google-images-download | Python Script to download hundreds | Computer Vision library
kandi X-RAY | google-images-download Summary
kandi X-RAY | google-images-download Summary
Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!
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Top functions reviewed by kandi - BETA
- Download data files
- Get next tab from s
- Return the size of a file
- Format an image
- Return a dict of all tabs
- Return a list of keywords from a file
- Builds a Google Search URL
- Get next item from s
- Creates the necessary directories
- Download a page from a URL
- Build url parameters
- Search for similar images
- Download an extended page
- Get all the items from the page
- Download an image
- Execute the download executor
- Fix broken json
- Download a single image
- Arguments for user input
- Setup the extension
google-images-download Key Features
google-images-download Examples and Code Snippets
Lines of Code : 3License : Permissive (MIT)
Copypython3 shuffle_images.py --dataset ${PWD}/data/train
## Recommended labelme config, autosave & nodata
labelme --autosave --nodata
License : No LicenseCopygit clone https://github.com/ripZeide/Troll_42 && cd Troll_42 && sh Zob
googleimagesdownload.exe -k "coca cola" -sk advertisements -f png -o Pos -s medium
git clone https://github.com/Joeclinton1/google-images-download.git
cd google-images-download && sudo python setup.py install #no need for 'sudo' on windows Anaconda environment
pip install git+https://gith!apt install chromium-chromedriver
"chromedriver": "/usr/bin/chromedriver",
from google_images_download import google_images_download
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
response = google_images_download.googleimagesdownload()
query = "hello"
arguments = {"keywords": query,arguments = {"keywords":"yuzu figure skater","limit":10,"print_urls":True, "no_download": True}
parser.add_argument('-s', '--size', help='image size', type=str, required=False,
choices=['large','medium','icon','>400*300','>640*480','>800*600','>1024*768','>2MP','>4MP','>6MP','>8MP',arguments = {"keywords":"foxes, shiba inu outside",
"limit":2000,
"print_urls":True,
"chromedriver":"/Users/jerelnovick/Desktop/Projects/Image_Recognition/chromedriver"}
from google_images_download import google_images_download
import sys
orig_stdout = sys.stdout
f = open('URLS.txt', 'w')
sys.stdout = f
response = google_images_download.googleimagesdownload()
arguments = {"keywords" : 'stackoverfloCommunity Discussions
Trending Discussions on google-images-download
QUESTION
I'm trying to make a one-class classification convolutional neural network. By one-class I mean I have one image dataset containing about 200 images of Nicolas Cage. By one class classification I mean look at an image and predict 1 if Nicolas Cage is contained in this image and predict 0 Nicolas Cage is not contained in the image.
I’m a definitely a machine learning/deep learning beginner so I was hoping someone with some more knowledge and experience could help guide me in the right direction. Here are my issues and questions right now. My network is performing terribly. I’ve tried making a few predictions with images of Nicolas Cage and it predicts 0 every single time.
- Should I collect more data for this to work? I’m performing data augmentations with a small dataset of 207 images. I was hoping the data augmentations would help the network generalize but I think I was wrong
- Should I try tweaking the amount of epochs, step per epoch, val steps, or the optimization algorithm I’m using for gradient descent? I’m using Adam but I was thinking maybe I should try stochastic gradient descent with different learning rates?
- Should I add more convolution or dense layers to help my network better generalize and learn?
- Should I just stop trying to do one class classification and go to normal binary classification because using a neural network with one class classification is not very feasible? I saw this post here one class classification with keras and it seems like the OP ended up using an isolation forest. So I guess I could try using some convolutional layers and feed into an isolation forest or an SVM? I could not find a lot of info or tutorials about people using isolation forests with one-class image classification.
Here is a screenshot of what my dataset looks like that I’ve collected use a package called google-images-download. It contains about 200 images of Nicolas Cage. I did two searches to download 500 images. After manually cleaning the images I was down to 200 quality pictures of Nic Cage. Dataset
The imports and model: ...ANSWER
Answered 2019-Aug-02 at 07:46Treating your problem as supervised problem:
You are solving a face recognition problem. Your problem is binary classification problem if you want to distinguish between "Nicolas Cage" or any other random image. For binary classification you need to have a class with 0 label or not "Nicolas Cage" class.
If I take a very famous example then it is Hotdog-Not-Hotdog problem (Silicon Valley). These links might help you.
https://github.com/J-Yash/Hotdog-Not-Hotdog/blob/master/Hotdog_classifier_transfer_learning.ipynb
Treating your problem as Unsupervised problem:
In this you can represent your image into an embedding vector. Pass your Nicolas Cage image into a pre-trained facenet that will give you face embedding and plot that embedding to see the relation between every image.
https://paperswithcode.com/paper/facenet-a-unified-embedding-for-face
QUESTION
I have a image data set that I want to use to train a CNN. I have initialized a class "tensorflow.python.data.ops.dataset_ops.DatasetV1Adapter" object that I understand is essentially an iterator that caches the train images in batches so that the entire data set need not be loaded at once.
I have received this error when trying to call model.fit():
...ANSWER
Answered 2020-Apr-14 at 03:29Two problems,
Your wildcard for directory matching appears to be incorrect.By looking at your code, it seems that your data needs to follow a structure like,
QUESTION
Is there a way to retrieve the image URLs from the images that are downloaded by google-images-download (https://github.com/hardikvasa/google-images-download)?
Here's some sample code:
...ANSWER
Answered 2018-Dec-05 at 02:42Implementing a good solution is pretty difficult in practice. That's why we build SerpAPI.com !
It is a web service that enables to search through Google Images and returns a clean json. it integrates with most of the programming languages: python, php, java, golang, nodejs...
https://serpapi.com/images-results
Google limit the number of search per day. but this service provides unlimited searches...
QUESTION
ANSWER
Answered 2018-Jul-25 at 14:26you need to override the methods in google_images_download including: download_image, _get_all_items, download. _get_all_items and download need to post download_image
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No vulnerabilities reported
Install google-images-download
You can use google-images-download like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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