def debug_history(self): iv = ImageViewer() iv.remove_axis_values() iv.add(self.source, 'source', cmap='bgr') for img, func in zip(self.debug_out_list, self.functions): iv.add(img, str(func), cmap='bgr') return iv
time = evaluation['time']
kwargs = evaluation['kwargs']
test_perc = evaluation['test_perc']
all_dices.append(dice_vals)
all_filenames = filenames if all_filenames is None else all_filenames
pairs = list(zip(dice_vals, filenames))
pairs.sort()
dice_vals = [x for x, _ in pairs]
filenames = [x for _, x in pairs]
plt.plot(dice_vals, label=f'dice {i}')
plt.axhline(y=mean, color='rgb'[i % 3], ls='--', label=f'mean {i}')
print(
f'{FILENAME:<32} mean={mean:.04f} time={time:.02f}s fps={len(filenames)/time:.02f} test_perc={test_perc} kwargs={kwargs}'
)
plt.title('\n'.join(show_filenames))
plt.legend()
plt.show()
if len(all_dices) == 2:
worst = [(max(a - b, b - a), f)
for a, b, f in zip(*all_dices, filenames)]
worst.sort()
iv = ImageViewer()
for s, f in worst[-12:]:
iv.add(cv2.imread(f'data_test/paintings_gt/imgs/{f}'),
cmap='bgr',
title=f'{s}')
iv.show()
# I created a script to have the filenames of the images we use as tests # for filename in TEST_PAINTINGS: filename = "data_test/painting_09/00_calibration.jpg" img = np.array(cv2.imread(filename)) # Via the append command you can add a function to the pipeline, # In this case I have to do it because the last function takes as source img pipeline.append(Function(highlight_paintings, source=img, pad=100)) # Using the run command I execute the functions in order. # with debug=True for each step debug images are created that then # can be displayed in sequence # with print_time=True the times are printed for each function # Filename is optional, it's for printing out = pipeline.run(img, debug=True, print_time=True, filename=filename) # debug_history() returns an ImageViewer class with the output sequence for each function plots.append(pipeline.debug_history()) iv.add(out, cmap='bgr') # With pop the last function is removed from the pipeline list pipeline.pop() for plot in plots: # all debugging graphs are displayed plot.show() iv.show() # IMPORTANTE ####################################################################################### # La Pipeline eseguita con il comando debug=False ritornerà i valori dell'ultima funzione che non sono # obbligatoriamente delle immagini, infatti in paintings_rectification.py la uso per ottenere i corners # dei quadri trovati. Mentre con debug=True ritornerà l'ultima immagine di debug creata. # # Ogni funzione che viene inserita nella pipeline accetta una variabile input ed una variabile debug
def start_at(arr):
assert arr.ndim == 1
for i, val in enumerate(arr):
if val != 0:
break
return i
if __name__ == "__main__":
from image_viewer import show_me, ImageViewer
from data_test.standard_samples import RANDOM_PAINTING, FISH_EYE, CHESSBOARD, TEST_DISTORTION, get_random_paintings
for paint in [FISH_EYE, ]:
img = cv2.imread(paint)
img = cv2.resize(img, (1280, 720))
iv = ImageViewer()
mask = generate_mask(img)
iv.add(mask, cmap='bgr', title='mask')
canvas = np.empty_like(mask, dtype=np.float32)
canvas += _apply_edge_detection(img, 150, 250)
canvas = np.clip(canvas, 0, 255)
iv.add(img, cmap='bgr', title='original')
iv.add(canvas, cmap='bgr', title='canvas')
k = HTRDC(img, steps=50, range_min=-0.25, range_max=0)
canvas = undistort(img, k1=k)
cv2.imwrite('data_test/00_calibration_desired.jpg', canvas)
iv.add(canvas, cmap='bgr', title=f'{paint} k={k:.06f}')
iv.show()
if __name__ == "__main__":
from image_viewer import ImageViewer
from data_test.standard_samples import RANDOM_PAINTING, PAINTINGS_DB, TEST_RETRIEVAL
from painting_rectification import four_point_transform, remove_pad
from painting_detection import painting_detection
dataset_images = [cv2.imread(url) for url in PAINTINGS_DB]
for painting_path in TEST_RETRIEVAL:
img = cv2.imread(painting_path)
painting_contours = painting_detection(img)
iv = ImageViewer(cols=4)
for i, corners in enumerate(painting_contours):
img_sec = four_point_transform(img, corners)
if not img_sec is None:
scores = retrieve_painting(img_sec,
dataset_images,
verbose=False,
threshold=30,
resize_factor=1,
mse=False)
res, diff = best_match(scores)
target = dataset_images[res]
iv.add(img_sec, cmap='bgr')
iv.add(target,
cmap='bgr',
title=f'[{res}] {scores[res]} diff={diff}')
iv.add(img, title=painting_path.split('/')[-1], cmap='bgr')
iv.show()
if not found_correct_shape:
paintings_contours.append(rect_contour(contour, pad))
return paintings_contours
if __name__ == "__main__":
from data_test.standard_samples import TEST_PAINTINGS, PEOPLE, TEST_RETRIEVAL, PERSPECTIVE, get_random_paintings
from image_viewer import ImageViewer, show_me
from stopwatch import Stopwatch
from step_06_corners_detection import draw_lines
iv = ImageViewer(cols=3)
watch = Stopwatch()
for filename in [
'data_test/painting_09/00_calibration.jpg',
]:
img = cv2.imread(filename)
watch.start()
paintigs_contours = painting_detection(img)
res = _draw_all_contours(paintigs_contours, img)
time = watch.stop()
iv.add(res, cmap='bgr', title=f'time={time:.02f}s')
print(f'filename={filename} time={time:.02f}s')
show_me(
res,
title=
f'filename={filename} time={time:.02f}s n={len(paintigs_contours)}'
)
iv.show()
if __name__ == "__main__":
from data_test.standard_samples import TEST_PAINTINGS, PERSPECTIVE, get_random_paintings
from image_viewer import show_me
from step_06_corners_detection import draw_corners
# rgbImage = cv2.imread(PERSPECTIVE)
for filename in TEST_PAINTINGS[::-1]:
rgbImage = cv2.imread(filename)
# rgbImage = cv2.rotate(rgbImage, cv2.ROTATE_90_CLOCKWISE)
painting_contours = painting_detection(rgbImage, area_perc=0.96)
rgbImage_num = rgbImage.copy()
for i, corners in enumerate(painting_contours):
rgbImage_num = cv2.putText(rgbImage_num, str(i + 1),
mean_center(corners),
cv2.FONT_HERSHEY_SIMPLEX, 5,
(0, 0, 255), 20, cv2.LINE_AA)
for i, corners in enumerate(painting_contours):
iv = ImageViewer()
iv.add(rgbImage_num, 'original', cmap='bgr')
sec = cut_section(rgbImage_num, corners)
iv.add(sec, 'target', cmap='bgr')
cv2.imwrite('data_test/target.jpg', sec)
img = four_point_transform(rgbImage, np.array(corners))
if not img is None:
iv.add(img, 'VIT painting {}'.format(i + 1), cmap='bgr')
cv2.imwrite('data_test/vit.jpg', img)
img = painting_rectification(rgbImage, np.array(corners))
if not img is None:
iv.add(img, 'STACK painting {}'.format(i + 1), cmap='bgr')
cv2.imwrite('data_test/stack.jpg', img)
iv.show()
