def test_define_str(self):
Config.clear()
Config.define_str("test", 5, "A test for int")
Config.define_str("test_f", 5., "A test for int with a float var")
Config.define_str("test_f2", 5.6, "A test for int with a float var")
Config.define_str("test_str", "5.6", "A test for int with a float var")
self.assertDictEqual(Config.get_dict(), {
'test': "5",
'test_f': "5.0",
'test_f2': "5.6",
'test_str': "5.6"
})
import cv2
import logging
import os
import coloredlogs
from distribute_config import Config
coloredlogs.install(level="DEBUG")
Config.define_str("file", "", "input file: video to read and split")
Config.define_float("extract_every", 100,
"Time in ms between two extracted images")
Config.define_str("prefix", "", "Prefix to the name of the images")
Config.define_str("outputdir", ".", "Where to save the pictures")
def main():
Config.load_conf("config_video_burst.yml")
config = Config.get_dict()
# check if the script can run
assert os.path.isfile(config["file"]), f"Option 'file' need to be provided"
os.makedirs(config["outputdir"], exist_ok=True)
if (config["prefix"] is ""):
config["prefix"] = get_prefix(config["file"])
logging.info(f'prefix: {config["prefix"]}')
frame_id = 0
last_save = -10000
video = cv2.VideoCapture(config["file"])
if not video.isOpened():
import logging
import json
import coloredlogs
import cv2
import tensorflow as tf
import numpy as np
from tqdm import tqdm # progress bar
from distribute_config import Config
# Run a frozen model on a set of images and output the detections as .json files, one per image.
# For now it only keeps "detection_classes" == 1, i.e. "class"="person"
coloredlogs.install(level="DEBUG")
Config.define_str(
"model_path", "/opt/model/frozen_inference_graph.pb",
"Path of the model to load and execute, for instance"
"/opt/model/frozen_inference_graph.pb. If you're using docker-compose you shouldn't change this."
)
Config.define_str("input_dir", "",
"Path where the images to annotate are stored")
Config.define_str(
"output_dir", "",
"Path to store pre-annotations (model annotations to help human annotators)"
)
with Config.namespace("class"):
Config.define_str_list("names", [], "name of the classes to annotate")
with Config.namespace("object_detection"):
Config.define_float("threshold", 0.2,
"Discard boxes with score below this value")
Config.define_float(
"max_width", 1.0,
import threading
import json
import logging
import coloredlogs
from PIL import Image
from flask import Flask, url_for, redirect, Response, jsonify
from flask_cors import CORS
import flask
from distribute_config import Config
coloredlogs.install(level='DEBUG')
Config.define_str("images_path", "static/images",
"Path where are stored the images to annotate")
Config.define_str("human_annotations_path", "static/human_annotations",
"Path where are stored human annotation")
Config.define_str("model_annotations_path", "static/model_annotations",
"Path where are stored model annotation for helping human")
with Config.namespace("class"):
Config.define_str_list("names", [], "name of the classes to annotate")
Config.define_str_list("colors", [], "colors for each classes")
Config.define_int("min_height", 0,
"Rectangle with lower height will be displayed red")
Config.define_int("min_width", 0,
"Rectangle with lower width will be displayed red")
image_provider = None
import json
import time
from distribute_config import Config
from reachy import Reachy
Config.define_str("file_path", "",
"path of the file to write the different positions")
Config.define_bool("record", True,
"if true then record and run. Else only run ")
def main():
Config.load_conf()
config = Config.get_dict()
reachy = Reachy()
for motor in reachy.motors:
motor.compliant = True
if config["record"]:
# First part of the script : the user can move the arm and hit return to save the arm position
all_positions = []
action = input("> ")
while action != "save":
# Save the position of the motors
position = {}
for motor in reachy.motors:
print(
f"The motor \"{motor.name}\" is currently in position: {motor.present_position}"
)
position[motor.name] = motor.present_position
import os
import logging
import json
import coloredlogs
import cv2
import tensorflow as tf
import numpy as np
from tqdm import tqdm # progress bar
from distribute_config import Config
# Run a frozen model on a set of images and output the detections as .json files, one per image.
# For now it only keeps "detection_classes" == 1, i.e. "class"="person"
coloredlogs.install(level="DEBUG")
Config.define_str(
"model_path", "",
"Path of the model to load and execute, for instance models/frozen_inference_graph.pb"
)
Config.define_str("input_dir", "",
"Path where the images to annotate are stored")
Config.define_str(
"output_dir", "",
"Path to store pre-annotations (model annotations to help human annotators)"
)
with Config.namespace("class"):
Config.define_str_list("names", [], "name of the classes to annotate")
with Config.namespace("object_detection"):
Config.define_float("threshold", 0.2,
"Discard boxes with score below this value")
Config.define_float(
"max_width", 0.7,
"Discard boxes with width upper this value because in some cases, very large detections are mostly false positives"