I'm trying to resize an image to 500x500px but got this error:
File "C:\Python27\lib\site-packages\PIL\Image.py", line 1681, in save
save_handler = SAVE[format.upper()] KeyError: 'JPG'
This is the code:
from PIL import Image
img = Image.open('car.jpg')
new_img = img.resize((500,500))
new_img.save('car_resized','jpg')
You need to set the format parameter in your call to the save function to 'JPEG':
from PIL import Image
img = Image.open('car.jpg')
new_img = img.resize((500,500))
new_img.save("car_resized.jpg", "JPEG", optimize=True)
Here is the solution:
from PIL import Image
img = Image.open('car.jpg')
new_img = img.resize((500,500), Image.ANTIALIAS)
quality_val = 90 ##you can vary it considering the tradeoff for quality vs performance
new_img.save("car_resized.jpg", "JPEG", quality=quality_val)
There are list of resampling techniques in PIL like ANTIALIAS, BICUBIC, BILINEAR and CUBIC.
ANTIALIAS is considered best for scaling down.
Related
I am trying to create a layer on a DICOM image, below code works fine for jpg/png images but not for DICOM.
import cv2
import numpy as np
import pydicom as dicom
ds=dicom.dcmread('D0009.dcm')
img=ds.pixel_array
blank = np.zeros(shape=(img.shape[0],img.shape[1],3), dtype=np.uint8)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(blank,
text='Logo',
org=(img.shape[1]//8, img.shape[0]//2),
fontFace=font,
fontScale= 2,color=(163,163,163),
thickness=11,
lineType=cv2.LINE_4)
blend=cv2.addWeighted(img,0.7,blank,1, 0, dtype = cv2.CV_32F)
cv2.imshow('sample image dicom',blend)
cv2.waitKey()
any help would be apreciated
I was able to get this working by normalizing the value range of the DICOM image and converting the DICOM image from greyscale to RGB image. Replace your line
img=ds.pixel_array
with these lines:
img = np.array(ds.pixel_array, dtype='float32')
img /= np.max(img)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
I'm converting a .png image to float32 the following way and I'm obtaining a broken image as shown below. If I remove the tf.image.convert_image_dtype call, everything goes well.
image = tf.io.read_file(filename)
image = tf.image.decode_png(image, channels=3)
image = tf.image.convert_image_dtype(image, tf.float32)
I've also tried different images with different formats like .bmp and .jpg but same thing happens. The code I use to visualize the image generated the above way is just:
a = a.numpy()
a = Image.fromarray(a, 'RGB')
As I've said, if I just remove the tf.image.convert_image_dtype call everything goes well.
Here are the download links of both images (I have less than 10 reputation here so I can't upload photos yet).
original_image
obtained_image
You can convert it back to integer like this
import tensorflow as tf
import numpy as np
from PIL import Image
image = tf.io.read_file("C:\\<your file>.png")
image = tf.image.decode_png(image, channels=3)
image = tf.image.convert_image_dtype(image, tf.float32)
a = image.numpy()
a = (a * 255 / np.max(a)).astype('uint8')
a = Image.fromarray(a, 'RGB')
a.show()
I am trying to crop an image with cv2 (converting it to a bytes file and therefore not needing to save it)and afterwards perform pytesseract.
This way i won't need to save the image twice during the process.
First when i create the image
When cropping the image
Process...
## CROPPING THE IMAGE REGION
ys, xs = np.nonzero(mask2)
ymin, ymax = ys.min(), ys.max()
xmin, xmax = xs.min(), xs.max()
croped = image[ymin:ymax, xmin:xmax]
pts = np.int32([[xmin, ymin],[xmin,ymax],[xmax,ymax],[xmax,ymin]])
cv2.drawContours(image, [pts], -1, (0,255,0), 1, cv2.LINE_AA)
#OPENCV IMAGE TO BYTES WITHOUT SAVING TO DISK
is_success, im_buf_arr = cv2.imencode(".jpg", croped)
byte_im = im_buf_arr.tobytes()
#PYTESSERACT IMAGE USING A BYTES FILE
Results = pytesseract.image_to_string(byte_im, lang="eng")
print(Results)
Unfortunately i get the error : Unsupported image object
Am i missing something? Is there a way to do this process without needing to save the file when cropping? Any help is highly appreciated.
you have croped which is a numpy array.
according to pytesseract examples, you simply do this:
# tesseract needs the right channel order
cropped_rgb = cv2.cvtColor(croped, cv2.COLOR_BGR2RGB)
# give the numpy array directly to pytesseract, no PIL or other acrobatics necessary
Results = pytesseract.image_to_string(cropped_rgb, lang="eng")
from PIL import Image
img_tesseract = Image.fromarray(croped)
Results = pytesseract.image_to_string(img_tesseract, lang="eng")
from PIL import Image
import io
def bytes_to_image(image_bytes):
io_bytes = io.BytesIO(image_bytes)
return Image.open(io_bytes)
pytesseract.image_to_data(byte_array_image,lang='eng')
I'm using PIL to resize a JPG. I'm expecting the same image, resized as output, but instead I get a correctly sized black box. The new image file is completely devoid of any information, just an empty file. Here is an excerpt for my script:
basewidth = 300
img = Image.open(path_to_image)
wpercent = (basewidth/float(img.size[0]))
hsize = int((float(img.size[1])*float(wpercent)))
img = img.resize((basewidth,hsize))
img.save(dir + "/the_image.jpg")
I've tried resizing with Image.LANCZOS as the second argument, (defaults to Image.NEAREST with 1 argument), but it didn't make a difference. I'm running Python3 on Ubunutu 16.04. Any ideas on why the image file is empty?
I also encountered the same issue when trying to resize an image with transparent background. The "resize" works after I add a white background to the image.
Code to add a white background then resize the image:
from PIL import Image
im = Image.open("path/to/img")
if im.mode == 'RGBA':
alpha = im.split()[3]
bgmask = alpha.point(lambda x: 255-x)
im = im.convert('RGB')
im.paste((255,255,255), None, bgmask)
im = im.resize((new_width, new_height), Image.ANTIALIAS)
ref:
Other's code for making thumbnail
Python: Image resizing: keep proportion - add white background
The simplest way to get to the bottom of this is to post your image! Failing that, we can check the various aspects of your image.
So, import Numpy and PIL, open your image and convert it to a Numpy ndarray, you can then inspect its characteristics:
import numpy as np
from PIL import Image
# Open image
img = Image.open('unhappy.jpg')
# Convert to Numpy Array
n = np.array(img)
Now you can print and inspect the following things:
n.shape # we are expecting something like (1580, 1725, 3)
n.dtype # we expect dtype('uint8')
n.max() # if there's white in the image, we expect 255
n.min() # if there's black in the image, we expect 0
n.mean() # we expect some value between 50-200 for most images
Disaster!
As you can see, the image isn't quite loaded correctly. The original:
The code:
import cv2
import imutils
a=imutils.url_to_image("https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_272x92dp.png", readFlag=-1)
cv2.imshow("goog", a)
cv2.waitKey()
The implementation of url_to_image in imutils:
def url_to_image(url, readFlag=cv2.IMREAD_COLOR):
# download the image, convert it to a NumPy array, and then read
# it into OpenCV format
resp = urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype="uint8")
image = cv2.imdecode(image, readFlag)
# return the image
return image
I also tried readFlag=cv2.IMREAD_UNCHANGED, but that didn't do the trick either.
please send help
alright gang we did it
so I tried another version of displaying:
plt.figure("Correct")
plt.imshow(imutils.opencv2matplotlib(a))
plt.show()
No luck it would appear. But then, looking into the opencv2matplotlib source, we find:
def opencv2matplotlib(image):
# OpenCV represents images in BGR order; however, Matplotlib
# expects the image in RGB order, so simply convert from BGR
# to RGB and return
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
Aha, but we have 4 channel color (alpha), so by common sense we need cv2.COLOR_BGRA2RGBA not cv2.COLOR_BGR2RGB!!
Testing this theory:
plt.figure("Correct")
plt.imshow(cv2.cvtColor(a, cv2.COLOR_BGRA2RGBA))
plt.show()
We get...
Whoop dee doop!
# import the necessary packages
import numpy as np
import urllib
import cv2
def url_to_image(url):
# download the image, convert it to a NumPy array, and then read
# it into OpenCV format
resp = urllib.request.urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype="uint8")
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
# return the image
return image
# initialize the list of image URLs to download
url="http://i.dailymail.co.uk/i/pix/2015/09/01/18/2BE1E88B00000578-3218613-image-m-5_1441127035222.jpg"
print ("downloading %s" % (url))
image = url_to_image(url)
cv2.imshow("Image", image)
cv2.waitKey(0)
And the output is: