image_to_numpy | image file into a numpy array | Computer Vision library
kandi X-RAY | image_to_numpy Summary
kandi X-RAY | image_to_numpy Summary
Most images captured by cell phones and consumer cameras aren't stored on disk in the same orientation they appear on screen. Exif Orientation data tells the program which way the image needs to be rotated to display correctly. Not handling Exif Orientation is a common source of bugs in Computer Vision and Machine Learning applications.
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Top functions reviewed by kandi - BETA
- Transpose an EXIF image .
- Load an image file .
image_to_numpy Key Features
image_to_numpy Examples and Code Snippets
Lines of Code : 8License : Strong Copyleft (CC BY-SA 4.0)
Copyimgdata = np.interp(imgdata, (imgdata.min(), imgdata.max()), (0, +1))
image = imgdata
sample = np.array([10, 20, 30], dtype=np.uint8)
sample2 = sample * 10
print(sample, sample2)
np.stack(X_image_array)
import os
...
imgTypes = ['jpg', 'png', 'gif', 'bmp']
train_data = [item for item in os.listdir(train_path) if \
(os.path.isfile(os.path.join(train_path, item)) and
os.path.splitext(item)[1].lower() in imgTypes)]
def image_2_data_2_GLCM305():
images = glob.glob("*.jpg")
for i in range(1, 11):
print(i)
data = []
for image in images:
img = cv2.imread(image,0)
img = img[i:i+1] # 640*480==> def rec_on_button_press(self,event):
self.start_x = event.x
self.start_y = event.y
self.rect=self.image_canvas.create_rectangle(self.x, self.y, 1, 1,fill=self.python_red)
def rec_on_move(self, event):
curX, recognizer.train(faces, np.array(Ids))
#Convert the image to numpy array and normalization.
resized_img = np.array([resized_img]) / 255.0
grey = Image.open(fname).convert('L')
img = cv2.resize(img, (width, height))
# convert image to numpy
x = numpy.asarray(img).astype().reshape()
res = sess.run([output_name], {input_name: x})
ims = [np.asarray(r.convert('RGB')).astype('float32') / 255.0) for r in images]
Community Discussions
Trending Discussions on image_to_numpy
QUESTION
I would like to split an image into triangle shaped tiles (equilateral) . I have tried to generate the coordinates of a triangle using the function from https://alexwlchan.net/2016/10/tiling-the-plane-with-pillow/. My code:
...ANSWER
Answered 2021-Mar-11 at 22:08I'm posting this as an answer because it's long, but it's not literally an answer. I'm hoping this will lead you to the next step in your design process.
Here are the design decisions you face, It's clear from your code that you can generate a list of triangle coordinates. Good, what next? You probably know the bounding box of your triangles (largest w and h) advance, so you can certainly create a set of images that contain your triangles, masked off with a black background or alpha=0 background. You could just copy the bounding rectangle to an image, then create a mask using the triangle as a path, and set the alpha to 0 outside of the mask. opencv should be able to do that.
But after you have those, what then? You talked about matching the edges. That's complicated. I suppose you could extract a vector of pixels from the three edges of each triangle, and then do some kind of approximate comparison.
If you do find matches that allow you to stitch together a composite, it is possible (assuming you have alpha=0 in the backgrounds) to blit all of these triangles back into some larger image, kind of like quilting. openvc can do block copy with alpha blending.
So, in the end, I think your problem is achievable, but it's going to be a lot of work, and probably more than we can offer here.
QUESTION
Just like the title says I m trying to Load an image file into a numpy array with Exif orientation support. I m doing this to Prevents upside-down and sideways images for face_recognition as it does not work on pictures taking by Iphones. To fix that I m using this script:
...ANSWER
Answered 2020-Jun-18 at 17:39I found this answer: https://stackoverflow.com/a/26928142/12076702.
The only changes you would need to make is to change filepath in image=Image.open(filepath) to the original image file path and this filepath, image.save(filepath) to the file path you want to save the image to.
You can use glob.glob() to get all the image file paths of a format of your choice in the specified directory by doing something like:
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