4992d4c34e
* update readme * remove legend for front * change default z_max to 100m * add visualization for distance * add visualization for distance * adjust text * remove legend for frontal image * change frontal visualization * adjust visualization * update fontsize as constant * change name of saving * change architecture name * change architecture name * change architecture name * remove gt boxes * support different resolutions * adapt to front and combined * change name to multi
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 stereoloco.train import Trainer
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from stereoloco.network import MonoLoco
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from stereoloco.network.process import preprocess_pifpaf, factory_for_gt
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from stereoloco.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=['multi'], 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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