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| @@ -1,17 +1,21 @@ | ||
| <h4 id="batchshape"><a href="#batchshape">Batch analysis of nuclear shapes</a></h4> | ||
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| - Download the input.zip containing the input images from [here](https://github.com/NEUBIAS/training-resources/tree/master/image_data/batch_process) and unpack to your course directory | ||
| - In a previous module [there is a workflow to measure the shapes of nuclei in one image](https://neubias.github.io/training-resources/workflow_segment_2d_nuclei_measure_shape/index.html#2dnuclei) | ||
| - Adapt this workflow for automated batch analysis of many images | ||
| - Start by building the skeleton of the workflow without filling in the functionality; | ||
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| Note that the code below runs fine, but does not produce any results: | ||
| Note that the pseudo-code below will run fine, but does not produce any results: | ||
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| ``` | ||
| def analyse(image_path, output_folder): | ||
| print("Analyzing:", image_path) | ||
| for image_path in image_paths: | ||
| analyse(image_path, output_dir) | ||
| FUNCTION analyse(image_path, output_folder) | ||
| PRINT "Analyzing:", image_path | ||
| END FUNCTION | ||
| FOR each image_path in image_paths | ||
| CALL analyse(image_path, output_dir) | ||
| END FOR | ||
| ``` | ||
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| - Make sure the loop with the (almost) empty analyse function runs without error before filling in the image analysis steps | ||
| - Make sure the loop with the (almost) empty analyse function runs without error before filling in the image analysis steps | ||
| - Inspect the analysis results in a suitable software |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,164 @@ | ||
| # %% | ||
| # Create a bunch of example images to simulate a typical analysis problem | ||
| # np.random.randint(lower,upper,size): generates random integers from lower to upper | ||
| # .reshape: reshapes a np.array to new dimensions | ||
| import numpy as np | ||
| image1 = np.random.randint(0,255,100).reshape((10,10)) | ||
| image2 = np.random.randint(0,255,100).reshape((10,10)) | ||
| image3 = np.random.randint(0,255,100).reshape((10,10)) | ||
| image4 = np.random.randint(0,255,100).reshape((10,10)) | ||
| image5 = np.random.randint(0,255,100).reshape((10,10)) | ||
| image6 = np.random.randint(0,255,100).reshape((10,10)) | ||
| image7 = np.random.randint(0,255,100).reshape((10,10)) | ||
| image8 = np.random.randint(0,255,100).reshape((10,10)) | ||
| image9 = np.random.randint(0,255,100).reshape((10,10)) | ||
| image10 = np.random.randint(0,255,100).reshape((10,10)) | ||
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| # Calculate the mean of every image | ||
| # Oberserve that it is very tedious and error prone | ||
| print(f'Image {1} has an avg intensity of {image1.mean()}.') | ||
| print(f'Image {2} has an avg intensity of {image2.mean()}.') | ||
| print(f'Image {3} has an avg intensity of {image3.mean()}.') | ||
| print(f'Image {4} has an avg intensity of {image4.mean()}.') | ||
| print(f'Image {5} has an avg intensity of {image5.mean()}.') | ||
| print(f'Image {6} has an avg intensity of {image6.mean()}.') | ||
| print(f'Image {7} has an avg intensity of {image7.mean()}.') | ||
| print(f'Image {8} has an avg intensity of {image8.mean()}.') | ||
| print(f'Image {9} has an avg intensity of {image9.mean()}.') | ||
| print(f'Image {10} has an avg intensity of {image10.mean()}.') | ||
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| # %% | ||
| # Create a for loop | ||
| # Typical notation in C-style: for i=0, i<10, i++ {do something} | ||
| # For loop in python | ||
| # Observe the difference in notation | ||
| for i in [0,1,2,3,4,5,6,7,8,9]: | ||
| print(i) | ||
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| # use range | ||
| # Observe that range is actually a generator producing values on the fly | ||
| print(range(10)) | ||
| print(type(range(10))) | ||
| for i in range(10): | ||
| print(i) | ||
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| # %% | ||
| # Anything iterable can be iterated over with a for loop | ||
| # Observe that: | ||
| # - list content don't matter | ||
| # - that the iterator will always be overwritten by the next item | ||
| for i in ['a','b','c',1,2,3,[1,2,3],1.5,2.7]: | ||
| print(f'"i" has the value "{i}" and type "{type(i)}"') | ||
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| # %% | ||
| # Use a for loop for the example in the beginning | ||
| # First pack images into a list for looping over | ||
| image_list = [image1,image2,image3,image4,image5,image6,image7,image8,image9,image10] | ||
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| # Loop over images and calculate the mean | ||
| for image in image_list: | ||
| print(f'Avg intensity: {image.mean()}') | ||
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| # Excersize: Modify the loop to calculate the standard deviation | ||
| for image in image_list: | ||
| print(f'Intensity standard deviation: {image.std()}') | ||
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| #%% | ||
| # Iterate with an index | ||
| number_of_images = len(image_list) | ||
| print(number_of_images) | ||
| for i in range(number_of_images): | ||
| print(f'Image {i} has an avg intensity of {image_list[i].mean()}.') | ||
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| # Iterate over two or more list (with the same length) | ||
| for i,image in zip(range(number_of_images),image_list): | ||
| print(f'Image {i} has an avg intensity of {image.mean()}.') | ||
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| # Iterate with additional index | ||
| for i,image in enumerate(image_list): | ||
| print(f'Image {i} has an avg intensity of {image.mean()}.') | ||
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| # %% | ||
| # For loops but advanced | ||
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| # %% | ||
| # list comprehension | ||
| squares = [] | ||
| for i in range(10): | ||
| squares.append(i**2) | ||
| print(squares) | ||
| # use list comprehension | ||
| squares = [i**2 for i in range(10)] | ||
| print(squares) | ||
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| # usefull for creating an filepath iterable | ||
| from pathlib import Path | ||
| [file for file in Path().cwd().iterdir()] | ||
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| # %% | ||
| # for loop with if | ||
| for x in range(10): | ||
| if x != 0: | ||
| print(f'X: {x}, 1/X: {1/x}') | ||
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| # for loop with if and else | ||
| for x in range(10): | ||
| if x == 0: | ||
| print(f'X: {x}, 1/X: Division by {x} not defined.') | ||
| else: | ||
| print(f'X: {x}, 1/X: {1/x}') | ||
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| # %% | ||
| # for loop with continue and break | ||
| # Usage: | ||
| # - skip files/data for processing | ||
| # - debugging | ||
| # continue | ||
| for x in range(10): | ||
| if x==0: | ||
| continue | ||
| print(f'X: {x}, 1/X: {1/x}') | ||
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| # break | ||
| for x in range(10): | ||
| if x==3: | ||
| break | ||
| print(x) | ||
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| # %% | ||
| # Nested loops | ||
| for x in range(1,4): | ||
| for y in range(1,4): | ||
| print(f'X: {x}, Y: {y}, X*Y: {x*y}') | ||
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| # Nested lists | ||
| for sublist in [[1,2,3],[4,5,6],[7,8,9]]: | ||
| for i in sublist: | ||
| print(i) | ||
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| # np.array | ||
| # Observe that you iterate only over the first dimension when using simple loop | ||
| array = np.random.randint(0,10,100).reshape(10,10) | ||
| print(array.shape) | ||
| for i in array: | ||
| print(i) | ||
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| for row in array: | ||
| for i in row: | ||
| print(i) | ||
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| # %% | ||
| # Looping over pandas DataFrame | ||
| # Observe that | ||
| # - looping with iterrows always gives an index | ||
| # - the second variable will always be a pandas Series object | ||
| import pandas as pd | ||
| df = pd.DataFrame({ | ||
| 'a':[1,2,3], | ||
| 'b':[4,5,6]}) | ||
| print(df) | ||
| for i,row in df.iterrows(): | ||
| print(type(row)) | ||
| value_a = row['a'] | ||
| value_b = row['b'] | ||
| print(f'Row index {i}\nColumn value "a": {value_a} and Column value "b": {value_b}') |
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