ipynb 라는 확장자가 보여서 확인 중

[링크 : https://github.com/saunack/MobileNetv2-SSD]

 

아나콘다 깔고

거기서 jupiter notebook을 설치하면 된다고

[링크 : https://mananacho.tistory.com/31]

[링크 : https://blog.naver.com/tamiel/221956194782]

 

커맨드 라인으로는 먼가 복잡한데, 쥬피터 없이 돌리는것도 아니고 무슨 의미가 있나 싶긴하다.

$ jupyter nbconvert --execute --to notebook lda2.ipynb

[링크 : https://data-scient2st.tistory.com/234]

Posted by 구차니

문서를  찾다가 지쳐서 걍 실행하고 인자를 보는걸로..

root        3019     925 72 06:38 ?        00:00:25 /usr/bin/python3 /home/root/.nxp-demo-experience/scripts/machine_learning/MLDemoLauncher.py detect

 

root@imx8mpevk:~/.nxp-demo-experience/scripts/machine_learning# cat MLDemoLauncher.py 
#!/usr/bin/env python3

"""
Copyright 2021-2023 NXP

SPDX-License-Identifier: BSD-2-Clause

This script launches the NNStreamer ML Demos using a UI to pick settings.
"""

import gi
import os
import sys
import glob
from gi.repository import Gtk, GLib, Gio

gi.require_version("Gtk", "3.0")

sys.path.append("/home/root/.nxp-demo-experience/scripts/")
import utils


class MLLaunch(Gtk.Window):
    """The GUI window for the ML demo launcher"""

    def __init__(self, demo):
        """Creates the UI window"""
        # Initialization
        self.demo = demo
        super().__init__(title=demo)
        self.set_default_size(450, 200)
        self.set_resizable(False)

        # Get platform
        self.platform = os.uname().nodename
     
        # OpenVX graph caching is not available on i.MX 8QuadMax platform.
        if self.platform != "imx8qmmek" :
            os.environ["VIV_VX_CACHE_BINARY_GRAPH_DIR"] = "/home/root/.cache/gopoint"
            os.environ["VIV_VX_ENABLE_CACHE_GRAPH_BINARY"] = "1"

        # Get widget properties
        devices = []
        if self.demo != "brand" and self.demo != "selfie_nn":
            if self.platform != "imx93evk":
                devices.append("Example Video")

        for device in glob.glob("/dev/video*"):
            devices.append(device)

        backends_available = ["CPU"]
        if (
            os.path.exists("/usr/lib/libvx_delegate.so")
            and self.demo != "pose"
            and self.demo != "selfie_nn"
        ):
            backends_available.insert(1, "GPU")
        if os.path.exists("/usr/lib/libneuralnetworks.so") and self.demo != "brand" and self.platform != "imx8qmmek":
            backends_available.insert(0, "NPU")
        if os.path.exists("/usr/lib/libethosu_delegate.so"):
            backends_available.insert(0, "NPU")
            backends_available.pop()

        displays_available = ["Weston"]

        colors_available = ["Red", "Green", "Blue", "Black", "White"]

        demo_modes_available = ["Background Substitution", "Segmentation Mask"]

        # Create widgets
        main_grid = Gtk.Grid.new()
        device_label = Gtk.Label.new("Source")
        self.device_combo = Gtk.ComboBoxText()
        backend_label = Gtk.Label.new("Backend")
        self.backend_combo = Gtk.ComboBoxText()
        self.display_combo = Gtk.ComboBoxText()
        self.launch_button = Gtk.Button.new_with_label("Run")
        self.status_bar = Gtk.Label.new()
        header = Gtk.HeaderBar()
        quit_button = Gtk.Button()
        quit_icon = Gio.ThemedIcon(name="process-stop-symbolic")
        quit_image = Gtk.Image.new_from_gicon(quit_icon, Gtk.IconSize.BUTTON)
        separator = Gtk.Separator.new(0)
        time_title_label = Gtk.Label.new("Video Refresh")
        self.time_label = Gtk.Label.new("--.-- ms")
        self.fps_label = Gtk.Label.new("-- FPS")
        inference_title_label = Gtk.Label.new("Inference Time")
        self.inference_label = Gtk.Label.new("--.-- ms")
        self.ips_label = Gtk.Label.new("-- IPS")
        if self.demo != "selfie_nn":
            self.width_entry = self.r_scale = Gtk.Scale.new_with_range(
                Gtk.Orientation.HORIZONTAL, 300, 1920, 2
            )
            self.height_entry = self.r_scale = Gtk.Scale.new_with_range(
                Gtk.Orientation.HORIZONTAL, 300, 1080, 2
            )
            self.width_label = Gtk.Label.new("Height")
            self.height_label = Gtk.Label.new("Width")
            self.color_label = Gtk.Label.new("Label Color")
        else:
            self.color_label = Gtk.Label.new("Text Color")
            self.demo_mode = Gtk.Label.new("Demo Mode")
            self.mode_combo = Gtk.ComboBoxText()
        self.color_combo = Gtk.ComboBoxText()

        # Organize widgets
        self.add(main_grid)
        self.set_titlebar(header)

        quit_button.add(quit_image)
        header.pack_end(quit_button)

        main_grid.set_row_spacing(10)
        main_grid.set_border_width(10)

        main_grid.attach(device_label, 0, 1, 2, 1)
        device_label.set_hexpand(True)
        main_grid.attach(backend_label, 0, 2, 2, 1)
        # main_grid.attach(display_label, 0, 3, 2, 1)
        if self.demo != "selfie_nn":
            main_grid.attach(self.width_label, 0, 4, 2, 1)
            main_grid.attach(self.height_label, 0, 5, 2, 1)
            main_grid.attach(self.color_label, 0, 6, 2, 1)
        else:
            main_grid.attach(self.demo_mode, 0, 4, 2, 1)
            main_grid.attach(self.color_label, 0, 5, 2, 1)

        main_grid.attach(self.device_combo, 2, 1, 2, 1)
        self.device_combo.set_hexpand(True)
        main_grid.attach(self.backend_combo, 2, 2, 2, 1)
        # main_grid.attach(self.display_combo, 2, 3, 2, 1)
        if self.demo != "selfie_nn":
            main_grid.attach(self.width_entry, 2, 4, 2, 1)
            main_grid.attach(self.height_entry, 2, 5, 2, 1)
            main_grid.attach(self.color_combo, 2, 6, 2, 1)
        else:
            main_grid.attach(self.mode_combo, 2, 4, 2, 1)
            main_grid.attach(self.color_combo, 2, 5, 2, 1)

        main_grid.attach(self.launch_button, 0, 7, 4, 1)
        main_grid.attach(self.status_bar, 0, 8, 4, 1)

        main_grid.attach(separator, 0, 9, 4, 1)

        main_grid.attach(time_title_label, 0, 10, 2, 1)
        main_grid.attach(self.time_label, 0, 11, 1, 1)
        main_grid.attach(self.fps_label, 1, 11, 1, 1)
        main_grid.attach(inference_title_label, 2, 10, 2, 1)
        main_grid.attach(self.inference_label, 2, 11, 1, 1)
        main_grid.attach(self.ips_label, 3, 11, 1, 1)

        # Configure widgets
        for device in devices:
            self.device_combo.append_text(device)
        for backend in backends_available:
            self.backend_combo.append_text(backend)
        for display in displays_available:
            self.display_combo.append_text(display)
        for color in colors_available:
            self.color_combo.append_text(color)
        if self.demo == "selfie_nn":
            for mode in demo_modes_available:
                self.mode_combo.append_text(mode)

        self.device_combo.set_active(0)
        self.backend_combo.set_active(0)
        self.display_combo.set_active(0)
        self.color_combo.set_active(0)
        if self.demo != "selfie_nn":
            self.width_entry.set_value(1920)
            self.height_entry.set_value(1080)
            self.width_entry.set_sensitive(False)
            self.height_entry.set_sensitive(False)
        else:
            self.mode_combo.set_active(0)
        self.device_combo.connect("changed", self.on_source_change)
        self.launch_button.connect("clicked", self.start)
        quit_button.connect("clicked", exit)
        if self.demo == "detect":
            header.set_title("Detection Demo")
        elif self.demo == "id":
            header.set_title("Classification Demo")
        elif self.demo == "pose":
            header.set_title("Pose Demo")
        elif self.demo == "brand":
            header.set_title("Brand Demo")
        elif self.demo == "selfie_nn":
            header.set_title("Selfie Segmenter Demo")
        else:
            header.set_title("NNStreamer Demo")
        header.set_subtitle("NNStreamer Examples")

    def start(self, button):
        """Starts the ML Demo with selected settings"""
        self.update_time = GLib.get_monotonic_time()
        self.launch_button.set_sensitive(False)
        if self.color_combo.get_active_text() == "Red":
            r = 1
            g = 0
            b = 0
        elif self.color_combo.get_active_text() == "Blue":
            r = 0
            g = 0
            b = 1
        elif self.color_combo.get_active_text() == "Green":
            r = 0
            g = 1
            b = 0
        elif self.color_combo.get_active_text() == "Black":
            r = 0
            g = 0
            b = 0
        elif self.color_combo.get_active_text() == "White":
            r = 1
            g = 1
            b = 1
        else:
            r = 1
            g = 0
            b = 0
        if self.demo == "detect":
            if self.platform == "imx93evk":
                model = utils.download_file("mobilenet_ssd_v2_coco_quant_postprocess_vela.tflite")
            else:
                model = utils.download_file("mobilenet_ssd_v2_coco_quant_postprocess.tflite")
            labels = utils.download_file("coco_labels.txt")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("detect_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                if self.platform == "imx93evk":
                    error = "mobilenet_ssd_v2_coco_quant_postprocess_vela.tflite"
                else:
                    error = "mobilenet_ssd_v2_coco_quant_postprocess.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                error = "coco_labels.txt"
            elif device == -1 or device == -2 or device == -3:
                error = "detect_example.mov"
        if self.demo == "id":
            if self.platform == "imx93evk":
                model = utils.download_file("mobilenet_v1_1.0_224_quant_vela.tflite")
            else:
                model = utils.download_file("mobilenet_v1_1.0_224_quant.tflite")
            labels = utils.download_file("1_1.0_224_labels.txt")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("id_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                if self.platform == "imx93evk":
                    error = "mobilenet_v1_1.0_224_quant_vela.tflite"
                else:
                    error = "mobilenet_v1_1.0_224_quant.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                error = "1_1.0_224_labels.txt"
            elif device == -1 or device == -2 or device == -3:
                error = "id_example.mov"
        if self.demo == "pose":
            model = utils.download_file("posenet_resnet50_uint8_float32_quant.tflite")
            labels = utils.download_file("key_point_labels.txt")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("pose_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                error = "posenet_resnet50_uint8_float32_quant.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                error = "key_point_labels.txt"
            elif device == -1 or device == -2 or device == -3:
                error = "pose_example.mov"
        if self.demo == "brand":
            model = utils.download_file("brand_model.tflite")
            labels = utils.download_file("brand_labels.txt")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("brand_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                error = "brand_model.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                error = "brand_labels.txt"
            elif device == -1 or device == -2 or device == -3:
                error = "brand_example.mov"
        if self.demo == "selfie_nn":
            if self.platform == "imx93evk":
                model = utils.download_file(
                    "selfie_segmenter_landscape_int8_vela.tflite"
                )
            else:
                model = utils.download_file("selfie_segmenter_int8.tflite")
            # Labels refer to background img
            if self.platform == "imx93evk":
                labels = utils.download_file("bg_image_landscape.jpg")
            else:
                labels = utils.download_file("bg_image.jpg")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("selfie_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                if self.platform == "imx93evk":
                    error = "selfie_segmenter_landscape_int8_vela.tflite"
                else:
                    error = "selfie_segmenter_int8.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                if self.platform == "imx93evk":
                    error = "bg_image_landscape.jpg"
                else:
                    error = "bg_image.jpg"
            elif device == -1 or device == -2 or device == -3:
                error = "selfie_example.mov"
            if self.mode_combo.get_active_text() == "Background Substitution":
                set_mode = 0
            else:
                set_mode = 1

        if model == -1 or labels == -1 or device == -1:
            """
            dialog = Gtk.MessageDialog(
                transient_for=self,
                flags=0,
                message_type=Gtk.MessageType.ERROR,
                buttons=Gtk.ButtonsType.CANCEL,
                text="Cannot find files! The file that you requested" +
                " does not have any metadata that is related to it. " +
                "Please see /home/root/.nxp-demo-experience/downloads.txt" +
                " to see if the requested file exists! \n \n Cannot find:" +
                error)
            dialog.run()
            dialog.destroy()
            """
            self.status_bar.set_text("Cannot find files!")
            self.launch_button.set_sensitive(True)
            return
        if model == -2 or labels == -2 or device == -2:
            """
            dialog = Gtk.MessageDialog(
                transient_for=self,
                flags=0,
                message_type=Gtk.MessageType.ERROR,
                buttons=Gtk.ButtonsType.CANCEL,
                text="Cannot download files! The URL used to download the" +
                " file cannot be reached. If you are connected to the " +
                "internet, please check the /home/root/.nxp-demo-experience" +
                "/downloads.txt for the URL. For some regions, " +
                "these sites may be blocked. To install these manually," +
                " please go to the file listed above and provide the " +
                "path to the file in \"PATH\" \n \n Cannot download " + error)
            dialog.run()
            dialog.destroy()
            """
            self.status_bar.set_text("Download failed!")
            self.launch_button.set_sensitive(True)
            return
        if model == -3 or labels == -3 or device == -4:
            """
            dialog = Gtk.MessageDialog(
                transient_for=self,
                flags=0,
                message_type=Gtk.MessageType.ERROR,
                buttons=Gtk.ButtonsType.CANCEL,
                text="Invalid files! The files where not what we expected." +
                "If you are SURE that the files are correct, delete " +
                "the \"SHA\" value in /home/root/.nxp-demo-experience" +
                "/downloads.txt to bypass the SHA check. \n \n Bad SHA for " +
                error)
            dialog.run()
            dialog.destroy()
            """
            self.status_bar.set_text("Downloaded bad file!")
            self.launch_button.set_sensitive(True)
            return
        if self.demo == "detect":
            import nndetection

            example = nndetection.ObjectDetection(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.display_combo.get_active_text(),
                self.update_stats,
                self.width_entry.get_value(),
                self.height_entry.get_value(),
                r,
                g,
                b,
            )
            example.run()
        if self.demo == "id":
            import nnclassification

            example = nnclassification.NNStreamerExample(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.display_combo.get_active_text(),
                self.update_stats,
                self.width_entry.get_value(),
                self.height_entry.get_value(),
                r,
                g,
                b,
            )
            example.run_example()
        if self.demo == "pose":
            import nnpose

            example = nnpose.NNStreamerExample(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.display_combo.get_active_text(),
                self.update_stats,
                self.width_entry.get_value(),
                self.height_entry.get_value(),
                r,
                g,
                b,
            )
            example.run_example()
        if self.demo == "brand":
            import nnbrand

            example = nnbrand.NNStreamerExample(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.display_combo.get_active_text(),
                self.update_stats,
                self.width_entry.get_value(),
                self.height_entry.get_value(),
                r,
                g,
                b,
            )
            example.run_example()
        if self.demo == "selfie_nn":
            import selfie_segmenter

            example = selfie_segmenter.SelfieSegmenter(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.update_stats,
                set_mode,
                r,
                g,
                b,
            )
            example.run()

        self.launch_button.set_sensitive(True)

    def update_stats(self, time):
        """Callback used the update stats in GUI"""
        interval_time = (GLib.get_monotonic_time() - self.update_time) / 1000000
        if interval_time > 1:
            refresh_time = time.interval_time
            inference_time = time.tensor_filter.get_property("latency")

            if refresh_time != 0 and inference_time != 0:
                # Print pipeline information
                if self.demo == "selfie_nn" or self.demo == "id" or self.demo == "detect":
                    self.time_label.set_text(
                        "{:12.2f} ms".format(1.0 / time.current_framerate * 1000.0)
                    )
                    self.fps_label.set_text(
                        "{:12.2f} FPS".format(time.current_framerate)
                    )
                else:
                    self.time_label.set_text("{:12.2f} ms".format(refresh_time / 1000))
                    self.fps_label.set_text(
                        "{:12.2f} FPS".format(1 / (refresh_time / 1000000))
                    )
                # Print inference information
                self.inference_label.set_text(
                    "{:12.2f} ms".format(inference_time / 1000)
                )
                self.ips_label.set_text(
                    "{:12.2f} FPS".format(1 / (inference_time / 1000000))
                )
            self.update_time = GLib.get_monotonic_time()
        return True

    def on_source_change(self, widget):
        """Callback to lock sliders"""
        if self.demo != "selfie_nn":
            if self.device_combo.get_active_text() == "Example Video":
                self.width_entry.set_value(1920)
                self.height_entry.set_value(1080)
                self.width_entry.set_sensitive(False)
                self.height_entry.set_sensitive(False)
            else:
                self.width_entry.set_sensitive(True)
                self.height_entry.set_sensitive(True)


if __name__ == "__main__":
    if (
        len(sys.argv) != 2
        and sys.argv[1] != "detect"
        and sys.argv[1] != "id"
        and sys.argv[1] != "pose"
        and sys.argv[1] != "selfie_nn"
    ):
        print("Demos available: detect, id, pose, selfie_nn")
    else:
        win = MLLaunch(sys.argv[1])
        win.connect("destroy", Gtk.main_quit)
        win.show_all()
        Gtk.main()

 

아래가 실행되는 녀석인데 nndetection을 import 하니까 그걸 따라가서 보는 중.

그나저나 LGPL 이면 그냥 공개해도 되려나?

root@imx8mpevk:~/.nxp-demo-experience/scripts/machine_learning# find / -name nndetection.py
/run/media/root-mmcblk2p2/home/root/.nxp-demo-experience/scripts/machine_learning/nndetection.py
/home/root/.nxp-demo-experience/scripts/machine_learning/nndetection.py

root@imx8mpevk:~/.nxp-demo-experience/scripts/machine_learning# cat /home/root/.nxp-demo-experience/scripts/machine_learning/nndetection.py
#!/usr/bin/env python3

"""
Copyright SSAFY Team 1 <jangjongha.sw@gmail.com>
Copyright 2021-2023 NXP

SPDX-License-Identifier: LGPL-2.1-only
Original Source: https://github.com/nnstreamer/nnstreamer-example

This demo shows how you can use the NNStreamer to detect objects.

From the original source, this was modified to better work with the a
GUI and to get better performance on the i.MX 8M Plus and i.MX93.
"""

import os
import sys
import gi
import re
import logging
import numpy as np
import cairo

gi.require_version("Gst", "1.0")
gi.require_foreign("cairo")
from gi.repository import Gst, GObject, GLib

DEBUG = False


class ObjectDetection:
    """The class that manages the demo"""

    def __init__(
        self,
        platform,
        device,
        backend,
        model,
        labels,
        display="Weston",
        callback=None,
        width=1920,
        height=1080,
        r=1,
        g=0,
        b=0,
    ):
        """Creates an instance of the demo

        Arguments:
        device -- What camera or video file to use
        backend -- Whether to use NPU or CPU
        model -- the path to the model
        labels -- the path to the labels
        display -- Whether to use X11 or Weston
        callback -- Callback to pass stats to
        width -- Width of output
        height -- Height of output
        r -- Red value for labels
        g -- Green value for labels
        b -- Blue value for labels
        """
        self.loop = None
        self.pipeline = None
        self.running = False
        self.video_caps = None
        self.first_frame = True

        self.BOX_SIZE = 4
        self.LABEL_SIZE = 91
        self.DETECTION_MAX = 20
        self.MAX_OBJECT_DETECTION = 20

        self.Y_SCALE = 10.0
        self.X_SCALE = 10.0
        self.H_SCALE = 5.0
        self.W_SCALE = 5.0

        self.VIDEO_WIDTH = width
        self.VIDEO_HEIGHT = height
        self.MODEL_WIDTH = 300
        self.MODEL_HEIGHT = 300

        self.tflite_model = model
        self.label_path = labels
        self.device = device
        self.backend = backend
        self.display = display
        self.tflite_labels = []
        self.detected_objects = []
        self.callback = callback
        self.r = r
        self.b = b
        self.g = g
        self.platform = platform
        self.current_framerate = 1000

        # Define PXP or GPU2D converter
        if self.platform == "imx93evk":
            self.nxp_converter = "imxvideoconvert_pxp "
        else:
            self.nxp_converter = "imxvideoconvert_g2d "

        if not self.tflite_init():
            raise Exception

        Gst.init(None)

    def run(self):
        """Starts pipeline and run demo"""

        if self.backend == "CPU":
            if self.platform == "imx93evk":
                backend = "true:cpu custom=NumThreads:2"
            else:
                backend = "true:cpu custom=NumThreads:4"
        elif self.backend == "GPU":
            os.environ["USE_GPU_INFERENCE"] = "1"
            backend = (
                "true:gpu custom=Delegate:External," "ExtDelegateLib:libvx_delegate.so"
            )
        else:
            if self.platform == "imx93evk":
                backend = (
                    "true:npu custom=Delegate:External,"
                    "ExtDelegateLib:libethosu_delegate.so"
                )
            else:
                os.environ["USE_GPU_INFERENCE"] = "0"
                backend = (
                    "true:npu custom=Delegate:External,"
                    "ExtDelegateLib:libvx_delegate.so"
                )

        if self.display == "X11":
            display = "ximagesink name=img_tensor "
        elif self.display == "None":
            self.print_time = GLib.get_monotonic_time()
            display = "fakesink "
        else:
            display = "fpsdisplaysink name=img_tensor text-overlay=false video-sink=waylandsink sync=false"

        # main loop
        self.loop = GLib.MainLoop()
        self.old_time = GLib.get_monotonic_time()
        self.update_time = GLib.get_monotonic_time()
        self.reload_time = -1
        self.interval_time = 999999

        # Create decoder for video file
        if self.platform == "imx8qmmek":
            decoder = "h264parse ! v4l2h264dec "
        else:
            decoder = "vpudec "

        if "/dev/video" in self.device:
            gst_launch_cmdline = "v4l2src name=cam_src device=" + self.device
            gst_launch_cmdline += " ! " + self.nxp_converter + "! video/x-raw,width="
            gst_launch_cmdline += str(int(self.VIDEO_WIDTH)) + ",height="
            gst_launch_cmdline += str(int(self.VIDEO_HEIGHT))
            gst_launch_cmdline += ",framerate=30/1,format=BGRx ! tee name=t"
        else:
            gst_launch_cmdline = "filesrc location=" + self.device
            gst_launch_cmdline += " ! qtdemux ! " + decoder + "! tee name=t"

        gst_launch_cmdline += " t. ! " + self.nxp_converter + "!  video/x-raw,"
        gst_launch_cmdline += "width={:d},".format(self.MODEL_WIDTH)
        gst_launch_cmdline += "height={:d},".format(self.MODEL_HEIGHT)
        gst_launch_cmdline += " ! queue max-size-buffers=2 leaky=2 ! "
        gst_launch_cmdline += "videoconvert ! video/x-raw,format=RGB !"
        gst_launch_cmdline += " tensor_converter ! tensor_filter"
        gst_launch_cmdline += " framework=tensorflow-lite model="
        gst_launch_cmdline += self.tflite_model + " accelerator=" + backend
        gst_launch_cmdline += " silent=FALSE name=tensor_filter latency=1 ! "
        gst_launch_cmdline += "tensor_sink name=tensor_sink t. ! "
        gst_launch_cmdline += self.nxp_converter + "! "
        gst_launch_cmdline += "cairooverlay name=tensor_res ! "
        gst_launch_cmdline += "queue max-size-buffers=2 leaky=2 ! "
        gst_launch_cmdline += display

        self.pipeline = Gst.parse_launch(gst_launch_cmdline)

        # bus and message callback
        bus = self.pipeline.get_bus()
        bus.add_signal_watch()
        bus.connect("message", self.on_bus_message)

        self.tensor_filter = self.pipeline.get_by_name("tensor_filter")
        self.wayland_sink = self.pipeline.get_by_name("img_tensor")

        # tensor sink signal : new data callback
        tensor_sink = self.pipeline.get_by_name("tensor_sink")
        tensor_sink.connect("new-data", self.new_data_cb)

        tensor_res = self.pipeline.get_by_name("tensor_res")
        tensor_res.connect("draw", self.draw_overlay_cb)
        tensor_res.connect("caps-changed", self.prepare_overlay_cb)
        if self.callback is not None:
            GObject.timeout_add(500, self.callback, self)

        # start pipeline
        self.pipeline.set_state(Gst.State.PLAYING)
        self.running = True

        self.set_window_title("img_tensor", "NNStreamer Object Detection Example")

        # run main loop
        self.loop.run()

        # quit when received eos or error message
        self.running = False
        self.pipeline.set_state(Gst.State.NULL)

        bus.remove_signal_watch()

    def tflite_init(self):
        """
        :return: True if successfully initialized
        """

        if not os.path.exists(self.tflite_model):
            logging.error("cannot find tflite model [%s]", self.tflite_model)
            return False

        label_path = self.label_path
        try:
            with open(label_path, "r") as label_file:
                for line in label_file.readlines():
                    if line[0].isdigit():
                        while str(len(self.tflite_labels)) not in line:
                            self.tflite_labels.append("Invalid")
                        self.tflite_labels.append(line[line.find(" ") + 1 :])
                    else:
                        self.tflite_labels.append(line)
        except FileNotFoundError:
            logging.error("cannot find tflite label [%s]", label_path)
            return False

        logging.info("finished to load labels, total [%d]", len(self.tflite_labels))
        return True

    # @brief Callback for tensor sink signal.
    def new_data_cb(self, sink, buffer):
        """Callback for tensor sink signal.

        :param sink: tensor sink element
        :param buffer: buffer from element
        :return: None
        """
        if self.running:
            new_time = GLib.get_monotonic_time()
            self.interval_time = new_time - self.old_time
            self.old_time = new_time
            if buffer.n_memory() != 4:
                return False

            #  tensor type is float32.
            # LOCATIONS_IDX:CLASSES_IDX:SCORES_IDX:NUM_DETECTION_IDX
            # 4:20:1:1\,20:1:1:1\,20:1:1:1\,1:1:1:1
            # [0] detection_boxes (default 4th tensor). BOX_SIZE :
            # #MaxDetection, ANY-TYPE
            # [1] detection_classes (default 2nd tensor).
            # #MaxDetection, ANY-TYPE
            # [2] detection_scores (default 3rd tensor)
            # #MaxDetection, ANY-TYPE
            # [3] num_detection (default 1st tensor). 1, ANY-TYPE

            # bytestrings that are based on float32 must be
            # decoded into float list.

            # boxes
            mem_boxes = buffer.peek_memory(0)
            ret, info_boxes = mem_boxes.map(Gst.MapFlags.READ)
            if ret:
                assert info_boxes.size == (
                    self.BOX_SIZE * self.DETECTION_MAX * 4
                ), "Invalid info_box size"
                decoded_boxes = list(
                    np.frombuffer(info_boxes.data, dtype=np.float32)
                )  # decode bytestrings to float list

            # detections
            mem_detections = buffer.peek_memory(1)
            ret, info_detections = mem_detections.map(Gst.MapFlags.READ)
            if ret:
                assert info_detections.size == (
                    self.DETECTION_MAX * 4
                ), "Invalid info_detection size"
                decoded_detections = list(
                    np.frombuffer(info_detections.data, dtype=np.float32)
                )  # decode bytestrings to float list

            # scores
            mem_scores = buffer.peek_memory(2)
            ret, info_scores = mem_scores.map(Gst.MapFlags.READ)
            if ret:
                assert info_scores.size == (
                    self.DETECTION_MAX * 4
                ), "Invalid info_score size"
                decoded_scores = list(
                    np.frombuffer(info_scores.data, dtype=np.float32)
                )  # decode bytestrings to float list

            # num detection
            mem_num = buffer.peek_memory(3)
            ret, info_num = mem_num.map(Gst.MapFlags.READ)
            if ret:
                assert info_num.size == 4, "Invalid info_num size"
                decoded_num = list(
                    np.frombuffer(info_num.data, dtype=np.float32)
                )  # decode bytestrings to float list

            self.get_detected_objects(
                decoded_boxes, decoded_detections, decoded_scores, int(decoded_num[0])
            )

            mem_boxes.unmap(info_boxes)
            mem_detections.unmap(info_detections)
            mem_scores.unmap(info_scores)
            mem_num.unmap(info_num)

            if self.display == "None":
                if (GLib.get_monotonic_time() - self.print_time) > 1000000:
                    inference = self.tensor_filter.get_property("latency")
                    print(
                        "Inference time: "
                        + str(inference / 1000)
                        + " ms ("
                        + "{:5.2f}".format(1 / (inference / 1000000))
                        + " IPS)"
                    )
                    self.print_time = GLib.get_monotonic_time()

    def get_detected_objects(self, boxes, detections, scores, num):
        """Pairs boxes with dectected objects"""
        threshold_score = 0.5
        detected = list()

        for i in range(num):
            score = scores[i]
            if score < threshold_score:
                continue

            c = detections[i]

            box_offset = self.BOX_SIZE * i
            ymin = boxes[box_offset + 0]
            xmin = boxes[box_offset + 1]
            ymax = boxes[box_offset + 2]
            xmax = boxes[box_offset + 3]

            x = xmin * self.MODEL_WIDTH
            y = ymin * self.MODEL_HEIGHT
            width = (xmax - xmin) * self.MODEL_WIDTH
            height = (ymax - ymin) * self.MODEL_HEIGHT

            obj = {
                "class_id": int(c),
                "x": x,
                "y": y,
                "width": width,
                "height": height,
                "prob": score,
            }

            detected.append(obj)

        # update result
        self.detected_objects.clear()

        for d in detected:
            self.detected_objects.append(d)
            if DEBUG:
                print("==============================")
                print("LABEL           : {}".format(self.tflite_labels[d["class_id"]]))
                print("x               : {}".format(d["x"]))
                print("y               : {}".format(d["y"]))
                print("width           : {}".format(d["width"]))
                print("height          : {}".format(d["height"]))
                print("Confidence Score: {}".format(d["prob"]))

    def prepare_overlay_cb(self, overlay, caps):
        """Store the information from the caps that we are interested in."""
        self.video_caps = caps

    def draw_overlay_cb(self, overlay, context, timestamp, duration):
        """Callback to draw the overlay."""
        if self.video_caps is None or not self.running:
            return
        scale_height = self.VIDEO_HEIGHT / 1080
        scale_width = self.VIDEO_WIDTH / 1920
        scale_text = max(scale_height, scale_width)

        # mutex_lock alternative required
        detected = self.detected_objects
        # mutex_unlock alternative needed

        drawed = 0
        context.select_font_face(
            "Sans", cairo.FONT_SLANT_NORMAL, cairo.FONT_WEIGHT_BOLD
        )
        context.set_font_size(int(50.0 * scale_text))
        context.set_source_rgb(self.r, self.g, self.b)

        for obj in detected:
            label = self.tflite_labels[obj["class_id"]][:-1]
            x = obj["x"] * self.VIDEO_WIDTH // self.MODEL_WIDTH
            y = obj["y"] * self.VIDEO_HEIGHT // self.MODEL_HEIGHT
            width = obj["width"] * self.VIDEO_WIDTH // self.MODEL_WIDTH
            height = obj["height"] * self.VIDEO_HEIGHT // self.MODEL_HEIGHT

            # draw rectangle
            context.rectangle(x, y, width, height)
            context.set_line_width(3)
            context.stroke()

            # draw title
            context.move_to(x + 5, y + int(50.0 * scale_text))
            context.show_text(label)

            drawed += 1
            if drawed >= self.MAX_OBJECT_DETECTION:
                break

        inference = self.tensor_filter.get_property("latency")
        # Get current framerate and avg. framerate
        output_wayland = self.wayland_sink.get_property("last-message")
        if output_wayland:
            current_text = re.findall(r"current:\s[\d]+[.\d]*", output_wayland)[0]
            self.current_framerate = float(re.findall(r"[\d]+[.\d]*", current_text)[0])

        context.set_font_size(int(25.0 * scale_text))
        context.move_to(
            int(50 * scale_width), int(self.VIDEO_HEIGHT - (100 * scale_height))
        )
        context.show_text("i.MX NNStreamer Detection Demo")
        if inference == 0:
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (75 * scale_height))
            )
            context.show_text("FPS: ")
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (50 * scale_height))
            )
            context.show_text("IPS: ")
        elif (
            GLib.get_monotonic_time() - self.reload_time
        ) < 100000 and self.refresh_time != -1:
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (75 * scale_height))
            )
            context.show_text(
                "FPS: {:6.2f} ({:6.2f} ms)".format(
                    self.current_framerate, 1.0 / self.current_framerate * 1000
                )
            )
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (50 * scale_height))
            )
            context.show_text(
                "IPS: {:6.2f} ({:6.2f} ms)".format(
                    1 / (inference / 1000000), inference / 1000
                )
            )
        else:
            self.reload_time = GLib.get_monotonic_time()
            self.refresh_time = self.interval_time
            self.inference = self.tensor_filter.get_property("latency")
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (75 * scale_height))
            )
            context.show_text(
                "FPS: {:6.2f} ({:6.2f} ms)".format(
                    self.current_framerate, 1.0 / self.current_framerate * 1000
                )
            )
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (50 * scale_height))
            )
            context.show_text(
                "IPS: {:6.2f} ({:6.2f} ms)".format(
                    1 / (inference / 1000000), inference / 1000
                )
            )
        if self.first_frame:
            context.move_to(int(400 * scale_width), int(600 * scale_height))
            context.set_font_size(int(200.0 * min(scale_width, scale_height)))
            context.show_text("Loading...")
            self.first_frame = False
        context.fill()

    def on_bus_message(self, bus, message):
        """Callback for message.

        :param bus: pipeline bus
        :param message: message from pipeline
        :return: None
        """
        if message.type == Gst.MessageType.EOS:
            logging.info("received eos message")
            self.loop.quit()
        elif message.type == Gst.MessageType.ERROR:
            error, debug = message.parse_error()
            logging.warning("[error] %s : %s", error.message, debug)
            self.loop.quit()
        elif message.type == Gst.MessageType.WARNING:
            error, debug = message.parse_warning()
            logging.warning("[warning] %s : %s", error.message, debug)
        elif message.type == Gst.MessageType.STREAM_START:
            logging.info("received start message")
        elif message.type == Gst.MessageType.QOS:
            data_format, processed, dropped = message.parse_qos_stats()
            format_str = Gst.Format.get_name(data_format)
            logging.debug(
                "[qos] format[%s] processed[%d] dropped[%d]",
                format_str,
                processed,
                dropped,
            )

    def set_window_title(self, name, title):
        """Set window title for X11.

        :param name: GstXImageasink element name
        :param title: window title
        :return: None
        """
        element = self.pipeline.get_by_name(name)
        if element is not None:
            pad = element.get_static_pad("sink")
            if pad is not None:
                tags = Gst.TagList.new_empty()
                tags.add_value(Gst.TagMergeMode.APPEND, "title", title)
                pad.send_event(Gst.Event.new_tag(tags))


if __name__ == "__main__":
    if (
        len(sys.argv) != 7
        and len(sys.argv) != 5
        and len(sys.argv) != 9
        and len(sys.argv) != 12
        and len(sys.argv) != 6
    ):
        print(
            "Usage: python3 nndetection.py <dev/video*/video file>"
            + " <NPU/CPU> <model file> <label file>"
        )
        exit()
    # Get platform
    platform = os.uname().nodename
    if len(sys.argv) == 7:
        example = ObjectDetection(
            platform,
            sys.argv[1],
            sys.argv[2],
            sys.argv[3],
            sys.argv[4],
            sys.argv[5],
            sys.argv[6],
        )
    if len(sys.argv) == 5:
        example = ObjectDetection(
            platform, sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]
        )
    if len(sys.argv) == 6:
        example = ObjectDetection(
            platform, sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5]
        )
    if len(sys.argv) == 9:
        example = ObjectDetection(
            platform,
            sys.argv[1],
            sys.argv[2],
            sys.argv[3],
            sys.argv[4],
            sys.argv[5],
            sys.argv[6],
            int(sys.argv[7]),
            int(sys.argv[8]),
        )
    if len(sys.argv) == 12:
        example = ObjectDetection(
            platform,
            sys.argv[1],
            sys.argv[2],
            sys.argv[3],
            sys.argv[4],
            sys.argv[5],
            sys.argv[6],
            int(sys.argv[7]),
            int(sys.argv[8]),
            int(sys.argv[9]),
            int(sys.argv[10]),
            int(sys.argv[11]),
        )
    example.run()

 

        self.pipeline = Gst.parse_launch(
            'v4l2src name=cam_src ! videoconvert ! videoscale ! '
            'video/x-raw,width=640,height=480,format=RGB ! tee name=t_raw '
            't_raw. ! queue leaky=2 max-size-buffers=2 ! videoscale ! video/x-raw,width=300,height=300 ! tensor_converter ! '
            'tensor_transform mode=arithmetic option=typecast:float32,add:-127.5,div:127.5 ! '
            'tensor_filter framework=tensorflow-lite model=' + self.tflite_model + ' ! '
            'tensor_decoder mode=bounding_boxes option1=mobilenet-ssd option2='
            + self.tflite_label + ' option3=' + self.tflite_box_prior + ' option4=640:480 option5=300:300 !'
            'compositor name=mix sink_0::zorder=2 sink_1::zorder=1 ! videoconvert ! ximagesink '
            't_raw. ! queue leaky=2 max-size-buffers=10 ! mix. '
        )

[링크 : https://github.com/nnstreamer/nnstreamer-example/blob/main/native/example_object_detection_tensorflow_lite/nnstreamer_example_object_detection_tflite.py]

 

gst_launch_cmdline 를 출력해보니 아래와 같이 gstreamer 파이프라인이 나온다.

v4l2src name=cam_src device=/dev/video3 ! imxvideoconvert_g2d ! video/x-raw,width=1920,height=1080,framerate=30/1,format=BGRx ! tee name=t t. ! imxvideoconvert_g2d !  video/x-raw,width=300,height=300, ! queue max-size-buffers=2 leaky=2 ! videoconvert ! video/x-raw,format=RGB ! tensor_converter ! tensor_filter framework=tensorflow-lite model=/home/root/.cache/gopoint/mobilenet_ssd_v2_coco_quant_postprocess.tflite accelerator=true:npu custom=Delegate:External,ExtDelegateLib:libvx_delegate.so silent=FALSE name=tensor_filter latency=1 ! tensor_sink name=tensor_sink t. ! imxvideoconvert_g2d ! cairooverlay name=tensor_res ! queue max-size-buffers=2 leaky=2 ! fpsdisplaysink name=img_tensor text-overlay=false video-sink=waylandsink sync=false

 

보기어려우니 엔터로 구분

v4l2src name=cam_src device=/dev/video3 !
imxvideoconvert_g2d !
video/x-raw,width=1920,height=1080,framerate=30/1,format=BGRx !
tee name=t t. !
imxvideoconvert_g2d !
video/x-raw,width=300,height=300, !
queue max-size-buffers=2 leaky=2 !
videoconvert !
video/x-raw,format=RGB !
tensor_converter !
tensor_filter framework=tensorflow-lite model=/home/root/.cache/gopoint/mobilenet_ssd_v2_coco_quant_postprocess.tflite accelerator=true:npu custom=Delegate:External,ExtDelegateLib:libvx_delegate.so silent=FALSE name=tensor_filter latency=1 !
tensor_sink name=tensor_sink t. !
imxvideoconvert_g2d !
cairooverlay name=tensor_res !
queue max-size-buffers=2 leaky=2 !
fpsdisplaysink name=img_tensor text-overlay=false video-sink=waylandsink sync=false

 

+

2024.01.03

# cd /home/root/.nxp-demo-experience/scripts/machine_learning
# python3 nndetection.py /dev/video3 NPU /home/root/.cache/gopoint/mobilenet_ssd_v2_coco_quant_postprocess.tflite /home/root/.cache/gopoint/coco_labels.txt

 

gst-launch 로도 실행은 되는데 callback 처리가 안되서 overlay가 출력이 안되어 동일한 화면을 보여주진 않는다.

gst-launch-1.0 v4l2src name=cam_src device=/dev/video3 ! imxvideoconvert_g2d ! video/x-raw,width=1920,height=1080,framerate=30/1,format=BGRx ! tee name=t t. ! imxvideoconvert_g2d ! video/x-raw,width=300,height=300, ! queue max-size-buffers=2 leaky=2 ! videoconvert ! video/x-raw,format=RGB ! tensor_converter ! tensor_filter framework=tensorflow-lite model=/home/root/.cache/gopoint/mobilenet_ssd_v2_coco_quant_postprocess.tflite accelerator=true:npu custom=Delegate:External,ExtDelegateLib:libvx_delegate.so silent=FALSE name=tensor_filter latency=1 !  tensor_sink name=tensor_sink t. ! imxvideoconvert_g2d ! cairooverlay name=tensor_res ! queue max-size-buffers=2 leaky=2 ! fpsdisplaysink name=img_tensor text-overlay=false video-sink=waylandsink sync=false
Posted by 구차니

자동완성으로 해보니 몇가지 나오는데 mnist 말고는 몰라서 찾아보는 중

>>> tf.keras.datasets.
tf.keras.datasets.boston_housing  tf.keras.datasets.cifar100        tf.keras.datasets.imdb            tf.keras.datasets.reuters         
tf.keras.datasets.cifar10         tf.keras.datasets.fashion_mnist   tf.keras.datasets.mnist

 

imdb는 영화 db

boston_housing은 statlib 사이트에서 정의된 보스톤 주택가격

reuter는 46 주제에 따른 11228 뉴스(로이터 뉴스) 인 듯.

This is a dataset of 11,228 newswires from Reuters, labeled over 46 topics.

[링크 : https://www.tensorflow.org/api_docs/python/tf/keras/datasets/boston_housing/load_data]

[링크 : https://www.tensorflow.org/api_docs/python/tf/keras/datasets]

 

cifar10은 10개 클래스니까.. 결과도 MNIST 처럼 10개로 나올 것 같고..

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

[링크 : https://www.tensorflow.org/datasets/catalog/cifar10?hl=en]

 

This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).

[링크 : https://www.tensorflow.org/datasets/catalog/cifar100?hl=en]

[링크 : https://www.tensorflow.org/datasets/catalog/fashion_mnist?hl=en]

[링크 : https://www.tensorflow.org/datasets/catalog/emnist?hl=en]

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

주피터 노트북 프로젝트(?) 실행하기  (0) 2024.01.02
i.mx8mp gopoint 실행 경로  (0) 2024.01.02
tensorflow lite / mnist 학습  (0) 2024.01.02
yolo-label  (0) 2022.03.22
tflite bazel rpi3b+  (0) 2022.01.27
Posted by 구차니

신기하네.. 그냥 알아서 받네?

>>> mnist = tf.keras.datasets.mnist
>>> (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 [==============================] - 1s 0us/step

 

학습하고 tflite 파일로 저장하기

import tensorflow as tf
import numpy as np

mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images = train_images.astype(np.float32) / 255.0
test_images = test_images.astype(np.float32) / 255.0

model = tf.keras.Sequential([
  tf.keras.layers.InputLayer(input_shape=(28, 28)),
  tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
  tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),
  tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(
                  from_logits=True),
              metrics=['accuracy'])
model.fit(
  train_images,
  train_labels,
  epochs=5,
  validation_data=(test_images, test_labels)
)

# 일반 모델로 변환
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

# 요건 차이 없음
# converter = tf.lite.TFLiteConverter.from_keras_model(model)
# converter.optimizations = [tf.lite.Optimize.DEFAULT]
# tflite_model_quant = converter.convert()

# quant 를 하려면 아래 코드 실행해야 함
def representative_data_gen():
  for input_value in tf.data.Dataset.from_tensor_slices(train_images).batch(1).take(100):
    yield [input_value]

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_model_quant = converter.convert()

# 파일로 저장하기
import pathlib

tflite_models_dir = pathlib.Path("/tmp/mnist_tflite_models/")
tflite_models_dir.mkdir(exist_ok=True, parents=True)

# Save the unquantized/float model:
tflite_model_file = tflite_models_dir/"mnist_model.tflite"
tflite_model_file.write_bytes(tflite_model)

# Save the quantized model:
tflite_model_quant_file = tflite_models_dir/"mnist_model_quant.tflite"
tflite_model_quant_file.write_bytes(tflite_model_quant)

[링크 : https://www.tensorflow.org/lite/performance/post_training_integer_quant?hl=ko]

 

netron을 통해 생성한걸 보는데 quant나 그냥이나 어째 차이가 없냐?

 

 

+

quantization 하면 uint8로 변경된다.

 

그나저나, MNIST에 대해서 오해가 있었다.

출력이 [1,10] 인데 0~9 까지의 숫자에 대한 필기 데이터베이스지 알파벳이 아니란 것 -_-!

그래서 출력이 딱 10개인 건 당연하다는 것..

[링크 : https://en.wikipedia.org/wiki/MNIST_database]

 

+

EMNIST 라고 알파벳 손글씨가 따로 있다.

[링크 : https://www.nist.gov/itl/products-and-services/emnist-dataset]

[링크 : https://www.tensorflow.org/datasets/catalog/emnist?hl=ko]

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

i.mx8mp gopoint 실행 경로  (0) 2024.01.02
tensorflow keras dataset  (0) 2024.01.02
yolo-label  (0) 2022.03.22
tflite bazel rpi3b+  (0) 2022.01.27
bazel cross compile  (0) 2022.01.27
Posted by 구차니

제 시간에 잘 도착하길 ㅠㅠ

항권권이 2터미널이니까 그쪽으로 신청하고 받으면 되는데 현장 구매는 안되는건가... 

비나폰이 그래도 규모가 더 큰데라고 해서 일단 신청했는데

 

모비폰에 비하면은 순수 데이터용이라, 전화 문자 수신도 안되니 조금 아쉽긴 하다.

그래도 다행히(!) 둘 다 핫스팟은 된다고 하니 멀 하던 상관은 없을 듯.

 

배송비가 붙으니 먼가 배아프다 ㅠㅠ

'개소리 왈왈 > 육아관련 주저리' 카테고리의 다른 글

다낭 도착  (0) 2024.01.05
물건너 가자~  (0) 2024.01.04
폭설  (0) 2023.12.30
  (0) 2023.12.28
개들은 무산 꿈을 꿀까  (0) 2023.12.24
Posted by 구차니
Apple2024. 1. 1. 21:20

차장보니 2세대 2007년 apple wireless keyboard로 블루투스 모델

아이맥, 맥프로 사면 번들로 주던 녀석이라고 한다.

 

AA 건전지 2개 쓰는 녀석인데 특이하게도 키보드 이름이 바뀌어 있었다.

블루투스 키보드는 이름을 바꿀수 있는건가..

 

아무튼 대충 써보려는데

한대는 아래 방향 방향키가 고장나고, 좌우 방향이 잘 안눌려서 쓰는거 포기

다른 한대는 엔터가 안되서 포기 (아래방향 방향키가 아예 도망감)

 

마음을 비우자 ㅠㅠ

[링크 : https://support.apple.com/ko-kr/guide/mac-help/mh29203/mac]

'Apple' 카테고리의 다른 글

맥북 초기화 하기  (0) 2022.12.27
맥북에어 외부 모니터 사용하기 + 화면끄기  (2) 2022.08.27
macos catalina iso 파일  (0) 2022.06.23
macos iso  (0) 2022.06.20
맥 화면 90도 돌리기  (0) 2022.06.18
Posted by 구차니
게임/daemon X machina2023. 12. 31. 22:08

오랫만에 마음에 맞는 멤버가 모여서 무한 뺑뺑이 ㅋㅋ

'게임 > daemon X machina' 카테고리의 다른 글

게임삭제  (0) 2024.05.15
daemon x machina head 장비 교체  (0) 2023.11.19
daemon x machina - dainsleif 획득  (0) 2023.11.04
daemon x machina - free order : aviator  (0) 2023.11.04
daemon x machina 끝!  (0) 2023.11.03
Posted by 구차니

이렇게 까지 온게 얼마만인가 싶을 정도로 많이 왔다.

나가보니 곳곳에 왕 큰 눈사람들이 가득가득

'개소리 왈왈 > 육아관련 주저리' 카테고리의 다른 글

물건너 가자~  (0) 2024.01.04
베트남 유심 결제  (0) 2024.01.02
  (0) 2023.12.28
개들은 무산 꿈을 꿀까  (0) 2023.12.24
약빨인가..  (0) 2023.12.23
Posted by 구차니
개소리 왈왈/블로그2023. 12. 29. 16:14

일단 1000도 안오는걸 보면

임베디드로 검색될게 없다는거고.. 반대로 생각하면 이쪽 분야가 죽었다고 봐야하려나..

쩝.. 그래도 나름 20년 가까이 먹고 있는데 분야를 바꾸어야 하나.. 씁쓸하다.

'개소리 왈왈 > 블로그' 카테고리의 다른 글

망할 티스토리  (0) 2024.04.28
나만 블로그 이상한가?  (0) 2024.04.26
블로그 미쳤다?  (4) 2023.11.30
갑자기 먼일이래 -ㅁ-?  (0) 2023.10.25
블로그 방문자 하락.. -_ㅠ  (0) 2023.10.03
Posted by 구차니

쿨쿨쿨

먼넘의 약을 얼마나 세개 지어났는지

먹고 자고 먹고 자고 -_-

낮잠을 6시간 자도 또 잡이 오게 하다니 대단하다 -_-

'개소리 왈왈 > 육아관련 주저리' 카테고리의 다른 글

베트남 유심 결제  (0) 2024.01.02
폭설  (0) 2023.12.30
개들은 무산 꿈을 꿀까  (0) 2023.12.24
약빨인가..  (0) 2023.12.23
피곤하다!!!  (0) 2023.12.17
Posted by 구차니