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Faster normalization of image (numpy array)
The Next CEO of Stack OverflowPeak detection in a 2D arrayDump a NumPy array into a csv fileWhy are elementwise additions much faster in separate loops than in a combined loop?Why is it faster to process a sorted array than an unsorted array?Why does Python code run faster in a function?Is < faster than <=?Numpy dot() and array casting performace optimizationDifference between every pair of columns of two numpy arrays (how to do it more efficiently)?Why is [] faster than list()?Faster performance for normalizing a numpy array?
I've got function that takes some image as a input and changes it values (scales) in a way that average value will be 96. Here is the function:
def normalize_image(image: np.ndarray):
image_median = np.median(image[image > 0])
image = image * 96.0 / image_median
image[image > 255] = 255
return image
I am using python 3.5.3 and numpy 1.15.2. I profiled my code with cProfile and it turned out that this function takes 6% of all time (in some scenarios up to 25% of all time) having only 50 calls. These array have shape of (155,256,256).
I am not very experienced with optimizing python and I wonder if this could be made faster somehow?
Normally I would start with using SIMD optimization but numpy use them heavily already.
python performance numpy
asked Mar 8 at 12:25
wdudzikwdudzik
823717
add a comment |
I've got function that takes some image as a input and changes it values (scales) in a way that average value will be 96. Here is the function:
def normalize_image(image: np.ndarray):
image_median = np.median(image[image > 0])
image = image * 96.0 / image_median
image[image > 255] = 255
return image
I am using python 3.5.3 and numpy 1.15.2. I profiled my code with cProfile and it turned out that this function takes 6% of all time (in some scenarios up to 25% of all time) having only 50 calls. These array have shape of (155,256,256).
I am not very experienced with optimizing python and I wonder if this could be made faster somehow?
Normally I would start with using SIMD optimization but numpy use them heavily already.
python performance numpy
asked Mar 8 at 12:25
wdudzikwdudzik
823717
1
The median calculation could take a long time (needs sorting of the array). Can you maybe replace it with the mean?
– Trilarion
Mar 8 at 12:29
@Trilarion it will provide worse results. I am not sure how big is the difference between mean and median for all of the images that I have. I tested it and there is almost no time difference between mean and median in this case.
– wdudzik
Mar 8 at 12:34
1
Microoptimization: replaceimage = image * 96.0 / image_medianwithimage = image * (96.0 / image_median).
– Warren Weckesser
Mar 8 at 12:49
add a comment |
I've got function that takes some image as a input and changes it values (scales) in a way that average value will be 96. Here is the function:
def normalize_image(image: np.ndarray):
image_median = np.median(image[image > 0])
image = image * 96.0 / image_median
image[image > 255] = 255
return image
I am using python 3.5.3 and numpy 1.15.2. I profiled my code with cProfile and it turned out that this function takes 6% of all time (in some scenarios up to 25% of all time) having only 50 calls. These array have shape of (155,256,256).
I am not very experienced with optimizing python and I wonder if this could be made faster somehow?
Normally I would start with using SIMD optimization but numpy use them heavily already.
python performance numpy
asked Mar 8 at 12:25
wdudzikwdudzik
823717
I've got function that takes some image as a input and changes it values (scales) in a way that average value will be 96. Here is the function:
def normalize_image(image: np.ndarray):
image_median = np.median(image[image > 0])
image = image * 96.0 / image_median
image[image > 255] = 255
return image
I am using python 3.5.3 and numpy 1.15.2. I profiled my code with cProfile and it turned out that this function takes 6% of all time (in some scenarios up to 25% of all time) having only 50 calls. These array have shape of (155,256,256).
I am not very experienced with optimizing python and I wonder if this could be made faster somehow?
Normally I would start with using SIMD optimization but numpy use them heavily already.
python performance numpy
python performance numpy
asked Mar 8 at 12:25
wdudzikwdudzik
823717
asked Mar 8 at 12:25
wdudzikwdudzik
823717
edited Mar 8 at 12:35
wdudzik
asked Mar 8 at 12:25
wdudzikwdudzik
823717
asked Mar 8 at 12:25
wdudzikwdudzik
823717
asked Mar 8 at 12:25
wdudzikwdudzik
823717
823717
1
The median calculation could take a long time (needs sorting of the array). Can you maybe replace it with the mean?
– Trilarion
Mar 8 at 12:29
@Trilarion it will provide worse results. I am not sure how big is the difference between mean and median for all of the images that I have. I tested it and there is almost no time difference between mean and median in this case.
– wdudzik
Mar 8 at 12:34
1
Microoptimization: replaceimage = image * 96.0 / image_medianwithimage = image * (96.0 / image_median).
– Warren Weckesser
Mar 8 at 12:49
add a comment |
1
The median calculation could take a long time (needs sorting of the array). Can you maybe replace it with the mean?
– Trilarion
Mar 8 at 12:29
@Trilarion it will provide worse results. I am not sure how big is the difference between mean and median for all of the images that I have. I tested it and there is almost no time difference between mean and median in this case.
– wdudzik
Mar 8 at 12:34
1
Microoptimization: replaceimage = image * 96.0 / image_medianwithimage = image * (96.0 / image_median).
– Warren Weckesser
Mar 8 at 12:49
1
1
The median calculation could take a long time (needs sorting of the array). Can you maybe replace it with the mean?
– Trilarion
Mar 8 at 12:29
The median calculation could take a long time (needs sorting of the array). Can you maybe replace it with the mean?
– Trilarion
Mar 8 at 12:29
@Trilarion it will provide worse results. I am not sure how big is the difference between mean and median for all of the images that I have. I tested it and there is almost no time difference between mean and median in this case.
– wdudzik
Mar 8 at 12:34
@Trilarion it will provide worse results. I am not sure how big is the difference between mean and median for all of the images that I have. I tested it and there is almost no time difference between mean and median in this case.
– wdudzik
Mar 8 at 12:34
1
1
Microoptimization: replace
image = image * 96.0 / image_median with image = image * (96.0 / image_median).– Warren Weckesser
Mar 8 at 12:49
Microoptimization: replace
image = image * 96.0 / image_median with image = image * (96.0 / image_median).– Warren Weckesser
Mar 8 at 12:49
add a comment |
1 Answer
1
active
oldest
votes
There is not much you can do quickly here.
You already use NumPy in a vectorized manner, so internally C code is executed that probably is already quite optimized.
The calculation of the median can take much longer than calculating the mean (because sorting is involved). Consider replacing it.
Adding some parentheses should save a division of the array
image = image * (96.0 / image_median)
because between operators of equal precedence Python goes from left to right.
answered Mar 8 at 12:50
TrilarionTrilarion
6,84054179
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
There is not much you can do quickly here.
You already use NumPy in a vectorized manner, so internally C code is executed that probably is already quite optimized.
The calculation of the median can take much longer than calculating the mean (because sorting is involved). Consider replacing it.
Adding some parentheses should save a division of the array
image = image * (96.0 / image_median)
because between operators of equal precedence Python goes from left to right.
answered Mar 8 at 12:50
TrilarionTrilarion
6,84054179
add a comment |
There is not much you can do quickly here.
You already use NumPy in a vectorized manner, so internally C code is executed that probably is already quite optimized.
The calculation of the median can take much longer than calculating the mean (because sorting is involved). Consider replacing it.
Adding some parentheses should save a division of the array
image = image * (96.0 / image_median)
because between operators of equal precedence Python goes from left to right.
answered Mar 8 at 12:50
TrilarionTrilarion
6,84054179
add a comment |
There is not much you can do quickly here.
You already use NumPy in a vectorized manner, so internally C code is executed that probably is already quite optimized.
The calculation of the median can take much longer than calculating the mean (because sorting is involved). Consider replacing it.
Adding some parentheses should save a division of the array
image = image * (96.0 / image_median)
because between operators of equal precedence Python goes from left to right.
answered Mar 8 at 12:50
TrilarionTrilarion
6,84054179
There is not much you can do quickly here.
You already use NumPy in a vectorized manner, so internally C code is executed that probably is already quite optimized.
The calculation of the median can take much longer than calculating the mean (because sorting is involved). Consider replacing it.
Adding some parentheses should save a division of the array
image = image * (96.0 / image_median)
because between operators of equal precedence Python goes from left to right.
answered Mar 8 at 12:50
TrilarionTrilarion
6,84054179
answered Mar 8 at 12:50
TrilarionTrilarion
6,84054179
answered Mar 8 at 12:50
TrilarionTrilarion
6,84054179
answered Mar 8 at 12:50
TrilarionTrilarion
6,84054179
6,84054179
add a comment |
add a comment |
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1
The median calculation could take a long time (needs sorting of the array). Can you maybe replace it with the mean?
– Trilarion
Mar 8 at 12:29
@Trilarion it will provide worse results. I am not sure how big is the difference between mean and median for all of the images that I have. I tested it and there is almost no time difference between mean and median in this case.
– wdudzik
Mar 8 at 12:34
1
Microoptimization: replace
image = image * 96.0 / image_medianwithimage = image * (96.0 / image_median).– Warren Weckesser
Mar 8 at 12:49