Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
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def main():
"""
The main entry point for the Xeno project.
Initializes the following components:
- Logger: Handles logging for the application.
- WiFiManager: Manages Wi-Fi connections.
- HTMLLogger: Logs scan results in HTML format.
- EPaperDisplay: Manages the e-paper display updates.
- ImageStateManager: Tracks and manages workflow states and images.
Workflow:
1. Initializes display and loads required credentials.
2. Continuously performs scans and updates results.
3. Logs workflow progress and handles any exceptions.
Raises:
Exception: If a critical error occurs during initialization or execution.
"""
os.makedirs("logs", exist_ok=True)
logger = Logger(log_file="logs/scan.log")
wifi_manager = WiFiManager(logger=logger)
html_logger = HTMLLogger(output_dir="utils/html_logs", json_dir="utils/json_logs")
display = EPaperDisplay()
state_manager = ImageStateManager()
try:
display.initialize()
while True:
logger.log("[INFO] Starting new scanning cycle.")
run_scans(logger, wifi_manager, html_logger, display, state_manager)
logger.log("[INFO] Scanning cycle completed. Sleeping for 10 minutes.")
time.sleep(600) # Sleep for 10 minutes
except Exception as e:
logger.log(f"[ERROR] Fatal error occurred: {e}")
finally:
display.clear() # Display is cleared
print("[INFO] E-paper display cleared.")
if __name__ == "__main__":
main()
# <FILESEP>
# Copyright Niantic 2020. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the DepthHints licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
import json
import os
import time
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
import datasets
import networks
from layers import (SSIM, BackprojectDepth, Project3D, compute_depth_errors,
disp_to_depth, get_smooth_loss,
transformation_from_parameters)
from networks.RTMonoDepth.RTMonoDepth import DepthDecoder, DepthEncoder
from utils import normalize_image, readlines, sec_to_hm_str
torch.backends.cudnn.enabled = True
class Trainer:
def __init__(self, options):
self.opt = options
self.log_path = os.path.join(self.opt.log_dir, self.opt.model_name)
# checking height and width are multiples of 32
assert self.opt.height % 32 == 0, "'height' must be a multiple of 32"
assert self.opt.width % 32 == 0, "'width' must be a multiple of 32"
self.models = {}
self.parameters_to_train = []
self.device = torch.device("cpu" if self.opt.no_cuda else "cuda")
self.num_scales = len(self.opt.scales)
self.num_input_frames = len(self.opt.frame_ids)
self.num_pose_frames = 2 if self.opt.pose_model_input == "pairs" else self.num_input_frames
assert self.opt.frame_ids[0] == 0, "frame_ids must start with 0"
self.use_pose_net = not (self.opt.use_stereo and self.opt.frame_ids == [0])
if self.opt.use_stereo:
self.opt.frame_ids.append("s")
if self.opt.use_learnable_K:
focal_len = 807.1375