Realsense

Stavo cercando un metodo per aveere un set di training per Monocular Depth Estimation ed ho ritirato fuori la RealSense  D415 per scoprire che ad Agosto 2021 la Intel ha dichiarato la dismissione di questi sensori robotici (ad esclusione di quelli stereoscopici).... mi ricorda tanto la storia di Intel Edison ed Intel Stick...progetti interessanti morti 

Questo piccolo programmino (che modifica uno degli esempi dell'SDK di Realsense) salva la mappa di profondita' in formato NumPy ed la corrispondente immagine JPG premendo il tasto S

Per compilare in binario l'eseguibile

pyinstaller main.py --onefile

requirements.txt

altgraph==0.17.2
importlib-metadata==4.8.3
numpy==1.19.5
opencv-python==4.1.2.30
Pillow==8.4.0
pyinstaller==4.7
pyinstaller-hooks-contrib==2021.4
pyrealsense2==2.50.0.3812
typing_extensions==4.0.1
zipp==3.6.0

main.py

import sys,getopt
import pyrealsense2 as rs
import numpy as np
import cv2
from PIL import Image
import os.path

def completo(argv):
    outputdir = ''
    if len(sys.argv) < 2:
        print('test.py -o <output_directory>')
        sys.exit()
    try:
        opts, args = getopt.getopt(argv, "hi:o:", ["odir="])
    except getopt.GetoptError:
        print('test.py -o <output_directory>')
        sys.exit(2)
    for opt, arg in opts:
        if opt == '-h':
            print('test.py -o <output_directory>')
            sys.exit()
        elif opt in ("-o", "--odir"):
            outputdir = arg
            if (os.path.isdir(outputdir)) == False:
                print('Errore nome directory output')
                sys.exit()
    print('Output file is ' + outputdir)
    ## License: Apache 2.0. See LICENSE file in root directory.
    ## Copyright(c) 2015-2017 Intel Corporation. All Rights Reserved.

    ###############################################
    ##      Open CV and Numpy integration        ##
    ###############################################



    # Configure depth and color streams
    pipeline = rs.pipeline()
    config = rs.config()

    # Get device product line for setting a supporting resolution
    pipeline_wrapper = rs.pipeline_wrapper(pipeline)
    pipeline_profile = config.resolve(pipeline_wrapper)
    device = pipeline_profile.get_device()
    device_product_line = str(device.get_info(rs.camera_info.product_line))

    found_rgb = False
    for s in device.sensors:
        if s.get_info(rs.camera_info.name) == 'RGB Camera':
            found_rgb = True
            break
    if not found_rgb:
        print("The demo requires Depth camera with Color sensor")
        exit(0)

    contatore = 0

    config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)

    if device_product_line == 'L500':
        config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
    else:
        config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)

    # Start streaming
    pipeline.start(config)

    try:
        while True:

            # Wait for a coherent pair of frames: depth and color
            frames = pipeline.wait_for_frames()
            depth_frame = frames.get_depth_frame()
            color_frame = frames.get_color_frame()
            if not depth_frame or not color_frame:
                continue

            # Convert images to numpy arrays
            depth_image = np.asanyarray(depth_frame.get_data())
            color_image = np.asanyarray(color_frame.get_data())

            # Apply colormap on depth image (image must be converted to 8-bit per pixel first)
            depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)

            depth_colormap_dim = depth_colormap.shape
            color_colormap_dim = color_image.shape


            # If depth and color resolutions are different, resize color image to match depth image for display
            if depth_colormap_dim != color_colormap_dim:
                resized_color_image = cv2.resize(color_image, dsize=(depth_colormap_dim[1], depth_colormap_dim[0]),
                                                 interpolation=cv2.INTER_AREA)
                images = np.hstack((resized_color_image, depth_colormap))
            else:
                images = np.hstack((color_image, depth_colormap))

            # Show images
            cv2.namedWindow('Immagine', cv2.WINDOW_AUTOSIZE)

            cv2.imshow('Immagine' , images)
            tasto = cv2.waitKey(1000)
            #print(str(tasto))
            if (tasto == 115): #S
                #salva su file
                contatore = contatore + 1
                str_conta = str(contatore)
                try:
                    np.save(str_conta.zfill(3)+'_depth.npy', depth_image)
                    im = Image.fromarray(color_image)
                    im.save(str_conta.zfill(3)+"_color.jpg")
                    print('Immagine ' + str(contatore) + 'acquisita')
                except:
                    print('Errore acquisizione')
                    exit()
            if (tasto == 113): #Q
                exit()

    finally:
        # Stop streaming
        pipeline.stop()

if __name__ == '__main__':
    completo(sys.argv[1:])