├── makefile ├── logo.png ├── requirements.txt ├── image-examples ├── 63001.jpg ├── 63002.jpg ├── 63003.jpg ├── 63004.jpg ├── 63005.jpg ├── 63006.jpg ├── 63007.jpg ├── 63008.jpg ├── 63009.jpg ├── 63010.jpg ├── 63011.jpg ├── 63012.jpg ├── 63014.jpg ├── 63015.jpg ├── 63016.jpg ├── 63017.jpg ├── 63019.jpg ├── 63020.jpg ├── 63021.jpg ├── 63022.jpg ├── 63023.jpg ├── 63024.jpg ├── 63025.jpg ├── 63026.jpg ├── 63027.jpg ├── 63029.jpg ├── 63030.jpg ├── 63031.jpg ├── 63032.jpg ├── 63033.jpg ├── 63034.jpg ├── 63035.jpg ├── 63036.jpg ├── 63037.jpg ├── 63038.jpg ├── 63039.jpg ├── 63042.jpg ├── 63043.jpg ├── 63045.jpg ├── 63046.jpg ├── 63048.jpg ├── 63049.jpg ├── 63050.jpg ├── 63051.jpg ├── 63052.jpg ├── 63054.jpg ├── 63056.jpg ├── 63057.jpg ├── 63058.jpg ├── 63061.jpg ├── 63062.jpg ├── 63063.jpg ├── 63064.jpg ├── 63065.jpg ├── 63067.jpg ├── 63069.jpg ├── 63070.jpg └── 63071.jpg ├── license-images ├── shape_tester.py ├── detector_tester.py ├── preprocessing.py ├── prediction.py ├── .gitignore ├── detector_trainer.py ├── shape_trainer.py ├── landmark-examples ├── csv-example.csv └── tps-example.tps ├── README.md ├── utils.py └── LICENSE /makefile: -------------------------------------------------------------------------------- 1 | all: 2 | pip3 install -r requirements.txt; 3 | -------------------------------------------------------------------------------- /logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/agporto/ml-morph/HEAD/logo.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas>=0.22.0 2 | numpy>=1.13.3 3 | opencv-python>=3.4.0.12 4 | dlib>=19.7.0 5 | -------------------------------------------------------------------------------- /image-examples/63001.jpg: -------------------------------------------------------------------------------- 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Attribution-NonCommercial-ShareAlike 3.0 United States License. 3 | To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/us/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 4 | 5 | 6 | -------------------------------------------------------------------------------- /shape_tester.py: -------------------------------------------------------------------------------- 1 | # Part of the standard library 2 | import os 3 | import sys 4 | import glob 5 | import argparse 6 | # Not part of the standard library 7 | import dlib 8 | 9 | #Parsing arguments 10 | ap = argparse.ArgumentParser() 11 | ap.add_argument("-t", "--test", type=str, default='test.xml', 12 | help="test data: xml filename", metavar='') 13 | ap.add_argument("-p", "--predictor", type=str, default='predictor.dat', 14 | help="trained shape predictor", metavar='') 15 | 16 | args = vars(ap.parse_args()) 17 | 18 | 19 | test_path = os.path.join('./', args['test']) 20 | print("Testing error (mean pixel deviation): {}".format( 21 | dlib.test_shape_predictor(test_path, args['predictor']))) -------------------------------------------------------------------------------- /detector_tester.py: -------------------------------------------------------------------------------- 1 | # Part of the standard library 2 | import os 3 | import sys 4 | import glob 5 | import argparse 6 | # Not part of the standard library 7 | import dlib 8 | 9 | #Parsing arguments 10 | ap = argparse.ArgumentParser() 11 | ap.add_argument("-t", "--test", type=str, default='test.xml', 12 | help="test data (default=test.xml)", metavar='') 13 | ap.add_argument("-d", "--detector", type=str, default='detector.svm', 14 | help="trained object detector (default=detector.svm)", metavar='') 15 | 16 | args = vars(ap.parse_args()) 17 | 18 | 19 | test_path = os.path.join('./', args['test']) 20 | print("Testing - {}".format( 21 | dlib.test_simple_object_detector(test_path, args['detector']))) 22 | -------------------------------------------------------------------------------- /preprocessing.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import utils 4 | 5 | 6 | ap = argparse.ArgumentParser() 7 | ap.add_argument('-i','--input-dir', type=str, default='images', help="input directory containing image files (default = images)", metavar='') 8 | ap.add_argument('-c','--csv-file', type=str, default=None, help="(optional) XY coordinate file in csv format", metavar='') 9 | ap.add_argument('-t','--tps-file', type=str, default=None, help="(optional) tps coordinate file", metavar='') 10 | 11 | 12 | 13 | args = vars(ap.parse_args()) 14 | 15 | assert os.path.isdir(args['input_dir']), "Could not find the folder {}".format(args['input_dir']) 16 | 17 | file_sizes=utils.split_train_test(args['input_dir']) 18 | 19 | if args['csv_file'] is not None: 20 | dict_csv=utils.read_csv(args['csv_file']) 21 | utils.generate_dlib_xml(dict_csv,file_sizes['train'],folder='train',out_file='train.xml') 22 | utils.generate_dlib_xml(dict_csv,file_sizes['test'],folder='test',out_file='test.xml') 23 | 24 | if args['tps_file'] is not None: 25 | dict_tps=utils.read_tps(args['tps_file']) 26 | utils.generate_dlib_xml(dict_tps,file_sizes['train'],folder='train',out_file='train.xml') 27 | utils.generate_dlib_xml(dict_tps,file_sizes['test'],folder='test',out_file='test.xml') 28 | -------------------------------------------------------------------------------- /prediction.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import utils 4 | 5 | 6 | ap = argparse.ArgumentParser() 7 | ap.add_argument('-i','--input-dir', type=str, default='pred', help="input directory (default = pred)", metavar='') 8 | ap.add_argument('-d','--detector', type=str, default='detector.svm', help="trained object detection model (default = detector.svm)", metavar='') 9 | ap.add_argument('-p','--predictor', type=str, default='predictor.dat', help="trained shape prediction model (default = predictor.dat)", metavar='') 10 | ap.add_argument('-o','--out-file', type=str, default='output.xml', help="output file name (default = output.xml)", metavar='') 11 | ap.add_argument('-u','--upsample-limit', type=int, default=0, help="upsample limit (default= 0 ; max = 2)", metavar='') 12 | ap.add_argument('-t','--threshold', type=float, default=0, help="detector's confidence threshold for outputting an object (default= 0)", metavar='') 13 | ap.add_argument('-l','--ignore-list', nargs='*', type=int, default=None, help=" (optional) prevents landmarks of choice from being output", metavar='') 14 | 15 | 16 | 17 | 18 | 19 | 20 | args = vars(ap.parse_args()) 21 | 22 | utils.predictions_to_xml(args['detector'],args['predictor'], dir=args['input_dir'],upsample=args['upsample_limit'],threshold=args['threshold'],ignore=args['ignore_list'],out_file=args['out_file']) 23 | 24 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /detector_trainer.py: -------------------------------------------------------------------------------- 1 | # Part of the standard library 2 | import os 3 | import sys 4 | import glob 5 | import argparse 6 | # Not part of the standard library 7 | import dlib 8 | 9 | #Parsing arguments 10 | ap = argparse.ArgumentParser() 11 | ap.add_argument("-d", "--dataset", type=str, default='train.xml', 12 | help="training data (default = train.xml)", metavar='') 13 | ap.add_argument("-t", "--test", type=str, default=None, 14 | help="(optional) test data. if not provided, the model is not tested", metavar='') 15 | ap.add_argument("-o", "--out", type=str, default='detector', 16 | help="output filename (default = detector)", metavar='') 17 | ap.add_argument("-n", "--n-threads", type=int, default=1, 18 | help="number of threads to be used (default = 1)", metavar='') 19 | ap.add_argument("-s", "--symmetrical", type=bool, default=False, 20 | help="(True/False) indicating whether objects are bilaterally symmetrical (default = False)", metavar='') 21 | ap.add_argument("-e", "--epsilon", type=float, default=0.01, 22 | help="insensitivity parameter (default = 0.01)", metavar='') 23 | ap.add_argument("-c", "--c-param", type=float, default=5, 24 | help="soft margin parameter C (default =5)", metavar='') 25 | ap.add_argument("-u", "--upsample", type=int, default=0, 26 | help="upsample limit (default = 0)", metavar='') 27 | ap.add_argument("-w", "--window-size", type=int, default=None, 28 | help="(optional) detection window size", metavar='') 29 | args = vars(ap.parse_args()) 30 | 31 | #Setting up the training parameters 32 | options = dlib.simple_object_detector_training_options() 33 | options.add_left_right_image_flips = args['symmetrical'] 34 | options.C = args['c_param'] 35 | options.num_threads = args['n_threads'] 36 | options.be_verbose = True 37 | options.epsilon=args['epsilon'] 38 | options.upsample_limit=args['upsample'] 39 | if args['window_size'] is not None: 40 | options.detection_window_size=args['window_size'] 41 | 42 | #Training the model 43 | train_path = os.path.join('./', args['dataset']) 44 | dlib.train_simple_object_detector(train_path, args['out']+".svm", options) 45 | print("Training - {}".format( 46 | dlib.test_simple_object_detector(train_path, args['out']+".svm"))) 47 | 48 | #Testing the model (if test data was provided) 49 | if args['test'] is not None: 50 | test_path = os.path.join('./', args['test']) 51 | print("Testing - {}".format( 52 | dlib.test_simple_object_detector(test_path, args['out']+".svm"))) 53 | -------------------------------------------------------------------------------- /shape_trainer.py: -------------------------------------------------------------------------------- 1 | # Part of the standard library 2 | import os 3 | import sys 4 | import glob 5 | import argparse 6 | # Not part of the standard library 7 | import dlib 8 | 9 | #Parsing arguments 10 | ap = argparse.ArgumentParser() 11 | ap.add_argument("-d", "--dataset", type=str, default='train.xml', 12 | help="training data (default = train.xml)", metavar='') 13 | ap.add_argument("-t", "--test", type=str, default=None, 14 | help="test data (default = test.xml).if not provided, no testing is done", metavar='') 15 | ap.add_argument("-o", "--out", type=str, default='predictor', 16 | help="output filename (default = predictor)", metavar='') 17 | ap.add_argument("-th", "--threads", type=int, default=1, 18 | help="number of threads to be used (default = 1)", metavar='') 19 | ap.add_argument("-dp", "--tree-depth", type=int, default=4, 20 | help="choice of tree depth (default = 4)", metavar='') 21 | ap.add_argument("-c", "--cascade-depth", type=int, default=15, 22 | help="choice of cascade depth (default = 15)", metavar='') 23 | ap.add_argument("-nu", "--nu", type=float, default=0.1, 24 | help="regularization parameter (default = 0.1)", metavar='') 25 | ap.add_argument("-os", "--oversampling", type=int, default=10, 26 | help="oversampling amount (default = 10)", metavar='') 27 | ap.add_argument("-s", "--test-splits", type=int, default=20, 28 | help="number of test splits (default = 20)", metavar='') 29 | ap.add_argument("-f", "--feature-pool-size", type=int, default=500, 30 | help="choice of feature pool size (default = 500)", metavar='') 31 | ap.add_argument("-n", "--num-trees", type=int, default=500, 32 | help="number of regression trees (default = 500)", metavar='') 33 | args = vars(ap.parse_args()) 34 | 35 | #Setting up the training parameters 36 | options = dlib.shape_predictor_training_options() 37 | options.num_trees_per_cascade_level=args['num_trees'] 38 | options.nu = args['nu'] 39 | options.num_threads=args['threads'] 40 | options.tree_depth = args['tree_depth'] 41 | options.cascade_depth = args['cascade_depth'] 42 | options.feature_pool_size = args['feature_pool_size'] 43 | options.num_test_splits = args['test_splits'] 44 | options.oversampling_amount = args['oversampling'] 45 | options.be_verbose = True 46 | 47 | #Training the model 48 | train_path = os.path.join('./', args['dataset']) 49 | dlib.train_shape_predictor(train_path, args['out']+".dat", options) 50 | print("Training error (average pixel deviation): {}".format( 51 | dlib.test_shape_predictor(train_path, args['out']+".dat"))) 52 | 53 | #Testing the model (if test data was provided) 54 | if args['test'] is not None: 55 | test_path = os.path.join('./', args['test']) 56 | print("Testing error (average pixel deviation): {}".format( 57 | dlib.test_shape_predictor(test_path, args['out']+".dat"))) -------------------------------------------------------------------------------- /landmark-examples/csv-example.csv: -------------------------------------------------------------------------------- 1 | id,X1,Y1,X2,Y2,X3,Y3,X4,Y4,X5,Y5,X6,Y6,X7,Y7,X8,Y8,X9,Y9,X10,Y10,X11,Y11,X12,Y12 2 | 63001.jpg,290,108,100,179,73,229,158,320,476,310,572,301,352,169,345,211,445,242,441,257,510,269,534,230 3 | 63002.jpg,284,76,55,156,24,210,120,313,487,323,602,324,353,157,343,203,454,246,447,261,529,279,560,238 4 | 63003.jpg,278,64,51,159,30,208,124,308,494,301,608,297,351,138,348,188,457,225,452,244,539,258,560,211 5 | 63004.jpg,283,152,68,236,46,284,136,387,488,392,597,393,356,229,344,276,453,316,448,332,534,348,558,311 6 | 63005.jpg,269,123,59,216,36,268,129,365,481,353,601,346,334,193,320,242,435,276,432,294,520,304,549,263 7 | 63006.jpg,246,129,37,219,19,270,107,363,460,357,574,350,316,196,310,246,424,281,417,296,506,308,530,266 8 | 63007.jpg,257,107,46,194,23,243,112,339,464,336,582,335,325,177,319,226,428,261,426,278,514,291,535,250 9 | 63008.jpg,275,126,94,204,73,250,157,342,468,330,563,324,332,191,326,232,431,264,428,279,502,288,523,251 10 | 63009.jpg,276,114,94,187,73,235,154,322,461,316,566,308,342,180,334,219,434,249,430,263,495,275,517,238 11 | 63010.jpg,282,130,106,219,87,268,177,356,484,321,585,307,349,192,346,231,445,256,441,271,519,277,537,234 12 | 63011.jpg,274,113,101,189,79,239,165,327,469,317,567,305,344,179,336,217,438,251,436,265,509,272,526,232 13 | 63012.jpg,276,135,93,216,72,266,171,355,469,330,567,323,343,202,336,239,440,267,436,280,504,289,527,251 14 | 63014.jpg,248,118,31,214,10,265,119,362,472,341,589,327,317,190,305,234,431,269,427,284,507,293,539,248 15 | 63015.jpg,250,111,36,209,15,263,115,357,488,331,606,316,323,178,319,227,448,257,446,275,527,280,558,237 16 | 63016.jpg,248,103,33,207,14,256,117,352,467,332,585,321,316,173,310,225,426,253,421,271,502,284,533,237 17 | 63017.jpg,248,105,40,194,16,248,109,340,468,329,576,327,321,173,310,219,431,255,425,274,512,282,537,240 18 | 63019.jpg,284,144,104,220,77,275,169,361,473,342,572,330,349,209,344,247,443,275,437,291,507,298,529,265 19 | 63020.jpg,299,140,114,217,90,264,180,355,478,346,573,341,360,207,349,249,447,280,441,293,510,306,530,273 20 | 63021.jpg,281,137,94,213,70,259,157,352,465,343,565,340,347,203,336,241,438,276,433,291,506,303,528,265 21 | 63022.jpg,281,135,102,216,81,271,169,359,474,330,570,320,353,202,345,240,436,265,432,283,502,289,526,250 22 | 63023.jpg,287,128,103,203,77,258,163,348,480,334,584,332,349,192,342,235,439,267,434,284,509,295,538,253 23 | 63024.jpg,273,123,84,205,64,256,153,345,465,330,569,322,340,189,332,229,430,260,426,274,495,284,524,244 24 | 63025.jpg,253,108,39,204,19,260,122,358,482,332,597,326,321,175,318,224,436,260,430,279,514,287,545,237 25 | 63026.jpg,251,106,36,197,13,253,112,352,472,336,585,329,315,173,308,223,425,258,420,277,500,284,533,239 26 | 63027.jpg,262,95,36,187,15,245,115,344,484,332,600,322,325,164,318,219,442,254,436,272,519,281,551,236 27 | 63029.jpg,251,87,46,175,19,226,113,315,465,314,575,305,321,162,312,202,424,240,421,255,499,267,531,221 28 | 63030.jpg,254,113,38,201,16,250,114,345,461,348,571,341,323,186,316,230,420,270,418,284,498,300,529,260 29 | 63031.jpg,294,123,114,201,92,246,182,329,476,314,577,298,348,180,344,218,444,246,440,260,506,266,530,229 30 | 63032.jpg,284,119,99,197,80,246,164,339,469,328,566,317,352,190,342,226,438,259,435,273,503,286,529,248 31 | 63033.jpg,315,110,130,183,104,233,192,319,496,305,594,298,369,172,366,207,464,240,461,256,523,265,553,227 32 | 63034.jpg,286,108,100,189,78,230,181,319,476,296,569,287,347,170,342,207,436,234,435,250,498,256,527,222 33 | 63035.jpg,288,125,103,212,81,255,191,342,472,312,566,299,352,184,347,222,442,246,440,261,498,269,526,227 34 | 63036.jpg,314,146,141,228,128,271,233,347,489,325,581,312,374,202,367,237,454,264,453,278,520,284,542,247 35 | 63037.jpg,243,138,31,256,17,311,123,393,480,339,589,317,316,200,316,247,427,269,422,286,516,287,538,244 36 | 63038.jpg,270,91,37,197,19,246,128,339,490,312,594,305,335,162,328,210,442,240,440,259,521,268,552,222 37 | 63039.jpg,263,117,36,219,20,273,127,360,486,336,596,329,332,183,329,230,443,264,440,280,525,290,556,251 38 | 63042.jpg,259,97,33,193,12,243,119,335,477,323,588,320,322,161,311,210,431,247,426,264,512,277,544,230 39 | 63043.jpg,290,113,101,191,78,246,162,337,476,320,574,310,349,180,343,223,438,252,433,267,504,277,532,236 40 | 63045.jpg,257,96,42,187,19,241,120,339,472,321,581,319,317,160,311,216,421,246,418,263,504,274,532,233 41 | 63046.jpg,254,114,44,206,21,257,109,351,480,350,595,347,321,188,316,236,443,273,436,291,521,304,548,260 42 | 63048.jpg,244,105,32,201,15,255,110,346,466,327,573,317,312,172,310,220,422,252,418,269,503,280,524,234 43 | 63049.jpg,282,104,94,184,71,232,156,322,471,317,571,311,343,174,338,211,442,247,438,262,509,274,530,234 44 | 63050.jpg,279,122,94,200,74,244,168,337,470,323,565,313,342,183,331,222,442,254,439,268,511,279,524,238 45 | 63051.jpg,283,137,108,214,88,257,173,350,470,335,566,328,347,199,339,236,434,269,432,284,505,292,525,255 46 | 63052.jpg,264,136,94,214,73,261,154,350,463,326,559,315,333,198,332,237,426,263,424,277,498,283,514,245 47 | 63054.jpg,279,131,93,201,71,247,155,342,465,332,562,327,345,196,338,236,431,263,428,280,498,293,523,254 48 | 63056.jpg,247,107,36,199,12,248,106,344,471,329,572,324,315,174,311,224,424,256,420,274,504,282,531,239 49 | 63057.jpg,263,111,46,202,19,251,113,355,474,343,579,337,321,181,310,233,427,268,424,285,520,297,536,251 50 | 63058.jpg,248,86,44,174,17,222,106,316,479,308,596,304,323,154,319,198,432,234,427,250,519,264,548,219 51 | 63061.jpg,298,129,121,213,101,264,188,342,480,319,574,308,355,189,350,226,451,254,447,267,512,275,534,238 52 | 63062.jpg,270,121,93,202,71,248,151,341,465,325,563,316,336,188,332,228,432,259,430,273,499,284,522,245 53 | 63063.jpg,276,118,88,202,68,247,162,339,474,323,571,311,340,183,334,222,440,251,435,267,505,276,530,240 54 | 63064.jpg,310,114,115,198,92,251,190,340,493,322,592,316,364,181,355,222,462,254,459,270,526,279,552,242 55 | 63065.jpg,282,108,89,189,70,246,171,334,465,302,568,293,337,168,333,208,433,236,431,251,502,260,527,220 56 | 63067.jpg,274,116,47,219,27,275,137,368,491,339,607,334,332,180,325,234,455,266,450,285,535,293,563,250 57 | 63069.jpg,258,89,35,180,11,235,119,334,469,320,582,314,321,154,312,207,435,242,432,259,518,273,542,231 58 | 63070.jpg,252,98,42,188,21,237,113,337,466,327,580,326,311,165,310,218,424,251,420,270,512,282,535,241 59 | 63071.jpg,257,90,39,187,15,243,115,339,477,324,583,319,322,159,322,210,439,245,436,262,514,276,542,231 60 | -------------------------------------------------------------------------------- /landmark-examples/tps-example.tps: -------------------------------------------------------------------------------- 1 | LM=12 2 | 290. 108. 3 | 100. 179. 4 | 73. 229. 5 | 158. 320. 6 | 476. 310. 7 | 572. 301. 8 | 352. 169. 9 | 345. 211. 10 | 445. 242. 11 | 441. 257. 12 | 510. 269. 13 | 534. 230. 14 | IMAGE=63001.jpg 15 | ID=0 16 | LM=12 17 | 284. 76. 18 | 55. 156. 19 | 24. 210. 20 | 120. 313. 21 | 487. 323. 22 | 602. 324. 23 | 353. 157. 24 | 343. 203. 25 | 454. 246. 26 | 447. 261. 27 | 529. 279. 28 | 560. 238. 29 | IMAGE=63002.jpg 30 | ID=1 31 | LM=12 32 | 278. 64. 33 | 51. 159. 34 | 30. 208. 35 | 124. 308. 36 | 494. 301. 37 | 608. 297. 38 | 351. 138. 39 | 348. 188. 40 | 457. 225. 41 | 452. 244. 42 | 539. 258. 43 | 560. 211. 44 | IMAGE=63003.jpg 45 | ID=2 46 | LM=12 47 | 283. 152. 48 | 68. 236. 49 | 46. 284. 50 | 136. 387. 51 | 488. 392. 52 | 597. 393. 53 | 356. 229. 54 | 344. 276. 55 | 453. 316. 56 | 448. 332. 57 | 534. 348. 58 | 558. 311. 59 | IMAGE=63004.jpg 60 | ID=3 61 | LM=12 62 | 269. 123. 63 | 59. 216. 64 | 36. 268. 65 | 129. 365. 66 | 481. 353. 67 | 601. 346. 68 | 334. 193. 69 | 320. 242. 70 | 435. 276. 71 | 432. 294. 72 | 520. 304. 73 | 549. 263. 74 | IMAGE=63005.jpg 75 | ID=4 76 | LM=12 77 | 246. 129. 78 | 37. 219. 79 | 19. 270. 80 | 107. 363. 81 | 460. 357. 82 | 574. 350. 83 | 316. 196. 84 | 310. 246. 85 | 424. 281. 86 | 417. 296. 87 | 506. 308. 88 | 530. 266. 89 | IMAGE=63006.jpg 90 | ID=5 91 | LM=12 92 | 257. 107. 93 | 46. 194. 94 | 23. 243. 95 | 112. 339. 96 | 464. 336. 97 | 582. 335. 98 | 325. 177. 99 | 319. 226. 100 | 428. 261. 101 | 426. 278. 102 | 514. 291. 103 | 535. 250. 104 | IMAGE=63007.jpg 105 | ID=6 106 | LM=12 107 | 275. 126. 108 | 94. 204. 109 | 73. 250. 110 | 157. 342. 111 | 468. 330. 112 | 563. 324. 113 | 332. 191. 114 | 326. 232. 115 | 431. 264. 116 | 428. 279. 117 | 502. 288. 118 | 523. 251. 119 | IMAGE=63008.jpg 120 | ID=7 121 | LM=12 122 | 276. 114. 123 | 94. 187. 124 | 73. 235. 125 | 154. 322. 126 | 461. 316. 127 | 566. 308. 128 | 342. 180. 129 | 334. 219. 130 | 434. 249. 131 | 430. 263. 132 | 495. 275. 133 | 517. 238. 134 | IMAGE=63009.jpg 135 | ID=8 136 | LM=12 137 | 282. 130. 138 | 106. 219. 139 | 87. 268. 140 | 177. 356. 141 | 484. 321. 142 | 585. 307. 143 | 349. 192. 144 | 346. 231. 145 | 445. 256. 146 | 441. 271. 147 | 519. 277. 148 | 537. 234. 149 | IMAGE=63010.jpg 150 | ID=9 151 | LM=12 152 | 274. 113. 153 | 101. 189. 154 | 79. 239. 155 | 165. 327. 156 | 469. 317. 157 | 567. 305. 158 | 344. 179. 159 | 336. 217. 160 | 438. 251. 161 | 436. 265. 162 | 509. 272. 163 | 526. 232. 164 | IMAGE=63011.jpg 165 | ID=10 166 | LM=12 167 | 276. 135. 168 | 93. 216. 169 | 72. 266. 170 | 171. 355. 171 | 469. 330. 172 | 567. 323. 173 | 343. 202. 174 | 336. 239. 175 | 440. 267. 176 | 436. 280. 177 | 504. 289. 178 | 527. 251. 179 | IMAGE=63012.jpg 180 | ID=11 181 | LM=12 182 | 248. 118. 183 | 31. 214. 184 | 10. 265. 185 | 119. 362. 186 | 472. 341. 187 | 589. 327. 188 | 317. 190. 189 | 305. 234. 190 | 431. 269. 191 | 427. 284. 192 | 507. 293. 193 | 539. 248. 194 | IMAGE=63014.jpg 195 | ID=12 196 | LM=12 197 | 250. 111. 198 | 36. 209. 199 | 15. 263. 200 | 115. 357. 201 | 488. 331. 202 | 606. 316. 203 | 323. 178. 204 | 319. 227. 205 | 448. 257. 206 | 446. 275. 207 | 527. 280. 208 | 558. 237. 209 | IMAGE=63015.jpg 210 | ID=13 211 | LM=12 212 | 248. 103. 213 | 33. 207. 214 | 14. 256. 215 | 117. 352. 216 | 467. 332. 217 | 585. 321. 218 | 316. 173. 219 | 310. 225. 220 | 426. 253. 221 | 421. 271. 222 | 502. 284. 223 | 533. 237. 224 | IMAGE=63016.jpg 225 | ID=14 226 | LM=12 227 | 248. 105. 228 | 40. 194. 229 | 16. 248. 230 | 109. 340. 231 | 468. 329. 232 | 576. 327. 233 | 321. 173. 234 | 310. 219. 235 | 431. 255. 236 | 425. 274. 237 | 512. 282. 238 | 537. 240. 239 | IMAGE=63017.jpg 240 | ID=15 241 | LM=12 242 | 284. 144. 243 | 104. 220. 244 | 77. 275. 245 | 169. 361. 246 | 473. 342. 247 | 572. 330. 248 | 349. 209. 249 | 344. 247. 250 | 443. 275. 251 | 437. 291. 252 | 507. 298. 253 | 529. 265. 254 | IMAGE=63019.jpg 255 | ID=16 256 | LM=12 257 | 299. 140. 258 | 114. 217. 259 | 90. 264. 260 | 180. 355. 261 | 478. 346. 262 | 573. 341. 263 | 360. 207. 264 | 349. 249. 265 | 447. 280. 266 | 441. 293. 267 | 510. 306. 268 | 530. 273. 269 | IMAGE=63020.jpg 270 | ID=17 271 | LM=12 272 | 281. 137. 273 | 94. 213. 274 | 70. 259. 275 | 157. 352. 276 | 465. 343. 277 | 565. 340. 278 | 347. 203. 279 | 336. 241. 280 | 438. 276. 281 | 433. 291. 282 | 506. 303. 283 | 528. 265. 284 | IMAGE=63021.jpg 285 | ID=18 286 | LM=12 287 | 281. 135. 288 | 102. 216. 289 | 81. 271. 290 | 169. 359. 291 | 474. 330. 292 | 570. 320. 293 | 353. 202. 294 | 345. 240. 295 | 436. 265. 296 | 432. 283. 297 | 502. 289. 298 | 526. 250. 299 | IMAGE=63022.jpg 300 | ID=19 301 | LM=12 302 | 287. 128. 303 | 103. 203. 304 | 77. 258. 305 | 163. 348. 306 | 480. 334. 307 | 584. 332. 308 | 349. 192. 309 | 342. 235. 310 | 439. 267. 311 | 434. 284. 312 | 509. 295. 313 | 538. 253. 314 | IMAGE=63023.jpg 315 | ID=20 316 | LM=12 317 | 273. 123. 318 | 84. 205. 319 | 64. 256. 320 | 153. 345. 321 | 465. 330. 322 | 569. 322. 323 | 340. 189. 324 | 332. 229. 325 | 430. 260. 326 | 426. 274. 327 | 495. 284. 328 | 524. 244. 329 | IMAGE=63024.jpg 330 | ID=21 331 | LM=12 332 | 253. 108. 333 | 39. 204. 334 | 19. 260. 335 | 122. 358. 336 | 482. 332. 337 | 597. 326. 338 | 321. 175. 339 | 318. 224. 340 | 436. 260. 341 | 430. 279. 342 | 514. 287. 343 | 545. 237. 344 | IMAGE=63025.jpg 345 | ID=22 346 | LM=12 347 | 251. 106. 348 | 36. 197. 349 | 13. 253. 350 | 112. 352. 351 | 472. 336. 352 | 585. 329. 353 | 315. 173. 354 | 308. 223. 355 | 425. 258. 356 | 420. 277. 357 | 500. 284. 358 | 533. 239. 359 | IMAGE=63026.jpg 360 | ID=23 361 | LM=12 362 | 262. 95. 363 | 36. 187. 364 | 15. 245. 365 | 115. 344. 366 | 484. 332. 367 | 600. 322. 368 | 325. 164. 369 | 318. 219. 370 | 442. 254. 371 | 436. 272. 372 | 519. 281. 373 | 551. 236. 374 | IMAGE=63027.jpg 375 | ID=24 376 | LM=12 377 | 251. 87. 378 | 46. 175. 379 | 19. 226. 380 | 113. 315. 381 | 465. 314. 382 | 575. 305. 383 | 321. 162. 384 | 312. 202. 385 | 424. 240. 386 | 421. 255. 387 | 499. 267. 388 | 531. 221. 389 | IMAGE=63029.jpg 390 | ID=25 391 | LM=12 392 | 254. 113. 393 | 38. 201. 394 | 16. 250. 395 | 114. 345. 396 | 461. 348. 397 | 571. 341. 398 | 323. 186. 399 | 316. 230. 400 | 420. 270. 401 | 418. 284. 402 | 498. 300. 403 | 529. 260. 404 | IMAGE=63030.jpg 405 | ID=26 406 | LM=12 407 | 294. 123. 408 | 114. 201. 409 | 92. 246. 410 | 182. 329. 411 | 476. 314. 412 | 577. 298. 413 | 348. 180. 414 | 344. 218. 415 | 444. 246. 416 | 440. 260. 417 | 506. 266. 418 | 530. 229. 419 | IMAGE=63031.jpg 420 | ID=27 421 | LM=12 422 | 284. 119. 423 | 99. 197. 424 | 80. 246. 425 | 164. 339. 426 | 469. 328. 427 | 566. 317. 428 | 352. 190. 429 | 342. 226. 430 | 438. 259. 431 | 435. 273. 432 | 503. 286. 433 | 529. 248. 434 | IMAGE=63032.jpg 435 | ID=28 436 | LM=12 437 | 315. 110. 438 | 130. 183. 439 | 104. 233. 440 | 192. 319. 441 | 496. 305. 442 | 594. 298. 443 | 369. 172. 444 | 366. 207. 445 | 464. 240. 446 | 461. 256. 447 | 523. 265. 448 | 553. 227. 449 | IMAGE=63033.jpg 450 | ID=29 451 | LM=12 452 | 286. 108. 453 | 100. 189. 454 | 78. 230. 455 | 181. 319. 456 | 476. 296. 457 | 569. 287. 458 | 347. 170. 459 | 342. 207. 460 | 436. 234. 461 | 435. 250. 462 | 498. 256. 463 | 527. 222. 464 | IMAGE=63034.jpg 465 | ID=30 466 | LM=12 467 | 288. 125. 468 | 103. 212. 469 | 81. 255. 470 | 191. 342. 471 | 472. 312. 472 | 566. 299. 473 | 352. 184. 474 | 347. 222. 475 | 442. 246. 476 | 440. 261. 477 | 498. 269. 478 | 526. 227. 479 | IMAGE=63035.jpg 480 | ID=31 481 | LM=12 482 | 314. 146. 483 | 141. 228. 484 | 128. 271. 485 | 233. 347. 486 | 489. 325. 487 | 581. 312. 488 | 374. 202. 489 | 367. 237. 490 | 454. 264. 491 | 453. 278. 492 | 520. 284. 493 | 542. 247. 494 | IMAGE=63036.jpg 495 | ID=32 496 | LM=12 497 | 243. 138. 498 | 31. 256. 499 | 17. 311. 500 | 123. 393. 501 | 480. 339. 502 | 589. 317. 503 | 316. 200. 504 | 316. 247. 505 | 427. 269. 506 | 422. 286. 507 | 516. 287. 508 | 538. 244. 509 | IMAGE=63037.jpg 510 | ID=33 511 | LM=12 512 | 270. 91. 513 | 37. 197. 514 | 19. 246. 515 | 128. 339. 516 | 490. 312. 517 | 594. 305. 518 | 335. 162. 519 | 328. 210. 520 | 442. 240. 521 | 440. 259. 522 | 521. 268. 523 | 552. 222. 524 | IMAGE=63038.jpg 525 | ID=34 526 | LM=12 527 | 263. 117. 528 | 36. 219. 529 | 20. 273. 530 | 127. 360. 531 | 486. 336. 532 | 596. 329. 533 | 332. 183. 534 | 329. 230. 535 | 443. 264. 536 | 440. 280. 537 | 525. 290. 538 | 556. 251. 539 | IMAGE=63039.jpg 540 | ID=35 541 | LM=12 542 | 259. 97. 543 | 33. 193. 544 | 12. 243. 545 | 119. 335. 546 | 477. 323. 547 | 588. 320. 548 | 322. 161. 549 | 311. 210. 550 | 431. 247. 551 | 426. 264. 552 | 512. 277. 553 | 544. 230. 554 | IMAGE=63042.jpg 555 | ID=36 556 | LM=12 557 | 290. 113. 558 | 101. 191. 559 | 78. 246. 560 | 162. 337. 561 | 476. 320. 562 | 574. 310. 563 | 349. 180. 564 | 343. 223. 565 | 438. 252. 566 | 433. 267. 567 | 504. 277. 568 | 532. 236. 569 | IMAGE=63043.jpg 570 | ID=37 571 | LM=12 572 | 257. 96. 573 | 42. 187. 574 | 19. 241. 575 | 120. 339. 576 | 472. 321. 577 | 581. 319. 578 | 317. 160. 579 | 311. 216. 580 | 421. 246. 581 | 418. 263. 582 | 504. 274. 583 | 532. 233. 584 | IMAGE=63045.jpg 585 | ID=38 586 | LM=12 587 | 254. 114. 588 | 44. 206. 589 | 21. 257. 590 | 109. 351. 591 | 480. 350. 592 | 595. 347. 593 | 321. 188. 594 | 316. 236. 595 | 443. 273. 596 | 436. 291. 597 | 521. 304. 598 | 548. 260. 599 | IMAGE=63046.jpg 600 | ID=39 601 | LM=12 602 | 244. 105. 603 | 32. 201. 604 | 15. 255. 605 | 110. 346. 606 | 466. 327. 607 | 573. 317. 608 | 312. 172. 609 | 310. 220. 610 | 422. 252. 611 | 418. 269. 612 | 503. 280. 613 | 524. 234. 614 | IMAGE=63048.jpg 615 | ID=40 616 | LM=12 617 | 282. 104. 618 | 94. 184. 619 | 71. 232. 620 | 156. 322. 621 | 471. 317. 622 | 571. 311. 623 | 343. 174. 624 | 338. 211. 625 | 442. 247. 626 | 438. 262. 627 | 509. 274. 628 | 530. 234. 629 | IMAGE=63049.jpg 630 | ID=41 631 | LM=12 632 | 279. 122. 633 | 94. 200. 634 | 74. 244. 635 | 168. 337. 636 | 470. 323. 637 | 565. 313. 638 | 342. 183. 639 | 331. 222. 640 | 442. 254. 641 | 439. 268. 642 | 511. 279. 643 | 524. 238. 644 | IMAGE=63050.jpg 645 | ID=42 646 | LM=12 647 | 283. 137. 648 | 108. 214. 649 | 88. 257. 650 | 173. 350. 651 | 470. 335. 652 | 566. 328. 653 | 347. 199. 654 | 339. 236. 655 | 434. 269. 656 | 432. 284. 657 | 505. 292. 658 | 525. 255. 659 | IMAGE=63051.jpg 660 | ID=43 661 | LM=12 662 | 264. 136. 663 | 94. 214. 664 | 73. 261. 665 | 154. 350. 666 | 463. 326. 667 | 559. 315. 668 | 333. 198. 669 | 332. 237. 670 | 426. 263. 671 | 424. 277. 672 | 498. 283. 673 | 514. 245. 674 | IMAGE=63052.jpg 675 | ID=44 676 | LM=12 677 | 279. 131. 678 | 93. 201. 679 | 71. 247. 680 | 155. 342. 681 | 465. 332. 682 | 562. 327. 683 | 345. 196. 684 | 338. 236. 685 | 431. 263. 686 | 428. 280. 687 | 498. 293. 688 | 523. 254. 689 | IMAGE=63054.jpg 690 | ID=45 691 | LM=12 692 | 247. 107. 693 | 36. 199. 694 | 12. 248. 695 | 106. 344. 696 | 471. 329. 697 | 572. 324. 698 | 315. 174. 699 | 311. 224. 700 | 424. 256. 701 | 420. 274. 702 | 504. 282. 703 | 531. 239. 704 | IMAGE=63056.jpg 705 | ID=46 706 | LM=12 707 | 263. 111. 708 | 46. 202. 709 | 19. 251. 710 | 113. 355. 711 | 474. 343. 712 | 579. 337. 713 | 321. 181. 714 | 310. 233. 715 | 427. 268. 716 | 424. 285. 717 | 520. 297. 718 | 536. 251. 719 | IMAGE=63057.jpg 720 | ID=47 721 | LM=12 722 | 248. 86. 723 | 44. 174. 724 | 17. 222. 725 | 106. 316. 726 | 479. 308. 727 | 596. 304. 728 | 323. 154. 729 | 319. 198. 730 | 432. 234. 731 | 427. 250. 732 | 519. 264. 733 | 548. 219. 734 | IMAGE=63058.jpg 735 | ID=48 736 | LM=12 737 | 298. 129. 738 | 121. 213. 739 | 101. 264. 740 | 188. 342. 741 | 480. 319. 742 | 574. 308. 743 | 355. 189. 744 | 350. 226. 745 | 451. 254. 746 | 447. 267. 747 | 512. 275. 748 | 534. 238. 749 | IMAGE=63061.jpg 750 | ID=49 751 | LM=12 752 | 270. 121. 753 | 93. 202. 754 | 71. 248. 755 | 151. 341. 756 | 465. 325. 757 | 563. 316. 758 | 336. 188. 759 | 332. 228. 760 | 432. 259. 761 | 430. 273. 762 | 499. 284. 763 | 522. 245. 764 | IMAGE=63062.jpg 765 | ID=50 766 | LM=12 767 | 276. 118. 768 | 88. 202. 769 | 68. 247. 770 | 162. 339. 771 | 474. 323. 772 | 571. 311. 773 | 340. 183. 774 | 334. 222. 775 | 440. 251. 776 | 435. 267. 777 | 505. 276. 778 | 530. 240. 779 | IMAGE=63063.jpg 780 | ID=51 781 | LM=12 782 | 310. 114. 783 | 115. 198. 784 | 92. 251. 785 | 190. 340. 786 | 493. 322. 787 | 592. 316. 788 | 364. 181. 789 | 355. 222. 790 | 462. 254. 791 | 459. 270. 792 | 526. 279. 793 | 552. 242. 794 | IMAGE=63064.jpg 795 | ID=52 796 | LM=12 797 | 282. 108. 798 | 89. 189. 799 | 70. 246. 800 | 171. 334. 801 | 465. 302. 802 | 568. 293. 803 | 337. 168. 804 | 333. 208. 805 | 433. 236. 806 | 431. 251. 807 | 502. 260. 808 | 527. 220. 809 | IMAGE=63065.jpg 810 | ID=53 811 | LM=12 812 | 274. 116. 813 | 47. 219. 814 | 27. 275. 815 | 137. 368. 816 | 491. 339. 817 | 607. 334. 818 | 332. 180. 819 | 325. 234. 820 | 455. 266. 821 | 450. 285. 822 | 535. 293. 823 | 563. 250. 824 | IMAGE=63067.jpg 825 | ID=54 826 | LM=12 827 | 258. 89. 828 | 35. 180. 829 | 11. 235. 830 | 119. 334. 831 | 469. 320. 832 | 582. 314. 833 | 321. 154. 834 | 312. 207. 835 | 435. 242. 836 | 432. 259. 837 | 518. 273. 838 | 542. 231. 839 | IMAGE=63069.jpg 840 | ID=55 841 | LM=12 842 | 252. 98. 843 | 42. 188. 844 | 21. 237. 845 | 113. 337. 846 | 466. 327. 847 | 580. 326. 848 | 311. 165. 849 | 310. 218. 850 | 424. 251. 851 | 420. 270. 852 | 512. 282. 853 | 535. 241. 854 | IMAGE=63070.jpg 855 | ID=56 856 | LM=12 857 | 257. 90. 858 | 39. 187. 859 | 15. 243. 860 | 115. 339. 861 | 477. 324. 862 | 583. 319. 863 | 322. 159. 864 | 322. 210. 865 | 439. 245. 866 | 436. 262. 867 | 514. 276. 868 | 542. 231. 869 | IMAGE=63071.jpg 870 | ID=57 871 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ml-morph [![DOI](https://zenodo.org/badge/195575274.svg)](https://zenodo.org/badge/latestdoi/195575274) 2 | 3 | Machine-learning tools for landmark-based morphometrics 4 | 5 | 6 | Porto, A. and Voje, K.L., 2020. ML‐morph: A fast, accurate and general approach for automated detection and landmarking of biological structures in images. Methods in Ecology and Evolution, 11(4), pp.500-512. 7 | 8 | ![Alt text](logo.png) 9 | 10 | ## Python Dependencies 11 | 12 | - numpy>=1.13.3 13 | - pandas>=0.22.0 14 | - dlib>=19.7.0 15 | - opencv-python>=3.4.0.12 16 | 17 | If their dependencies are satisfied, these modules can be installed using: 18 | 19 | pip install -r requirements.txt 20 | 21 | ## Optional Dependencies 22 | - imglab 23 | 24 | For those who want to visualize the xml files produced by the pipeline, we recommend installing the [`imglab`](https://github.com/davisking/dlib/tree/master/tools/imglab) tool that is included in the `dlib 19.7.0` source code. 25 | Please refer to the [`original repository`](https://github.com/davisking/dlib/tree/master/tools/imglab) for installation details and basic usage. 26 | An alternative [`version of imglab`](https://imglab.in/) is also available and can be used directly from the web browser. 27 | 28 | ## Installation notes and general issues 29 | For Mac users, a series of dependencies for `dlib>=19.7.0` will need to be installed before it can be used. A detailed protocol can be found [here](https://medium.com/@210/install-dlib-on-mac-ff9f4d03ad8). 30 | 31 | For windows users, the `dlib>=19.7.0` installation will sometimes fail. An alternative way to install it is to use a `.whl`: 32 | 33 | pip install https://pypi.python.org/packages/da/06/bd3e241c4eb0a662914b3b4875fc52dd176a9db0d4a2c915ac2ad8800e9e/dlib-19.7.0-cp36-cp36m-win_amd64.whl#md5=b7330a5b2d46420343fbed5df69e6a3f 34 | 35 | Also note that while **ml-morph** can handle multiple image file formats, some care is needed with regards to the presence of special characters in image filenames. So far, we have only had problems with `&`, but it is possible that other special characters might lead the software to throw out an error. 36 | 37 | ## Usage 38 | **ml-morph** uses a combination of [`object detection`](https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf) with [`shape prediction`](http://www.nada.kth.se/~sullivan/Papers/Kazemi_cvpr14.pdf) to perform automated landmarking in images of semi-rigid biological structures. To generate such detectors and predictors, we need to train machine learning models using manually annotated datasets. These manually annotated datasets can be generated using the `imglab` tool, or (alternatively) can also be generated by converting traditional morphometric landmark files (`tps` and `standard XY coordinate`). 39 | 40 | The pipeline itself has four mains components, which should be applied in sequence: 41 | 1. Preprocessing `preprocessing.py` 42 | 2. Training and Testing object detectors `detector_trainer.py` `detector_tester.py` 43 | 3. Training and testing shape predictors `shape_trainer.py` `shape_tester.py` 44 | 4. Predicting the landmark positions in a new set of images `prediction.py` 45 | 46 | 47 | ### Vignette 48 | 49 | Here, we will go through the **ml-morph** pipeline using a tiny fly wing dataset (see `image-examples`) to illustrate the full-training process. This dataset contains `58` images of drosophilid wings, which have been annotated for `12` landmarks (see `landmark-examples`). We have provided landmark annotations in two file formats traditionally used by the morphometric community. While this dataset is too small to generate high performing detectors and predictors, it still allows us to demonstrate how this software can be used. 50 | 51 | 52 | #### 1) Preprocessing (_preprocessing.py_) 53 | 54 | In the preprocessing step, **ml-morph** will split a user-defined image folder into `train` and `test` sets. When splitting the image files, **ml-morph** will convert all image files to `.jpg` and _(optionally)_ also generate the `train.xml` and `test.xml` downstream files from previously acquired annotations. The xml files generated during this step contain the landmark and bounding box annotations for each image in the `train` and `test` folders. Please see the following scenarios for usage details: 55 | 56 | ##### option 1 - No previous annotation 57 | When creating a training and testing dataset from scratch, the preprocessing step will only serve to split the images into `train`and `test` sets (80%/20% split). This can be accomplished using: 58 | 59 | python3 preprocessing.py -i image-examples 60 | 61 | Once created, the images in the `train`and `test` folders will need to be manually annotated. The user should choose the appropriate software to do so ([`tpsDig`](https://life.bio.sunysb.edu/morph/), [`geomorph`](https://cran.r-project.org/web/packages/geomorph/index.html), [`imglab`](https://github.com/davisking/dlib/tree/master/tools/imglab), to cite a few possibilities). In order to annotate the training images from scratch using [`imglab`](https://github.com/davisking/dlib/tree/master/tools/imglab), simply create the initial `train.xml` file using: 62 | 63 | ./imglab -c train.xml /train 64 | 65 | This file can then be annotated, in our example, using the following command: 66 | 67 | ./imglab --parts '1 2 3 4 5 6 7 8 9 10 11 12' train.xml 68 | 69 | See `./imglab -h` for other usage possibilities and the `imglab` manual for details of the annotation procedure. 70 | 71 | 72 | 73 | ##### option 2 - Previously annotated dataset 74 | When creating training and testing sets from previously annotated datasets, the preprocessing step will not only split the images into `train` and `test` sets but also generate the downstream input files (`train.xml` and `test.xml`). Using the `.tps` annotation file as an example, simply type: 75 | 76 | python3 preprocessing.py -i image-examples -t landmark-examples/tps-example.tps 77 | 78 | This command will generate the `train` and `test` folders, as well as the `train.xml` and `test.xml` downstream files. 79 | 80 | 81 | 82 | 83 | #### 2) Training and testing object detectors (detector_trainer.py) 84 | 85 | In order to train a model to detect fly wings in images, we can use the **ml-morph** detector trainer. Several parameters can be given to the trainer: 86 | 87 | python3 detector_trainer.py --help 88 | 89 | ``` 90 | usage: detector_trainer.py [-h] [-d] [-t] [-o] [-n] [-s] [-e] [-c] [-u] [-w] 91 | 92 | optional arguments: 93 | -h, --help show this help message and exit 94 | -d , --dataset training data (default = train.xml) 95 | -t , --test (optional) test data. if not provided, the model is not 96 | tested 97 | -o , --out output filename (default = detector) 98 | -n , --n-threads number of threads to be used (default = 1) 99 | -s , --symmetrical (True/False) indicating whether objects are bilaterally 100 | symmetrical (default = False) 101 | -e , --epsilon insensitivity parameter (default = 0.01) 102 | -c , --c-param soft margin parameter C (default =5) 103 | -u , --upsample upsample limit (default = 0) 104 | -w , --window-size (optional) detection window size 105 | 106 | ``` 107 | 108 | 109 | In the wing example, we have a very small dataset, so we are running the **ml-morph** model trainer with parameters that work reasonably in this context: 110 | 111 | python3 detector_trainer.py -d train.xml -t test.xml -n 7 -w 79000 -e 0.001 -c 15 112 | 113 | 114 | ``` 115 | Training with C: 15 116 | Training with epsilon: 0.001 117 | Training using 7 threads. 118 | Training with sliding window 428 pixels wide by 184 pixels tall. 119 | ``` 120 | 121 | These parameters should not be used, in any way, as default training parameters. Exploration of the **ml-morph** parameter space can have a large impact on improving the final performance of the detection algorithm. Also note that testing of the model is performed immediately after training if the `--test` flag is provided, but not otherwise. 122 | At the end, you should observe something like this: 123 | 124 | ``` 125 | Training complete. 126 | Trained with C: 15 127 | Training with epsilon: 0.001 128 | Trained using 7 threads. 129 | Trained with sliding window 428 pixels wide by 184 pixels tall. 130 | Saved detector to file detector.svm 131 | Training - precision: 1, recall: 1, average precision: 1 132 | Testing - precision: 1, recall: 1, average precision: 1 133 | ``` 134 | 135 | One file is generated during the training process (`detector.svm` or the user-defined name). This file represents the support vector machine (SVM) classifier. With this classifier, one can use **ml-morph** to perform object detection in images of fly wings. 136 | If the user wants to test a model that was trained in the past, this can be done using: 137 | 138 | python3 detector_tester.py -t test.xml -d detector.svm 139 | 140 | ``` 141 | Testing - precision: 1, recall: 1, average precision: 1 142 | ``` 143 | 144 | 145 | 146 | #### 3) Training and testing shape predictors (shape_trainer.py) 147 | 148 | In order to train a model to predict wing shape, we can use the **ml-morph** shape trainer. Several parameters can be given to the trainer: 149 | 150 | python3 shape_trainer.py --help 151 | 152 | ``` 153 | usage: shape_trainer.py [-h] [-d] [-t] [-o] [-th] [-dp] [-c] [-nu] [-os] [-s] 154 | [-f] [-n] 155 | 156 | optional arguments: 157 | -h, --help show this help message and exit 158 | -d , --dataset training data (default = train.xml) 159 | -t , --test test data (default = test.xml).if not provided, no 160 | testing is done 161 | -o , --out output filename (default = predictor) 162 | -th , --threads number of threads to be used (default = 1) 163 | -dp , --tree-depth choice of tree depth (default = 4) 164 | -c , --cascade-depth 165 | choice of cascade depth (default = 15) 166 | -nu , --nu regularization parameter (default = 0.1) 167 | -os , --oversampling 168 | oversampling amount (default = 10) 169 | -s , --test-splits number of test splits (default = 20) 170 | -f , --feature-pool-size 171 | choice of feature pool size (default = 500) 172 | -n , --num-trees number of regression trees (default = 500) 173 | ``` 174 | 175 | 176 | Again, since we have a very small dataset, we are running the **ml-morph** model trainer with parameters that work reasonably in this context: 177 | 178 | python3 shape_trainer.py -d train.xml -t test.xml -th 7 -dp 3 -c 20 -nu 0.08 -os 200 -f 700 179 | 180 | ``` 181 | Training with cascade depth: 20 182 | Training with tree depth: 3 183 | Training with 500 trees per cascade level. 184 | Training with nu: 0.08 185 | Training with random seed: 186 | Training with oversampling amount: 200 187 | Training with feature pool size: 700 188 | Training with feature pool region padding: 0 189 | Training with lambda_param: 0.1 190 | Training with 20 split tests. 191 | Fitting trees... 192 | ``` 193 | 194 | As above, these parameters should not be used, in any way, as default training parameters. Also note that testing of the model is performed immediately after training if the `-t` flag is provided, but not otherwise. 195 | At the end, you should observe something like this: 196 | 197 | ``` 198 | Training complete 199 | Training complete, saved predictor to file predictor.dat 200 | Training error (average pixel deviation): 0.0018115942028985507 201 | Testing error (average pixel deviation): 2.2899980012040912 202 | ``` 203 | 204 | One file is generated during the training process (`predictor.dat` or the user-defined name). This file represents the **ml-morph** cascade shape regression model. With this model, one can perform shape prediction (i.e., landmark coordinates) in objects detected using **ml-morph**. 205 | If the user wants to test a model that was trained in the past, this can be done using: 206 | 207 | python3 shape_tester.py -t test.xml -p predictor.dat 208 | 209 | ``` 210 | Testing error (average pixel deviation): 2.2899980012040912 211 | ``` 212 | 213 | 214 | #### 4) Predicting the landmark positions in a new set of images (prediction.py) 215 | 216 | Finally, **ml-morph** also allows users to use trained models to perform automated landmarking in a new image set. Several parameters can be given to the prediction algorithm: 217 | 218 | python3 prediction.py --help 219 | 220 | ``` 221 | usage: prediction.py [-h] [-i] [-d] [-p] [-o] [-u] [-t] [-l] 222 | 223 | optional arguments: 224 | -h, --help show this help message and exit 225 | -i , --input-dir input directory (default = pred) 226 | -d , --detector trained object detection model (default = 227 | detector.svm) 228 | -p , --predictor trained shape prediction model (default = 229 | predictor.dat) 230 | -o , --out-file output file name (default = output.xml) 231 | -u , --upsample-limit 232 | upsample limit (default= 0 ; max = 2) 233 | -t , --threshold detector's confidence threshold for outputting an 234 | object (default= 0) 235 | -l , --ignore-list (optional) prevents landmarks of choice from being 236 | output 237 | ``` 238 | 239 | For simplicity, here we will just predict the landmark positions in the images in the test set. This can be done as follows: 240 | 241 | python3 prediction.py -i test -d detector.svm -p predictor.dat 242 | 243 | A single xml file is produced as an output (`output.xml` or user-defined name). The user can then visualize the predictions using `imglab`, if needed: 244 | 245 | ./imglab output.xml 246 | 247 | Alternatively, the landmark data can also be imported into python using functions within `utils.py`: 248 | 249 | ```python 250 | from utils import * 251 | df = dlib_xml_to_pandas('output.xml') 252 | ``` 253 | 254 | As should be noted, the tiny size of the dataset prevents the model from attaining high performance. Still, for such small dataset, the performance is quite reasonable. 255 | 256 | And that is it ! 257 | 258 | ## Final remarks 259 | 260 | Any feedback on the software in this repository is greatly appreciated! 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | #Part of the standard library 2 | import xml.etree.ElementTree as ET 3 | from xml.dom import minidom 4 | import os 5 | import csv 6 | import re 7 | import random 8 | import shutil 9 | import glob 10 | 11 | #Not part of the standard library 12 | import numpy as np 13 | import pandas as pd 14 | import cv2 15 | import dlib 16 | 17 | #Tools for using previously annotated datasets 18 | 19 | def read_csv(input): 20 | ''' 21 | This function reads a XY coordinate file (following the tpsDig coordinate system) containing several specimens(rows) 22 | and any number of landmarks. It is generally assumed here that the file contains a header and no other 23 | columns other than an id column (first column) and the X0 Y0 ...Xn Yn coordinates for n landmarks.It is also 24 | assumed that the file contains no missing values. 25 | 26 | Parameters: 27 | input (str): The XY coordinate file (csv format) 28 | Returns: 29 | dict: dictionary containing two keys (im= image id, coords= array with 2D coordinates) 30 | 31 | ''' 32 | csv_file = open(input, 'r') 33 | csv =csv_file.read().splitlines() 34 | csv_file.close() 35 | im, coords_array = [], [] 36 | 37 | for i, ln in enumerate(csv): 38 | if i > 0: 39 | im.append(ln.split(',')[0]) 40 | coord_vec=ln.split(',')[1:] 41 | coords_mat = np.reshape(coord_vec, (int(len(coord_vec)/2),2)) 42 | coords = np.array(coords_mat, dtype=float) 43 | coords_array.append(coords) 44 | return {'im': im, 'coords': coords_array} 45 | 46 | def read_tps(input): 47 | ''' 48 | This function reads a tps coordinate file containing several specimens and an arbitrary number of landmarks. 49 | A single image file can contain as many specimens as wanted. 50 | It is generally assumed here that all specimens were landmarked in the same order.It is also assumed that 51 | the file contains no missing values. 52 | 53 | Parameters: 54 | input (str): The tps coordinate file 55 | Returns: 56 | dict: dictionary containing four keys 57 | (lm= number of landmarks,im= image id, scl= scale, coords= array with 2D coordinates) 58 | 59 | ''' 60 | tps_file = open(input, 'r') 61 | tps = tps_file.read().splitlines() 62 | tps_file.close() 63 | lm, im, sc, coords_array = [], [], [], [] 64 | 65 | for i, ln in enumerate(tps): 66 | if ln.startswith("LM"): 67 | lm_num = int(ln.split('=')[1]) 68 | lm.append(lm_num) 69 | coords_mat = [] 70 | for j in range(i + 1, i + 1 + lm_num): 71 | coords_mat.append(tps[j].split(' ')) 72 | coords_mat = np.array(coords_mat, dtype=float) 73 | coords_array.append(coords_mat) 74 | 75 | if ln.startswith("IMAGE"): 76 | im.append(ln.split('=')[1]) 77 | 78 | if ln.startswith("SCALE"): 79 | sc.append(ln.split('=')[1]) 80 | return {'lm': lm, 'im': im, 'scl': sc, 'coords': coords_array} 81 | 82 | 83 | #dlib xml tools 84 | 85 | 86 | def add_part_element(bbox,num,sz): 87 | ''' 88 | Internal function used by generate_dlib_xml. It creates a 'part' xml element containing the XY coordinates 89 | of an arbitrary number of landmarks. Parts are nested within boxes. 90 | 91 | Parameters: 92 | bbox (array): XY coordinates for a specific landmark 93 | num(int)=landmark id 94 | sz (int)=the image file's height in pixels 95 | 96 | 97 | Returns: 98 | part (xml tag): xml element containing the 2D coordinates for a specific landmark id(num) 99 | 100 | ''' 101 | part = ET.Element('part') 102 | part.set('name',str(int(num))) 103 | part.set('x',str(int(bbox[0]))) 104 | part.set('y',str(int(sz-bbox[1]))) 105 | return part 106 | 107 | def add_bbox_element(bbox,sz,padding=0): 108 | ''' 109 | Internal function used by generate_dlib_xml. It creates a 'bounding box' xml element containing the 110 | four parameters that define the bounding box (top,left, width, height) based on the minimum and maximum XY 111 | coordinates of its parts(i.e.,landmarks). An optional padding can be added to the bounding box.Boxes are 112 | nested within images. 113 | 114 | Parameters: 115 | bbox (array): XY coordinates for all landmarks within a bounding box 116 | sz (int)= the image file's height in pixels 117 | padding(int)= optional parameter definining the amount of padding around the landmarks that should be 118 | used to define a bounding box, in pixels (int). 119 | 120 | 121 | Returns: 122 | box (xml tag): xml element containing the parameters that define a bounding box and its corresponding parts 123 | 124 | ''' 125 | box = ET.Element('box') 126 | height = bbox[:,1].max()-bbox[:,1].min()+2*padding 127 | width = bbox[:,0].max()-bbox[:,0].min()+2*padding 128 | top = sz-bbox[:,1].max()-padding 129 | if top < 1: 130 | top =1 131 | left = bbox[:,0].min()-padding 132 | if left < 1: 133 | left =1 134 | 135 | box.set('top', str(int(top))) 136 | box.set('left', str(int(left))) 137 | box.set('width', str(int(width))) 138 | box.set('height', str(int(height))) 139 | for i in range(0,len(bbox)): 140 | box.append(add_part_element(bbox[i,:],i,sz)) 141 | return box 142 | 143 | def add_image_element(image, coords, sz, path): 144 | ''' 145 | Internal function used by generate_dlib_xml. It creates a 'image' xml element containing the 146 | image filename and its corresponding bounding boxes and parts. 147 | 148 | Parameters: 149 | image (str): image filename 150 | coords (array)= XY coordinates for all landmarks within a bounding box 151 | sz (int)= the image file's height in pixels 152 | 153 | 154 | Returns: 155 | image (xml tag): xml element containing the parameters that define each image (boxes+parts) 156 | 157 | ''' 158 | image_e = ET.Element('image') 159 | image_e.set('file', str(path)) 160 | image_e.append(add_bbox_element(coords,sz)) 161 | return image_e 162 | 163 | def generate_dlib_xml(images,sizes,folder='train',out_file='output.xml'): 164 | ''' 165 | Generates a dlib format xml file for training or testing of machine learning models. 166 | 167 | Parameters: 168 | images (dict): dictionary output by read_tps or read_csv functions 169 | sizes (dict)= dictionary of image file sizes output by the split_train_test function 170 | folder(str)= name of the folder containing the images 171 | 172 | 173 | Returns: 174 | None (file written to disk) 175 | ''' 176 | root = ET.Element('dataset') 177 | root.append(ET.Element('name')) 178 | root.append(ET.Element('comment')) 179 | 180 | images_e = ET.Element('images') 181 | root.append(images_e) 182 | 183 | for i in range(0,len(images['im'])): 184 | name=os.path.splitext(images['im'][i])[0]+'.jpg' 185 | path=os.path.join(folder,name) 186 | if os.path.isfile(path) is True: 187 | present_tags=[] 188 | for img in images_e.findall('image'): 189 | present_tags.append(img.get('file')) 190 | 191 | if path in present_tags: 192 | pos=present_tags.index(path) 193 | images_e[pos].append(add_bbox_element(images['coords'][i],sizes[name][0])) 194 | 195 | else: 196 | images_e.append(add_image_element(name,images['coords'][i],sizes[name][0],path)) 197 | 198 | et = ET.ElementTree(root) 199 | xmlstr = minidom.parseString(ET.tostring(et.getroot())).toprettyxml(indent=" ") 200 | with open(out_file, "w") as f: 201 | f.write(xmlstr) 202 | 203 | #Directory preparation tools 204 | 205 | 206 | def split_train_test(input_dir): 207 | ''' 208 | Splits an image directory into 'train' and 'test' directories. The original image directory is preserved. 209 | When creating the new directories, this function converts all image files to 'jpg'. The function returns 210 | a dictionary containing the image dimensions in the 'train' and 'test' directories. 211 | 212 | Parameters: 213 | input_dir(str)=original image directory 214 | 215 | Returns: 216 | sizes (dict): dictionary containing the image dimensions in the 'train' and 'test' directories. 217 | ''' 218 | # Listing the filenames.Folders must contain only image files (extension can vary).Hidden files are ignored 219 | filenames = os.listdir(input_dir) 220 | filenames = [os.path.join(input_dir, f) for f in filenames if not f.startswith('.')] 221 | 222 | # Splitting the images into 'train' and 'test' directories (80/20 split) 223 | random.seed(845) 224 | filenames.sort() 225 | random.shuffle(filenames) 226 | split = int(0.8 * len(filenames)) 227 | train_set = filenames[:split] 228 | test_set = filenames[split:] 229 | 230 | filenames = {'train':train_set, 231 | 'test': test_set} 232 | sizes={} 233 | for split in ['train','test']: 234 | sizes[split]={} 235 | if not os.path.exists(split): 236 | os.mkdir(split) 237 | else: 238 | print("Warning: the folder {} already exists. It's being replaced".format(split)) 239 | shutil.rmtree(split) 240 | os.mkdir(split) 241 | 242 | for filename in filenames[split]: 243 | basename=os.path.basename(filename) 244 | name=os.path.splitext(basename)[0] + '.jpg' 245 | sizes[split][name]=image_prep(filename,name,split) 246 | return sizes 247 | 248 | def image_prep(file, name, dir_path): 249 | ''' 250 | Internal function used by the split_train_test function. Reads the original image files and, while 251 | converting them to jpg, gathers information on the original image dimensions. 252 | 253 | Parameters: 254 | file(str)=original path to the image file 255 | name(str)=basename of the original image file 256 | dir_path(str)= directory where the image file should be saved to 257 | 258 | Returns: 259 | file_sz(array): original image dimensions 260 | ''' 261 | img = cv2.imread(file) 262 | if img is None: 263 | print('File {} was ignored'.format(file)) 264 | else: 265 | file_sz= [img.shape[0],img.shape[1]] 266 | cv2.imwrite(os.path.join(dir_path,name), img) 267 | return file_sz 268 | 269 | 270 | 271 | 272 | # Tools for predicting objects and shapes in new images 273 | 274 | def predictions_to_xml(detector_name:str, predictor_name:str,dir='pred',upsample=0,threshold=0,ignore=None,out_file='output_prediction.xml'): 275 | ''' 276 | Generates a dlib format xml file for model predictions. It uses previously trained models to 277 | identify objects in images and to predict their shape. 278 | 279 | Parameters: 280 | detector_name (str): object detector filename 281 | predictor_name (str): shape predictor filename 282 | dir(str): (optional) name of the directory containing images to be predicted 283 | upsample (int): (optional) number of times that an image should be upsampled (max=2 times) 284 | treshold (float): (optional) confidence threshold. Objects detected with lower confidence than 285 | the threshold are not output 286 | ignore (list): list of landmarks that should be ignored (based on landmark numeric id) 287 | out_file (str): name of the output file (xml format) 288 | 289 | Returns: 290 | None (out_file written to disk) 291 | 292 | ''' 293 | predictor = dlib.shape_predictor(predictor_name) 294 | detector = dlib.fhog_object_detector(detector_name) 295 | root = ET.Element('dataset') 296 | root.append(ET.Element('name')) 297 | root.append(ET.Element('comment')) 298 | images_e = ET.Element('images') 299 | root.append(images_e) 300 | for f in glob.glob(dir+"/*.jpg"): 301 | path, file = os.path.split(f) 302 | img = cv2.imread(f) 303 | image_e = ET.Element('image') 304 | image_e.set('file', str(f)) 305 | [boxes, confidences, detector_idxs] = dlib.fhog_object_detector.run( 306 | detector, img, upsample_num_times=upsample, adjust_threshold=threshold) 307 | for k, d in enumerate(boxes): 308 | shape = predictor(img, d) 309 | box = ET.Element('box') 310 | box.set('top', str(int(d.top()))) 311 | box.set('left', str(int(d.left()))) 312 | box.set('width', str(int(d.right()-d.left()))) 313 | box.set('height', str(int(d.bottom()-d.top()))) 314 | for i in range(0,shape.num_parts): 315 | if ignore is not None: 316 | if i not in ignore: 317 | part = ET.Element('part') 318 | part.set('name',str(int(i))) 319 | part.set('x',str(int(shape.part(i).x))) 320 | part.set('y',str(int(shape.part(i).y))) 321 | box.append(part) 322 | else: 323 | part = ET.Element('part') 324 | part.set('name',str(int(i))) 325 | part.set('x',str(int(shape.part(i).x))) 326 | part.set('y',str(int(shape.part(i).y))) 327 | box.append(part) 328 | 329 | image_e.append(box) 330 | images_e.append(image_e) 331 | 332 | et = ET.ElementTree(root) 333 | xmlstr = minidom.parseString(ET.tostring(et.getroot())).toprettyxml(indent=" ") 334 | with open(out_file, "w") as f: 335 | f.write(xmlstr) 336 | 337 | #Importing to pandas tools 338 | 339 | def natural_sort_XY(l): 340 | ''' 341 | Internal function used by the dlib_xml_to_pandas. Performs the natural sorting of an array of XY 342 | coordinate names. 343 | 344 | Parameters: 345 | l(array)=array to be sorted 346 | 347 | Returns: 348 | l(array): naturally sorted array 349 | ''' 350 | convert = lambda text: int(text) if text.isdigit() else 0 351 | alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key) ] 352 | return sorted(l, key = alphanum_key) 353 | 354 | def dlib_xml_to_pandas(xml_file: str,parse=False): 355 | ''' 356 | Imports dlib xml data into a pandas dataframe. An optional file parsing argument is present 357 | for very specific applications. For most people, the parsing argument should remain as 'False'. 358 | 359 | Parameters: 360 | xml_file(str)=file to be imported (dlib xml format) 361 | 362 | Returns: 363 | df(dataframe): returns a pandas dataframe containing the data in the xml_file. 364 | ''' 365 | tree=ET.parse(xml_file) 366 | root=tree.getroot() 367 | landmark_list=[] 368 | for images in root: 369 | for image in images: 370 | for boxes in image: 371 | box=boxes.attrib['top']\ 372 | +'_'+boxes.attrib['left']\ 373 | +'_'+boxes.attrib['width']\ 374 | +'_'+boxes.attrib['height'] 375 | for parts in boxes: 376 | if parts.attrib['name'] is not None: 377 | if parse is False: 378 | data={'id':image.attrib['file'], 379 | 'box_id':box, 380 | 'box_top':float(boxes.attrib['top']), 381 | 'box_left':float(boxes.attrib['left']), 382 | 'box_width':float(boxes.attrib['width']), 383 | 'box_height':float(boxes.attrib['height']), 384 | 'X'+parts.attrib['name']:float(parts.attrib['x']), 385 | 'Y'+parts.attrib['name']:float(parts.attrib['y']) } 386 | else: 387 | data={'id':image.attrib['file'].replace('/','_').replace('x','').split('_')[1], 388 | 'side':image.attrib['file'].replace('/','_').replace('x','').split('_')[2], 389 | 'replicate':image.attrib['file'].replace('/','_').replace('x','').split('_')[3], 390 | 'voltage':image.attrib['file'].replace('/','_').replace('x','').split('_')[4], 391 | 'zoom':image.attrib['file'].replace('/','_').replace('x','').split('_')[5], 392 | 'box_id':box, 393 | 'box_top':float(boxes.attrib['top']), 394 | 'box_left':float(boxes.attrib['left']), 395 | 'box_width':float(boxes.attrib['width']), 396 | 'box_height':float(boxes.attrib['height']), 397 | 'X'+parts.attrib['name']:float(parts.attrib['x']), 398 | 'Y'+parts.attrib['name']:float(parts.attrib['y']) } 399 | 400 | landmark_list.append(data) 401 | dataset=pd.DataFrame(landmark_list) 402 | df = dataset.groupby(['id', 'box_id'], sort=False).max() 403 | df=df[natural_sort_XY(df)] 404 | return df 405 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------