

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name: "SqueezeNet_Residual"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {