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test_stitcher.py
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import os
import unittest
from datetime import datetime
import numpy as np
from .context import (
VERBOSE_DIR,
AffineStitcher,
Stitcher,
StitchingError,
StitchingWarning,
load_test_img,
test_input,
test_output,
write_test_result,
)
class TestStitcher(unittest.TestCase):
def test_stitcher_weir(self):
stitcher = Stitcher()
max_derivation = 30
expected_shape = (673, 2636)
# from image filenames
imgs = [test_input("weir*.jpg")]
name = "weir_from_filenames"
self.stitch_test_with_warning(
stitcher,
imgs,
expected_shape,
max_derivation,
name,
StitchingWarning,
"Not all images are included",
)
# from loaded numpy arrays
imgs = [
load_test_img("weir_1.jpg"),
load_test_img("weir_2.jpg"),
load_test_img("weir_3.jpg"),
load_test_img("weir_noise.jpg"),
]
name = "weir_from_numpy_images"
self.stitch_test_with_warning(
stitcher,
imgs,
expected_shape,
max_derivation,
name,
StitchingWarning,
"Not all images are included",
)
def test_stitcher_with_not_matching_images(self):
stitcher = Stitcher()
imgs = [test_input("s1.jpg"), test_input("boat1.jpg")]
self.stitch_test_with_error(
stitcher,
imgs,
(),
0,
"",
StitchingError,
"No match exceeds the given confidence threshold",
verbose=False,
)
def test_stitcher_aquaduct(self):
stitcher = Stitcher(nfeatures=250, crop=False)
imgs = [test_input("s?.jpg")]
max_derivation = 3
expected_shape = (700, 1811)
name = "s_result"
self.stitch_test(stitcher, imgs, expected_shape, max_derivation, name)
def test_stitcher_boat1(self):
settings = {
"warper_type": "fisheye",
"wave_correct_kind": "no",
"finder": "dp_colorgrad",
"compensator": "no",
"crop": False,
}
stitcher = Stitcher(**settings)
imgs = [
test_input("boat5.jpg"),
test_input("boat2.jpg"),
test_input("boat3.jpg"),
test_input("boat4.jpg"),
test_input("boat1.jpg"),
test_input("boat6.jpg"),
]
max_derivation = 600
expected_shape = (14488, 7556)
name = "boat_fisheye"
self.stitch_test(
stitcher, imgs, expected_shape, max_derivation, name, verbose=False
)
def test_stitcher_boat2(self):
settings = {
"warper_type": "compressedPlaneA2B1",
"finder": "dp_colorgrad",
"compensator": "channel_blocks",
"crop": False,
}
stitcher = Stitcher(**settings)
imgs = [
test_input("boat5.jpg"),
test_input("boat2.jpg"),
test_input("boat3.jpg"),
test_input("boat4.jpg"),
test_input("boat1.jpg"),
test_input("boat6.jpg"),
]
max_derivation = 600
expected_shape = (7400, 12340)
name = "boat_fisheye"
self.stitch_test(
stitcher, imgs, expected_shape, max_derivation, name, verbose=False
)
def test_stitcher_boat_aquaduct_subset(self):
graph = test_output("boat_subset_matches_graph.txt")
settings = {"final_megapix": 1, "matches_graph_dot_file": graph}
stitcher = Stitcher(**settings)
imgs = [
test_input("boat5.jpg"),
test_input("s1.jpg"),
test_input("s2.jpg"),
test_input("boat2.jpg"),
test_input("boat3.jpg"),
test_input("boat4.jpg"),
test_input("boat1.jpg"),
test_input("boat6.jpg"),
]
max_derivation = 100
expected_shape = (705, 3374)
name = "boat_subset_low_res"
self.stitch_test_with_warning(
stitcher,
imgs,
expected_shape,
max_derivation,
name,
StitchingWarning,
"Not all images are included",
)
with open(graph, "r") as file:
graph_content = file.read()
self.assertTrue(graph_content.startswith("graph matches_graph{"))
def test_affine_stitcher_budapest(self):
settings = {
"detector": "sift",
"crop": False,
}
stitcher = AffineStitcher(**settings)
imgs = [test_input("budapest?.jpg")]
max_derivation = 50
expected_shape = (1155, 2310)
name = "budapest"
self.stitch_test(stitcher, imgs, expected_shape, max_derivation, name)
def test_stitcher_feature_masks(self):
stitcher = Stitcher(crop=False)
# without masks
imgs = [test_input("barcode1.png"), test_input("barcode2.png")]
max_derivation = 25
expected_shape = (905, 2124)
name = "features_without_mask"
self.stitch_test(stitcher, imgs, expected_shape, max_derivation, name)
# with masks
masks = [test_input("mask1.png"), test_input("mask2.png")]
max_derivation = 15
expected_shape = (716, 1852)
name = "features_with_mask"
self.stitch_test(
stitcher, imgs, expected_shape, max_derivation, name, feature_masks=masks
)
def stitch_test(
self,
stitcher,
imgs,
expected_shape,
max_derivation,
name,
feature_masks=[],
verbose=True,
):
result = stitcher.stitch(imgs, feature_masks)
if verbose:
verbose_dir_name = datetime.now().strftime("%Y%m%d_%H%M%S") + "_" + name
verbose_dir = os.path.join(VERBOSE_DIR, verbose_dir_name)
os.makedirs(verbose_dir)
result_verbose = stitcher.stitch_verbose(imgs, feature_masks, verbose_dir)
np.testing.assert_allclose(
result.shape, result_verbose.shape, atol=max_derivation
)
np.testing.assert_allclose(
result.shape[:2], expected_shape, atol=max_derivation
)
write_test_result(name + ".jpg", result)
def stitch_test_with_warning(
self,
stitcher,
imgs,
expected_shape,
max_derivation,
name,
expected_warning_type,
expected_warning_message,
feature_masks=[],
verbose=True,
):
with self.assertWarns(expected_warning_type) as cm:
self.stitch_test(
stitcher,
imgs,
expected_shape,
max_derivation,
name,
feature_masks,
verbose,
)
self.assertTrue(str(cm.warning).startswith(expected_warning_message))
def stitch_test_with_error(
self,
stitcher,
imgs,
expected_shape,
max_derivation,
name,
expected_error_type,
expected_error_message,
feature_masks=[],
verbose=True,
):
with self.assertRaises(expected_error_type) as cm:
self.stitch_test(
stitcher,
imgs,
expected_shape,
max_derivation,
name,
feature_masks,
verbose,
)
self.assertTrue(str(cm.exception).startswith(expected_error_message))
def test_use_of_a_stitcher_for_multiple_image_sets(self):
# the scale should not be fixed by the first run but set dynamically
# based on every input image set.
stitcher = Stitcher()
_ = stitcher.stitch([test_input("s1.jpg"), test_input("s2.jpg")])
self.assertEqual(round(stitcher.images._scalers["MEDIUM"].scale, 2), 0.83)
_ = stitcher.stitch([test_input("boat1.jpg"), test_input("boat2.jpg")])
self.assertEqual(round(stitcher.images._scalers["MEDIUM"].scale, 2), 0.24)
def start_test():
unittest.main()
if __name__ == "__main__":
start_test()