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Error when running the analysis on an image with odd width or height #4

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bpavie opened this issue Apr 5, 2023 · 0 comments
Open

Error when running the analysis on an image with odd width or height #4

bpavie opened this issue Apr 5, 2023 · 0 comments

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@bpavie
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bpavie commented Apr 5, 2023 •
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Hi,

I cropped the sample image and had an odd height, and tried to run it but get the following error:

$ python3 hairyroots.py -i sample/TAKFA3-n3-1_Classes_smaller.tif -o sample/ --id_root 3 --id_roothair 1 --id_background 2 --pixel_size 1.72 -p 
usage: hairyroots.py [-h] [-i INPUT_PATH] [-o OUTPUT_PATH] [-p] [--pixel_size PIXEL_SIZE] [--id_root ID_ROOT] [--id_background ID_BACKGROUND] [--id_roothair ID_ROOTHAIR] [--max_dist MAX_DIST] [--thresh_d2r THRESH_DIST_TO_ROOT]
                     [--prune IS_PRUNE] [--bin_op IS_CLOSE_GAPS] [--measure {strain_energy,total_curvature}] [--cost_type {mean,exp,rms,pow3,pow4,geom}] [--n_levels N_LEVELS] [--n_repeats N_REPEATS] [--use_ref_tips] [--no_ref_tips]
                     [--w_curve W_CURVE] [--w_len W_LEN] [--w_mind W_MIND]

Extracts and measures root hairs from classified image.

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT_PATH, --in INPUT_PATH
                        tiff input file
  -o OUTPUT_PATH, --out OUTPUT_PATH
                        csv output filename
  -p, --print           Select to output other data
  --pixel_size PIXEL_SIZE
                        pixel size in microns per pixel
  --id_root ID_ROOT     id of root
  --id_background ID_BACKGROUND
                        id of background
  --id_roothair ID_ROOTHAIR
                        id of root hairs
  --max_dist MAX_DIST   Maximum distance of root hair clusters to root in pixels.
  --thresh_d2r THRESH_DIST_TO_ROOT
                        Minimum distance of root hair to root
  --prune IS_PRUNE      Preprocessing step: Prune medial axis
  --bin_op IS_CLOSE_GAPS
                        Preprocessing step: Binary opening/closing
  --measure {strain_energy,total_curvature}
                        Type of curvature measure
  --cost_type {mean,exp,rms,pow3,pow4,geom}
                        Way to summarize optimization objectives.
  --n_levels N_LEVELS   Minimum number of iteration levels for optimization.
  --n_repeats N_REPEATS
                        Number of times simulated annealing is repeated.
  --use_ref_tips        Treat tips separately from non-tips for referance values.
  --no_ref_tips         Do not treat tips separately from non-tips for referance values.
  --w_curve W_CURVE     Weight for curvature optimzation.
  --w_len W_LEN         Weight for length optimzation.
  --w_mind W_MIND       Weight for minimum distance to root optimzation.
usage: hairyroots.py [-h] [-i INPUT_PATH] [-o OUTPUT_PATH] [-p] [--pixel_size PIXEL_SIZE] [--id_root ID_ROOT] [--id_background ID_BACKGROUND] [--id_roothair ID_ROOTHAIR] [--max_dist MAX_DIST] [--thresh_d2r THRESH_DIST_TO_ROOT]
                     [--prune IS_PRUNE] [--bin_op IS_CLOSE_GAPS] [--measure {strain_energy,total_curvature}] [--cost_type {mean,exp,rms,pow3,pow4,geom}] [--n_levels N_LEVELS] [--n_repeats N_REPEATS] [--use_ref_tips] [--no_ref_tips]
                     [--w_curve W_CURVE] [--w_len W_LEN] [--w_mind W_MIND]
Namespace(input_path='sample/TAKFA3-n3-1_Classes_smaller.tif', output_path='sample/', print_all=True, pixel_size=1.72, id_root=3, id_background=2, id_roothair=1, max_dist=10, thresh_dist_to_root=10, is_prune=True, is_close_gaps=True, measure='total_curvature', cost_type='rms', n_levels=1000, n_repeats=1, use_ref_tips=True, w_curve=1.0, w_len=1.0, w_mind=1.0, func=<function run_pipeline at 0x14614fab9160>)
memory use: 0.1386
Elapsed time: 00:00:00
memory use: 0.1578
Elapsed time: 00:00:00
Traceback (most recent call last):
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/hairyroots.py", line 579, in <module>
    main()
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/hairyroots.py", line 574, in main
    args.func(args)
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/hairyroots.py", line 84, in run_pipeline
    ma, ma_dist, dist_to_root, data = prep.run(data)
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/preprocessing.py", line 37, in run
    classes = self.removeFarRootHairs(classes, max_distance=self.max_distance, id_root=self.id_root, id_background=self.id_background, id_roothair=self.id_roothair)
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/preprocessing.py", line 122, in removeFarRootHairs
    edge = self.find_edge(img, id_root)       # pixel coordinates of edge of main root
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/preprocessing.py", line 292, in find_edge
    n_neighbours_edge = segmentation.Segmentation.numOfNeighbours(edge_loc, arr)
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/segmentation.py", line 86, in numOfNeighbours
    nneighbours = rank.sum(arrBinary, a)            # sum of neighbouring pixels
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/dirtmu_env/lib/python3.9/site-packages/skimage/_shared/utils.py", line 282, in fixed_func
    return func(*args, **kwargs)
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/dirtmu_env/lib/python3.9/site-packages/skimage/filters/rank/generic.py", line 1068, in sum
    return _apply_scalar_per_pixel_3D(generic_cy._sum_3D, image,
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/dirtmu_env/lib/python3.9/site-packages/skimage/filters/rank/generic.py", line 276, in _apply_scalar_per_pixel_3D
    image, footprint, out, mask, n_bins = _handle_input_3D(
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/dirtmu_env/lib/python3.9/site-packages/skimage/filters/rank/generic.py", line 198, in _handle_input_3D
    raise ValueError('Image dimensions and neighborhood dimensions'
ValueError: Image dimensions and neighborhood dimensions do not match

When I re-crop the image to none odd width/height, it works, but I get a different error at the end of the analysis:


$ python3 hairyroots.py -i sample/TAKFA3-n3-1_Classes_smaller.tif -o sample/ --id_root 3 --id_roothair 1 --id_background 2 --pixel_size 1.72 -p 
usage: hairyroots.py [-h] [-i INPUT_PATH] [-o OUTPUT_PATH] [-p] [--pixel_size PIXEL_SIZE] [--id_root ID_ROOT] [--id_background ID_BACKGROUND] [--id_roothair ID_ROOTHAIR] [--max_dist MAX_DIST] [--thresh_d2r THRESH_DIST_TO_ROOT]
                     [--prune IS_PRUNE] [--bin_op IS_CLOSE_GAPS] [--measure {strain_energy,total_curvature}] [--cost_type {mean,exp,rms,pow3,pow4,geom}] [--n_levels N_LEVELS] [--n_repeats N_REPEATS] [--use_ref_tips] [--no_ref_tips]
                     [--w_curve W_CURVE] [--w_len W_LEN] [--w_mind W_MIND]

Extracts and measures root hairs from classified image.

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT_PATH, --in INPUT_PATH
                        tiff input file
  -o OUTPUT_PATH, --out OUTPUT_PATH
                        csv output filename
  -p, --print           Select to output other data
  --pixel_size PIXEL_SIZE
                        pixel size in microns per pixel
  --id_root ID_ROOT     id of root
  --id_background ID_BACKGROUND
                        id of background
  --id_roothair ID_ROOTHAIR
                        id of root hairs
  --max_dist MAX_DIST   Maximum distance of root hair clusters to root in pixels.
  --thresh_d2r THRESH_DIST_TO_ROOT
                        Minimum distance of root hair to root
  --prune IS_PRUNE      Preprocessing step: Prune medial axis
  --bin_op IS_CLOSE_GAPS
                        Preprocessing step: Binary opening/closing
  --measure {strain_energy,total_curvature}
                        Type of curvature measure
  --cost_type {mean,exp,rms,pow3,pow4,geom}
                        Way to summarize optimization objectives.
  --n_levels N_LEVELS   Minimum number of iteration levels for optimization.
  --n_repeats N_REPEATS
                        Number of times simulated annealing is repeated.
  --use_ref_tips        Treat tips separately from non-tips for referance values.
  --no_ref_tips         Do not treat tips separately from non-tips for referance values.
  --w_curve W_CURVE     Weight for curvature optimzation.
  --w_len W_LEN         Weight for length optimzation.
  --w_mind W_MIND       Weight for minimum distance to root optimzation.
usage: hairyroots.py [-h] [-i INPUT_PATH] [-o OUTPUT_PATH] [-p] [--pixel_size PIXEL_SIZE] [--id_root ID_ROOT] [--id_background ID_BACKGROUND] [--id_roothair ID_ROOTHAIR] [--max_dist MAX_DIST] [--thresh_d2r THRESH_DIST_TO_ROOT]
                     [--prune IS_PRUNE] [--bin_op IS_CLOSE_GAPS] [--measure {strain_energy,total_curvature}] [--cost_type {mean,exp,rms,pow3,pow4,geom}] [--n_levels N_LEVELS] [--n_repeats N_REPEATS] [--use_ref_tips] [--no_ref_tips]
                     [--w_curve W_CURVE] [--w_len W_LEN] [--w_mind W_MIND]
Namespace(input_path='sample/TAKFA3-n3-1_Classes_smaller.tif', output_path='sample/', print_all=True, pixel_size=1.72, id_root=3, id_background=2, id_roothair=1, max_dist=10, thresh_dist_to_root=10, is_prune=True, is_close_gaps=True, measure='total_curvature', cost_type='rms', n_levels=1000, n_repeats=1, use_ref_tips=True, w_curve=1.0, w_len=1.0, w_mind=1.0, func=<function run_pipeline at 0x14ca644db160>)
memory use: 0.1429
Elapsed time: 00:00:00
memory use: 0.1534
Elapsed time: 00:00:00
cleaning up tips...
cleaning up tips...
cleaning up tips...
memory use: 0.2658
Elapsed time: 00:00:31
memory use: 0.3268
Elapsed time: 00:00:14
**************************************************
             Getting candidates                   
      # of connected components:80
**************************************************
Component 0:  3190 nodes
 - 313449 candidates; memory use: 0.5601
Component 1:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 2:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 3:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 4:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 5:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 6:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 7:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 8:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 9:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 10:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 11:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 12:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 13:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 14:  3 nodes
 - 1 candidates; memory use: 0.5604
Component 15:  1133 nodes
 - 96109 candidates; memory use: 0.6331
Component 16:  3 nodes
 - 1 candidates; memory use: 0.6331
Component 17:  3 nodes
 - 1 candidates; memory use: 0.6331
Component 18:  3 nodes
 - 1 candidates; memory use: 0.6331
Component 19:  3 nodes
 - 1 candidates; memory use: 0.6331
Component 20:  3 nodes
 - 1 candidates; memory use: 0.6331
Component 21:  642 nodes
 - 53291 candidates; memory use: 0.6739
Component 22:  3 nodes
 - 1 candidates; memory use: 0.6741
Component 23:  5 nodes
 - 1 candidates; memory use: 0.6741
Component 24:  3 nodes
 - 1 candidates; memory use: 0.6741
Component 25:  3 nodes
 - 1 candidates; memory use: 0.6741
Component 26:  617 nodes
 - 45310 candidates; memory use: 0.7082
Component 27:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 28:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 29:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 30:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 31:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 32:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 33:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 34:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 35:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 36:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 37:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 38:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 39:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 40:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 41:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 42:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 43:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 44:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 45:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 46:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 47:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 48:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 49:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 50:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 51:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 52:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 53:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 54:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 55:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 56:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 57:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 58:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 59:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 60:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 61:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 62:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 63:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 64:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 65:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 66:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 67:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 68:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 69:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 70:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 71:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 72:  1 nodes
 - 0 candidates; memory use: 0.7082
Component 73:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 74:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 75:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 76:  11 nodes
 - 15 candidates; memory use: 0.7082
Component 77:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 78:  3 nodes
 - 1 candidates; memory use: 0.7082
Component 79:  3 nodes
 - 1 candidates; memory use: 0.7082
memory use: 0.7082
Gathering data for dummies...
memory use: 0.7151
Gathering data for candidates...
 - 508248 candidate(s)
 - Candidate 0
 - Candidate 10000
 - Candidate 20000
 - Candidate 30000
 - Candidate 40000
 - Candidate 50000
 - Candidate 60000
 - Candidate 70000
 - Candidate 80000
 - Candidate 90000
 - Candidate 100000
 - Candidate 110000
 - Candidate 120000
 - Candidate 130000
 - Candidate 140000
 - Candidate 150000
 - Candidate 160000
 - Candidate 170000
 - Candidate 180000
 - Candidate 190000
 - Candidate 200000
 - Candidate 210000
 - Candidate 220000
 - Candidate 230000
 - Candidate 240000
 - Candidate 250000
 - Candidate 260000
 - Candidate 270000
 - Candidate 280000
 - Candidate 290000
 - Candidate 300000
 - Candidate 310000
 - Candidate 320000
 - Candidate 330000
 - Candidate 340000
 - Candidate 350000
 - Candidate 360000
 - Candidate 370000
 - Candidate 380000
 - Candidate 390000
 - Candidate 400000
 - Candidate 410000
 - Candidate 420000
 - Candidate 430000
 - Candidate 440000
 - Candidate 450000
 - Candidate 460000
 - Candidate 470000
 - Candidate 480000
 - Candidate 490000
 - Candidate 500000
Traceback (most recent call last):
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/hairyroots.py", line 579, in <module>
    main()
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/hairyroots.py", line 574, in main
    args.func(args)
  File "/dodrio/scratch/projects/starting_2023_001/bpavie/DIRTmu/hairyroots.py", line 207, in run_pipeline
    good_candidates = np.array(all_candidates)[candidate_filter] #[all_candidates[i] for i in candidate_filter]
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (508248,) + inhomogeneous part.

NB:
I created the python conda env using this yaml file on linux, which seems to install create a correct conda env to run DIRT/mu

name: DIRTmu_env_py3
channels:
  - conda-forge
  - defaults
  - conda-forge/label/cf202003
dependencies:
  - python=3.9.10
  - graph-tool
  - pip
  - scipy=1.8.0
  - scikit-image=0.19.2
  - scikit-learn=1.0.2
  - matplotlib=3.5.1
  - matplotlib-base=3.5.1
  - psutil=5.9.0
  - pandas=1.4.1
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