9eafb1414e
* skeleton stereo_baselines * skeleton stereo_baselines (2) * combine stereo evaluation * fix stereo bugs * refactor for multiple baselines * temp * temp * fix disparity bug * fix bug on avg_disparity * cleaning * cleaning * cleaning (2) * methods_stereo variable * refactor evaluation * cleaning * create general mode to save txt files * temp * fix bug in compare errors * fix dicionary of iou * unify parser * skeleton pose baseline * update visualization names * refactor for modularity * multiple baselines running * update printing information * add evaluation files filter * refactor skeleton * refactor skeleton(2) * cleaning * working refactor without reid * add features and keypoints distinction * working reid representation * refactor to matricial form * while version * new for version * correct bug in updated for version * fix bugs in while version and reformat * add new counting * fix bug on counting * fix bug on evaluation * pylint cleaning * build new version * change test setting to allow flexibility on different platforms * pylint clening(2)
70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
"""Test if the main modules of the package run correctly"""
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import os
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import sys
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import json
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# Python does not consider the current directory to be a package
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sys.path.insert(0, os.path.join('..', 'monoloco'))
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from PIL import Image
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from monoloco.train import Trainer
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from monoloco.network import MonoLoco
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from monoloco.network.process import preprocess_pifpaf, factory_for_gt
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from monoloco.visuals.printer import Printer
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JOINTS = 'tests/joints_sample.json'
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PIFPAF_KEYPOINTS = 'tests/002282.png.pifpaf.json'
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IMAGE = 'docs/002282.png'
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def tst_trainer(joints):
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trainer = Trainer(joints=joints, epochs=150, lr=0.01)
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_ = trainer.train()
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dic_err, model = trainer.evaluate()
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return dic_err['val']['all']['mean'], model
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def tst_prediction(model, path_keypoints):
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with open(path_keypoints, 'r') as f:
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pifpaf_out = json.load(f)
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kk, _ = factory_for_gt(im_size=[1240, 340])
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# Preprocess pifpaf outputs and run monoloco
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boxes, keypoints = preprocess_pifpaf(pifpaf_out)
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monoloco = MonoLoco(model)
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outputs, varss = monoloco.forward(keypoints, kk)
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dic_out = monoloco.post_process(outputs, varss, boxes, keypoints, kk)
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return dic_out, kk
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def tst_printer(dic_out, kk, image_path):
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"""Draw a fake figure"""
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with open(image_path, 'rb') as f:
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pil_image = Image.open(f).convert('RGB')
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printer = Printer(image=pil_image, output_path='tests/test_image', kk=kk, output_types=['combined'], z_max=15)
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figures, axes = printer.factory_axes()
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printer.draw(figures, axes, dic_out, pil_image, save=True)
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def test_package():
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# Training test
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val_acc, model = tst_trainer(JOINTS)
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assert val_acc < 2.5
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# Prediction test
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dic_out, kk = tst_prediction(model, PIFPAF_KEYPOINTS)
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assert dic_out['boxes'] and kk
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# Visualization test
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tst_printer(dic_out, kk, IMAGE)
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