Install Screen
$ sudo apt install screen
Enter a new Screen Session
$ screen
Detach from current screen session
Install Screen
$ sudo apt install screen
Enter a new Screen Session
$ screen
Detach from current screen session
import os | |
import sys | |
import random | |
import math | |
import numpy as np | |
import skimage.io | |
import matplotlib | |
import matplotlib.pyplot as plt | |
# Root directory of the project |
# register this function, so JS code could call this | |
output.register_callback('notebook.run_algo', run_algo) | |
# put the JS code in cell and run it | |
take_photo() |
import IPython | |
import time | |
import sys | |
import numpy as np | |
import cv2 | |
import base64 | |
import logging | |
from google.colab import output | |
from PIL import Image |
from IPython.display import display, Javascript | |
from google.colab.output import eval_js | |
from base64 import b64decode | |
def take_photo(filename='photo.jpg', quality=0.8): | |
js = Javascript(''' | |
// ... | |
// JavaScript code here <<== | |
// ... | |
''') |
async function takePhoto(quality) { | |
// create html elements | |
const div = document.createElement('div'); | |
const video = document.createElement('video'); | |
video.style.display = 'block'; | |
// request the stream. This will ask for Permission to access | |
// a connected Camera/Webcam | |
const stream = await navigator.mediaDevices.getUserMedia({video: true}); |
Processing 1 images | |
image shape: (425, 640, 3) min: 0.00000 max: 255.00000 uint8 | |
molded_images shape: (1, 1024, 1024, 3) min: -123.70000 max: 151.10000 float64 | |
image_metas shape: (1, 93) min: 0.00000 max: 1024.00000 float64 | |
anchors shape: (1, 261888, 4) min: -0.35390 max: 1.29134 float32 |
# Load a random image from the images folder | |
file_names = next(os.walk(IMAGE_DIR))[2] | |
image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names))) | |
# Run detection | |
results = model.detect([image], verbose=1) | |
# Visualize results | |
r = results[0] | |
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], |
# Create model object in inference mode. | |
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config) | |
# Load weights trained on MS-COCO | |
model.load_weights(COCO_MODEL_PATH, by_name=True) |
async function takePhoto(quality) { | |
// create html elements | |
const div = document.createElement('div'); | |
const video = document.createElement('video'); | |
video.style.display = 'block'; | |
// request the stream. This will ask for Permission to access | |
// a connected Camera/Webcam | |
const stream = await navigator.mediaDevices.getUserMedia({video: true}); |