I've got code along the lines of the following which generates a new image out of some existing images.
from PIL import Image as pyImage
def create_compound_image(back_image_path, fore_image_path, fore_x_position):
back_image_size = get_image_size(back_image_path)
fore_image_size = get_image_size(fore_image_path)
new_image_width = (fore_image_size[0] / 2) + back_image_size[0]
new_image_height = fore_image_size[1] + back_image_size[1]
new_image = create_new_image_canvas(new_image_width, new_image_height)
back_image = pyImage.open(back_image_path)
fore_image = pyImage.open(fore_image_path)
new_image.paste(back_image, (0, 0), mask = None)
new_image.paste(fore_image, (fore_x_position, back_image_size[1]), mask = None)
return new_image
Later in the code, I've got something like this:
from kivy.uix.image import Image
img = Image(source = create_compound_image(...))
If I do the above, I get the message that Image.source only accepts string/unicode.
If I create a StringIO.StringIO() object from the new image, and try to use that as the source, the error message is the same as above. If I use the output of the StringIO object's getvalue() method as the source, the message is that the source must be encoded string without NULL bytes, not str.
What is the proper way to use the output of the create_compound_image() function as the source when creating a kivy Image object?
It seems you want to just combine two images into one, you can actually just create a texture using Texture.create and blit the data to a particular pos using Texture.blit_buffer .
from kivy.core.image import Image
from kivy.graphics import Texture
bkimg = Image(bk_img_path)
frimg = Image(fr_img_path)
new_size = ((frimg.texture.size[0]/2) + bkimg.texture.size[0],
frimg.texture.size[1] + bkimg.texture.size[1])
tex = Texture.create(size=new_size)
tex.blit_buffer(pbuffer=bkimg.texture.pixels, pos=(0, 0), size=bkimg.texture.size)
tex.blit_buffer(pbuffer=frimg.texture.pixels, pos=(fore_x_position, bkimg.texture.size[1]), size=frimg.texture.size)
Now you can use this texture anywhere directly like::
from kivy.uix.image import Image
image = Image()
image.texture = tex
source is a StringProperty and is expecting a path to file. That's why you got errors when you tried to pass PIL.Image object, StringIO object or string representation of image. It's not what framework wants. As for getting image from StringIO, it was discussed before here:
https://groups.google.com/forum/#!topic/kivy-users/l-3FJ2mA3qI
https://github.com/kivy/kivy/issues/684
You can also try much simpler, quick and dirty method - just save your image as a tmp file and read it normal way.
Related
I have a directory of images and an image that I know is in this image directory there is a similar image in the directory saved in a different format and scaled differently, but I dont know where (about 100 000 images).
I want to look for the image and find out its filename inside this directory.
I am looking for a mostly already made soulution which I couldn't find. I found OpenCV but I would need to write code around that. Is there a project like that out there?
If there isn't could you help me make a simple C# console app using OpenCV, I tried their templates but never managed to get SURF or CudaSURF working.
Thanks
Edited as per #Mark Setchell's comment
If the image is identical, the fastest way is to get the file size of the image you are looking for and compare it with the file sizes of the images amongst which you are searching.
I suggest this first because, as Christoph clarifies in the comments, it doesn't require reading the file at all - it is just metadata.
If that yields more than one matching answer, calculate a hash (MD5 or other) and pick the filename that produces the same hash.
Again, as mentioned by Christoph in the comments, this doesn't require decoding the image, or holding the decompressed image in RAM, just checksumming it.
So in the end I used this site and modified the python code used there for searching a directory instead of a single image. There is not much code so the full thing is below:
import argparse
from ast import For, arg
import cv2
from os import listdir
from os.path import isfile, join
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", type=str, required=True,
help="path to input image where we'll apply template matching")
ap.add_argument("-t", "--template", type=str, required=True,
help="path to template image")
args = vars(ap.parse_args())
# load the input image and template image from disk
print("[INFO] loading template...")
template = cv2.imread(args["template"])
cv2.namedWindow("Output")
cv2.startWindowThread()
# Display an image
cv2.imshow("Output", template)
cv2.waitKey(0)
# convert both the image and template to grayscale
templateGray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
imageFileNames = [f for f in listdir(args["image"]) if isfile(join(args["image"], f))]
for imageFileName in imageFileNames:
try:
imagePath = args["image"] + imageFileName
print("[INFO] Loading " + imagePath + " from disk...")
image = cv2.imread(imagePath)
print("[INFO] Converting " + imageFileName + " to grayscale...")
imageGray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print("[INFO] Performing template matching for " + imageFileName + "...")
result = cv2.matchTemplate(imageGray, templateGray,
cv2.TM_CCOEFF_NORMED)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(result)
(startX, startY) = maxLoc
endX = startX + template.shape[1]
endY = startY + template.shape[0]
if maxVal > 0.75:
print("maxVal = " + str(maxVal))
# draw the bounding box on the image
cv2.rectangle(image, (startX, startY), (endX, endY), (255, 0, 0), 3)
# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)
cv2.imshow("Output", template)
except KeyboardInterrupt:
break
except:
print(imageFileName)
print("Error")
cv2.destroyAllWindows()
The code above shows any image with match value (what I guess is how much similarity there is between source and template) greater than 0.75
Probably still too low but if you want to use it tweak it to your liking.
Note that this WILL NOT work if the image is rotated and if, like me, you have a bright light source in the template other lightsources will come up as false positives
As for time it took me about 7 hours, where the script paused about every 20 minutes for a false positive until I found my image. I got through about 2/3 of all images.
as a sidenote it took 10 minutes to just build the array of files inside the directory, and it took about 500mb of ram once done
This is not the best answer so if anyone more qualified finds this feel free to write another answer.
I want to load a dataset of grayscale images. I used ImageFolder but this doesn't load gray images by default as it converts images to RGB.
I found solutions that load images with ImageFolder and after convert images in grayscale, using:
transforms.Grayscale(num_output_channels=1)
or
ImageOps.grayscale(image)
Is it correct?
How can I load grayscale imaged without conversion? I try ImageDataBunch, but I have problems to import fastai.vision
Assuming the dataset is stored in the "Dataset" folder as given below, set the root directory as "Dataset":
Dataset
class_1
img1.png
img2.png
class_2
img1.png
img2.png
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
root = 'Dataset/'
data_transform = transforms.Compose([transforms.Grayscale(num_output_channels=1),
transforms.ToTensor()])
dataset = ImageFolder(root, transform=data_transform)
For reference, train and test dataset are being split into 70% and 30% respectively.
# Split test and train dataset
train_size = int(0.7 * len(dataset))
test_size = len(dataset) - train_size
train_data, test_data = random_split(dataset, [train_size, test_size])
This dataset can be further divided into train and test data loaders as given below to perform operation in batches.
Usually you will see the dataset is assigned batch_size once to be used for both train and test loaders. But, I try to define it separately. The idea is to give the batch_size such that it is a factor of the train/test data loader's size, otherwise it will give an error.
# Set batch size of train data loader
batch_size_train = 20
# Set batch size of test data loader
batch_size_test = 22
# load the split train and test data into batches via DataLoader()
train_loader = DataLoader(train_data, batch_size=batch_size_train, shuffle=True)
test_loader = DataLoader(test_data, batch_size=batch_size_test, shuffle=True)
Yes, that is correct and AFAIK pillow by default loads images in RGB, see e.g. answers to this question. So conversion to grayscale is the only way, though takes time of course.
Pure pytorch solution (if ImageFolder isn't appropriate)
You can roll out your own data loading functionalities and If I were you I wouldn't go fastai route as it's pretty high level and takes away control from you (you might not need those functionalities anyway).
In principle, all you have to do is to create something like this below:
import pathlib
import torch
from PIL import Image
class ImageDataset(torch.utils.data.Dataset):
def __init__(self, path: pathlib.Path, images_class: int, regex="*.png"):
self.files = [file for file in path.glob(regex)]
self.images_class: int = images_class
def __getitem__(self, index):
return Image.open(self.files[index]).convert("LA"), self.images_class
# Assuming you have `png` images, can modify that with regex
final_dataset = (
ImageDataset(pathlib.Path("/path/to/dogs/images"), 0)
+ ImageDataset(pathlib.Path("/path/to/cats/images"), 1)
+ ImageDataset(pathlib.Path("/path/to/turtles/images"), 2)
)
Above would get you images from the paths provided above and each image would return appropriate provided class.
This gives you more flexibility (different folder setting than torchvision.datasets.ImageFolder) for a few more lines.
Ofc, you could add more of those or use loop or whatever else.
You could also apply torchvision.transforms, e.g. transforming images above to tensors, read
torchdata solution
Disclaimer, author here. If you are cocerned about loading times of your data and grayscale transformation you could use torchdata third party library for pytorch.
Using it one could create the same thing as above but use cache or map (to use torchvision.transforms or other transformations easily) and some other things known e.g. from tensorflow.data module, see below:
import pathlib
from PIL import Image
import torchdata
# Change inheritance
class ImageDataset(torchdata.Dataset):
def __init__(self, path: pathlib.Path, images_class: int, regex="*.png"):
super().__init__() # And add constructor call and that's it
self.files = [file for file in path.glob(regex)]
self.images_class: int = images_class
def __getitem__(self, index):
return Image.open(self.files[index]), self.images_class
final_dataset = (
ImageDataset(pathlib.Path("/path/to/dogs/images"), 0)
+ ImageDataset(pathlib.Path("/path/to/cats/images"), 1)
+ ImageDataset(pathlib.Path("/path/to/turtles/images"), 2)
).cache() # will cache data in-memory after first pass
# You could apply transformations after caching for possible speed-up
torchvision ImageFolder loader
As correctly pointed out by #jodag in the comments, one can use loader callable with single argument path to do customized data opening, e.g. for grayscale it could be:
from PIL import Image
import torchvision
dataset = torchvision.datasets.ImageFolder(
"/path/to/images", loader=lambda path: Image.open(path).convert("LA")
)
Please notice you could also use it for other types of files, those doesn't have to be images.
Make custom loader, feed it to ImageFolder:
import numpy as np
from PIL import Image, ImageOps
def gray_reader(image_path):
im = Image.open(image_path)
im2 = ImageOps.grayscale(im)
im.close()
return np.array(im2) # return np array
# return im2 # return PIL Image
some_dataset = ImageFolder(image_root_path, loader=gray_reader)
Edit:
Below code is much better than previous, get color image and convert to grayscale in transform()
def get_transformer(h, w):
valid_transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Grayscale(num_output_channels=1),
transforms.Resize((h, w)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]) ])
return valid_transform
I am sending an image by curl to flask server, i am using this curl command
curl -F "file=#image.jpg" http://localhost:8000/home
and I am trying to read the file using OpenCV on the server side.
On the server side I handle the image by this code
#app.route('/home', methods=['POST'])
def home():
data =request.files['file']
img = cv.imread(data)
fact_resp= model.predict(img)
return jsonify(fact_resp)
I am getting this error-
img = cv.imread(data)
TypeError: expected string or Unicode object, FileStorage found
How do I read the file using OpenCV on the server side?
Thanks!
I had similar issues while using opencv with flask server, for that first i saved the image to disk and read that image using saved filepath again using cv.imread()
Here is a sample code:
data =request.files['file']
filename = secure_filename(file.filename) # save file
filepath = os.path.join(app.config['imgdir'], filename);
file.save(filepath)
cv.imread(filepath)
But now i have got even more efficient approach from here by using cv.imdecode() to read image from numpy array as below:
#read image file string data
filestr = request.files['file'].read()
#convert string data to numpy array
file_bytes = numpy.fromstring(filestr, numpy.uint8)
# convert numpy array to image
img = cv.imdecode(file_bytes, cv.IMREAD_UNCHANGED)
After a bit of experimentation, I myself figured out a way to read the file using CV2.
For this I first read the image using PIL.image method
This is my code,
#app.route('/home', methods=['POST'])
def home():
data =request.files['file']
img = Image.open(request.files['file'])
img = np.array(img)
img = cv2.resize(img,(224,224))
img = cv2.cvtColor(np.array(img), cv2.COLOR_BGR2RGB)
fact_resp= model.predict(img)
return jsonify(fact_resp)
I wonder if there is any straight forward way to do this without using PIL.
So incase you want to do something like ,
file = request.files['file']
img = cv.imread(file)
then do it like this
import numpy as np
file = request.files['file']
file_bytes = np.fromfile(file, np.uint8)
file = cv.imdecode(file_bytes, cv.IMREAD_COLOR)
Now you don't need to do cv.imread() again, but can use this in the next line of codes.
This applies to OpenCV v3.x and onwards
Two-line solution, change grayscale to what you need
file_bytes = numpy.fromfile(request.files['image'], numpy.uint8)
# convert numpy array to image
img = cv.imdecode(file_bytes, cv.IMREAD_GRAYSCALE)
I would like to write the equivalent of
convert left.jpg right.jpg +append ouput.jpg
I found something like it in another post:
files = glob('*.jpg')
with Image() as orig: # create empty Image object
for f in files:
page = Image(filename=f)
orig.sequence.append(page)
orig.save(filename='result.pdf')
and changed it to
with Image() as orig: # create empty Image object
page = Image(filename='left.jpg'); orig.sequence.append(page)
page = Image(filename='right.jpg'); orig.sequence.append(page)
orig.save(filename='output.jpg')
but the output file just shows the first file, rather than a file with the images side-by-side.
My first attempt was completely wrong, it probably makes an animated image. Provided the two images are the same size, this will do it:
with Image() as blankimage:
with Image(filename = 'imageA.tif') as imageA:
w = imageA.width; h = imageA.height
with Image(filename = 'imageB.tif') as imageB:
blankimage.blank(w*2, h)
blankimage.composite(imageA, 0, 0)
blankimage.composite(imageB, w, 0)
blankimage.save(filename = 'output.tif')
I am building an application to continuously display an image fetched from an IP camera. I have figured out how to fetch the image, and how to also display the image using Tkinter. But I cannot get it to continuously refresh the image. Using Python 2.7+.
Here is the code I have so far.
import urllib2, base64
from PIL import Image,ImageTk
import StringIO
import Tkinter
URL = 'http://myurl.cgi'
USERNAME = 'myusername'
PASSWORD = 'mypassword'
def fetch_image(url,username,password):
# this code works fine
request = urllib2.Request(url)
base64string = base64.encodestring('%s:%s' % (username, password)).replace('\n', '')
request.add_header("Authorization", "Basic %s" % base64string)
result = urllib2.urlopen(request)
imgresp = result.read()
img = Image.open(StringIO.StringIO(imgresp))
return img
root = Tkinter.Tk()
img = fetch_image(URL,USERNAME,PASSWORD)
tkimg = ImageTk.PhotoImage(img)
Tkinter.Label(root,image=tkimg).pack()
root.mainloop()
How should I edit the code so that the fetch_image is called repeatedly and its output updated in the Tkinter window?
Note that I am not using any button-events to trigger the image refresh, rather it should be refreshed automatically, say, every 1 second.
Here is a solution that uses Tkinter's Tk.after function, which schedules future calls to functions. If you replace everything after your fetch_image definition with the snipped below, you'll get the behavior you described:
root = Tkinter.Tk()
label = Tkinter.Label(root)
label.pack()
img = None
tkimg = [None] # This, or something like it, is necessary because if you do not keep a reference to PhotoImage instances, they get garbage collected.
delay = 500 # in milliseconds
def loopCapture():
print "capturing"
# img = fetch_image(URL,USERNAME,PASSWORD)
img = Image.new('1', (100, 100), 0)
tkimg[0] = ImageTk.PhotoImage(img)
label.config(image=tkimg[0])
root.update_idletasks()
root.after(delay, loopCapture)
loopCapture()
root.mainloop()