monodepth_gst_vision_app.py

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import os, time
from pprint import pprint
import numpy as np
import yaml
import threading
import argparse
import math

import gi
gi.require_version('Gst', '1.0')
gi.require_version('GstApp', '1.0')
from gi.repository import Gst, GstApp, GLib, GObject
Gst.init(None)

import gst_configs, model_runner, display, utils

# global variables to help control the GST thread
stop_threads = False
infer_thread = None


def parse_args():
    parser = argparse.ArgumentParser()

    parser.add_argument('-c', '--camera', default='usb-1080p', help='name of camera type to use. options are usb-720p from logitech, usb-1080p (c920 or c922 from logitech), and IMX219 (RPi cam v2)')
    parser.add_argument('-m', '--modeldir', default='./model/', help='location of the model directory. Assumed to have dataset.yaml, param.yaml, model as model.onnx, and subdir for artifacts. See typical format of directories from /opt/model_zoo for example')
    parser.add_argument('-d', '--device', default='/dev/video2', help="location of the camera device under /dev")
    parser.add_argument('-o', '--output-dimensions', default='1920x1080', help="Resolution of the output display in WxH format, e.g. 1920x1080")

    args = parser.parse_args()
    
    return args

def print_stats(stats):
    print('\nRan %i frames' % stats['count'])
    mean_inf = stats['total_pre_stage_s'] / stats['count']
    mean_out = stats['total_output_stage_s'] / stats['count']
    mean_infer = stats['total_inference_s'] / stats['count']
    mean_overall = stats['total_infer_frame'] / stats['count']
    fps = 1/mean_overall
    std_inf = math.sqrt(stats['total_pre_stage_sq'] / stats['count'] - mean_inf**2)
    std_out = math.sqrt(stats['total_output_stage_sq'] / stats['count'] - mean_out**2)
    print('**** Runtime Stats ****')
    print('---- Pull input time (ms): avg %d +- %d (min to max: %d to %d)' % (mean_inf*1000, std_inf*1000, stats['total_pre_stage_min']*1000, stats['total_pre_stage_max']*1000))
    print('---- infer time (ms): avg %d' % (mean_infer*1000))
    print('---- Output (draw, post-proc) time (ms): avg %d +- %d' % (mean_out*1000, std_out*1000))
    print('---- FPS: %.02f' % fps)
    print("-----------------------\n")


def application_thread(gst_conf:gst_configs.GstBuilder, model_obj:model_runner.ModelRunner, display_obj:display.DisplayDrawer, args):
    '''
    This is where all the appsrc/appsink code lies
    '''

    if not hasattr(gst_conf, 'gst_str'): gst_conf.build_gst_strings(model_obj)

    print('Starting with in_gst: \n%s\n' % gst_conf.gst_str)

    print('\nout gst: ' + gst_conf.out_gst_str)
    gst_conf.start_gst()
    
    #we'll collect some statistics on where time is spent in the application
    stats = {'count':0, 'total_pre_stage_s':0, 'total_output_stage_s':0, 'total_pre_stage_sq':0, 'total_output_stage_sq':0, 'total_pre_stage_min':100000, 'total_pre_stage_max':-1, 'total_infer_frame':0, 'total_inference_s': 0}

    #run to init
    output_frame = display_obj.make_frame_init()
    t_loop = time.time()

    global stop_threads 
    while not stop_threads:
        #push an image from the last iteration first so we're able to create the display output immediately
        display_obj.push_to_display(output_frame)
        print('pull buffers')
        t_start_loop = time.time()
        sample_tensor, _ = gst_conf.pull_sample(gst_conf.app_in_tensor, loop=False)
        if not sample_tensor: continue
        sample_image, struct_image = gst_conf.pull_sample(gst_conf.app_in_image, loop=False)
        if not sample_image: continue

        print('got GST buffers in app code')
        t_pre_draw = time.time()

        #decode the tensor. Model dependent
        infer_input = model_obj.decode_input_tensor(sample_tensor)
        t_pre_infer = time.time()
        infer_output = model_obj.run_onnx(infer_input)
        t_post_infer = time.time()
        input_image = gst_conf.format_image_from_sample(sample_image, struct_image)

        t_post_proc = time.time()

        # create the output frame; gets pushed at top of loop. copy() inference result since it is read-only from GST
        output_frame = display_obj.make_frame(input_image, infer_output[0].copy(), model_obj)
        t_final = time.time()
        
        #collect some stats    
        stats['count'] += 1
        stats['total_pre_stage_s'] += t_pre_draw - t_start_loop
        stats['total_pre_stage_min'] = min(t_pre_draw - t_start_loop, stats['total_pre_stage_min'])
        stats['total_pre_stage_max'] = max(t_pre_draw - t_start_loop, stats['total_pre_stage_max'])

        stats['total_pre_stage_sq'] += (t_pre_draw - t_start_loop)**2
        stats['total_output_stage_s'] += t_final - t_pre_draw
        stats['total_output_stage_sq'] += (t_final - t_pre_draw)**2

        stats['total_inference_s'] += (t_post_infer - t_pre_infer)
        stats['total_infer_frame'] += (time.time() - t_loop)
        print((time.time() - t_loop))
        t_loop = time.time()
        # print_stats(stats)

    # raises an error if stopped before an iteration of inference completed
    print_stats(stats)

def main():
    args = parse_args()
    
    # camera parameters and information assumed based on device in CLI args
    cam_params = gst_configs.CamParams(args.camera, device=args.device)
    # configure display output information
    display_dimensions = args.output_dimensions.split('x')
    display_width = int(display_dimensions[0])
    display_height = int(display_dimensions[1])
    display_obj = display.DisplayDrawer(display_width, display_height)

    #load model's params'yaml file
    modeldir = args.modeldir
    paramsfile = os.path.join(modeldir, 'param.yaml')
    model_params = yaml.safe_load(open(paramsfile, 'r'))

    #the set of classes/categories recognized by the model

    # setup the model for inference. Parameters used by gst_config
    model_obj = model_runner.ModelRunner(modeldir, paramsfile=paramsfile)
    model_obj.load_model_tidl() #load model to get info about input data type
    
    #create the gstreamer pipeline based on model and camera parameters
    gst_conf = gst_configs.GstBuilder(model_params, cam_params, display_obj) 
    gst_conf.build_gst_strings(model_obj)
    # start the pipeline and saves references to appsrc/appsink
    gst_conf.setup_gst_appsrcsink()

    display_obj.set_gst_info(gst_conf.app_out, gst_conf.gst_caps)
    
    # Use for pushing information to the display. Assuming 1920x1080p display

    # fork into an application thread to make KB interrupts easier to catch
    global stop_threads
    stop_threads = False
    app_thread = threading.Thread(target=application_thread, args=[gst_conf, model_obj, display_obj,  args])
    app_thread.start()

    try: 
        while not stop_threads:
            time.sleep(2)
    except KeyboardInterrupt:
        print('KB shortcut caught')
        stop_threads = True

    gst_conf.pipe.set_state(Gst.State.PAUSED)
    gst_conf.out_pipe.set_state(Gst.State.PAUSED)
    print(stop_threads)
    print('paused pipe; waiting gst thread to join')
    app_thread.join()
    print('exiting...')

if __name__ == '__main__':
    main()