Zong Fan CasiaFan

## tic_tac_go.py
"""
Modified from github repo: shakedzy/tic_tac_toe
"""
import numpy as np
import torch
import torch.nn as nn
import os
import time
import random
from collections import deque

## CBM3D.py
# -*- coding: utf-8 -*-

import cv2
# import PSNR
import numpy as np
import pysnooper

cv2.setUseOptimized(True)

# Parameters initialization

## opencv_heatmap.py
import heatmap
import cv2

def use_heatmap(image, box_centers):
    hm = heatmap.Heatmap()
    img = hm.heatmap(box_centers, dotsize=200, size=(image.shape[1], image.shape[0]), opacity=128, area=((0, 0), (image.shape[1], image.shape[0])))
    return img

img = "/path/to/image.jpg"
centers = [(10, 20), (30, 40) ] # centers of heatmaps

## onnx_tensorrt_backend_example.py
import onnx
import argparse
import onnx_tensorrt.backend as backend
import numpy as np
import time

def main():
    parser = argparse.ArgumentParser(description="Onnx runtime engine.")
    parser.add_argument(
        "--onnx", default="/home/arkenstone/test_face_model/res50/mxnet_exported_mnet.onnx",

## onnx_tensorrt_inference.py
import tensorrt as trt
import numpy as np
import pycuda.autoinit
import pycuda.driver as cuda
import time

model_path = "model.onnx"
input_size = 32

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

## image_client_for_trt_serving.py
import argparse
import numpy as np
import os
import tensorrtserver.api as tapi
import tensorrtserver.api.model_config_pb2 as model_config
import cv2
import queue as q


def model_dtype_to_np(model_dtype):

## deepstream_gstreamer_decoding_with_python.py
import subprocess as sp
import cv2
import numpy as np

cmd = ["gst-launch-1.0",
       "rtspsrc", "location=rtsp://admin:password@192.168.2.71/Streaming/Channels/1", "latency=100", "!",
       "queue", "!",
       "rtph264depay", "!",
       "h264parse", "!",
       "nvv4l2decoder", "drop-frame-interval=2", "!",

## ubuntu1804_dGPU_install_nv_deepstream.dockerfile
From ubuntu:18.04 as base

# install github and vim
RUN apt-get install -y vim wget gnupg

ENV DEBIAN_FRONTEND "noninteractive" # to skip any interactive configurations during installation
RUN apt-get install -yq keyboard-configuration

# install gstreamer
RUN apt install -y \

## ffmpeg_python_with_gpu_acceleration.py
import subprocess as sp
import cv2
import numpy as np
from PIL import Image
import tensorflow as tf

ffmpeg_cmd_1 = ["./ffmpeg",  "-y",
              "-hwaccel",  "nvdec",
              "-c:v",  "h264_cuvid",
              "-vsync", "0",

## tf_model_post_training_quantization.py
import tensorflow as tf
from tensorflow.core.framework import types_pb2, graph_pb2, attr_value_pb2
from tensorflow.tools.graph_transforms import TransformGraph
from google.protobuf import text_format
import numpy as np

# object detection api input and output nodes
input_name = "image_tensor"
output_names = ["detection_boxes", "detection_classes", "detection_scores", "num_detections"]
# Const should be float32 in object detection api during nms (see here: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/non-max-suppression-v4.html)
	"""
	Modified from github repo: shakedzy/tic_tac_toe
	"""
	import numpy as np
	import torch
	import torch.nn as nn
	import os
	import time
	import random
	from collections import deque
	# -- coding: utf-8 --

	import cv2
	# import PSNR
	import numpy as np
	import pysnooper

	cv2.setUseOptimized(True)

	# Parameters initialization
	import heatmap
	import cv2

	def use_heatmap(image, box_centers):
	hm = heatmap.Heatmap()
	img = hm.heatmap(box_centers, dotsize=200, size=(image.shape[1], image.shape[0]), opacity=128, area=((0, 0), (image.shape[1], image.shape[0])))
	return img

	img = "/path/to/image.jpg"
	centers = [(10, 20), (30, 40) ] # centers of heatmaps
	import onnx
	import argparse
	import onnx_tensorrt.backend as backend
	import numpy as np
	import time

	def main():
	parser = argparse.ArgumentParser(description="Onnx runtime engine.")
	parser.add_argument(
	"--onnx", default="/home/arkenstone/test_face_model/res50/mxnet_exported_mnet.onnx",
	import tensorrt as trt
	import numpy as np
	import pycuda.autoinit
	import pycuda.driver as cuda
	import time

	model_path = "model.onnx"
	input_size = 32

	TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
	import subprocess as sp
	import cv2
	import numpy as np

	cmd = ["gst-launch-1.0",
	"rtspsrc", "location=rtsp://admin:password@192.168.2.71/Streaming/Channels/1", "latency=100", "!",
	"queue", "!",
	"rtph264depay", "!",
	"h264parse", "!",
	"nvv4l2decoder", "drop-frame-interval=2", "!",
	From ubuntu:18.04 as base

	# install github and vim
	RUN apt-get install -y vim wget gnupg

	ENV DEBIAN_FRONTEND "noninteractive" # to skip any interactive configurations during installation
	RUN apt-get install -yq keyboard-configuration

	# install gstreamer
	RUN apt install -y \
	import tensorflow as tf
	from tensorflow.core.framework import types_pb2, graph_pb2, attr_value_pb2
	from tensorflow.tools.graph_transforms import TransformGraph
	from google.protobuf import text_format
	import numpy as np

	# object detection api input and output nodes
	input_name = "image_tensor"
	output_names = ["detection_boxes", "detection_classes", "detection_scores", "num_detections"]
	# Const should be float32 in object detection api during nms (see here: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/non-max-suppression-v4.html)