def run_detection(transform, jpg, out, shape, interpolation, name,
LineDetectorClass):
image = image_cv_from_jpg_fn(jpg)
image = cv2.resize(image, shape, interpolation=interpolation)
# bgr = bgr[bgr.shape[0] / 2:, :, :]
image_detections = line_detection(LineDetectorClass, image)
transformed = transform(image)
transformed_clipped = image_clip_255(transformed)
transformed_detections = line_detection(LineDetectorClass,
transformed_clipped)
if not os.path.exists(out):
os.makedirs(out)
bn = os.path.splitext(os.path.basename(jpg))[0]
def write(postfix, im):
fn = os.path.join(out, '%s.%s.%s.png' % (bn, name, postfix))
cv2.imwrite(fn, zoom_image(im, 4))
together = make_images_grid(
[
image, # transformed,
merge_masks_res(image_detections),
gray2rgb(image_detections['edges']),
image_detections['annotated'],
transformed_clipped,
merge_masks_res(transformed_detections),
gray2rgb(transformed_detections['edges']),
transformed_detections['annotated'],
],
cols=4,
pad=35,
bgcolor=[1, 1, 1])
# write the string "name" in the upper left of image together
cv2.putText(together, name, (0, 20), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255), 2)
return together
def resize_small_images(image_dict):
check_isinstance(image_dict, dict)
max_H, max_W = 0, 0
for _, image in image_dict.items():
H, W = image.shape[0:2]
max_H = max(max_H, W)
max_W = max(max_W, W)
d = OrderedDict()
for k, image in image_dict.items():
if len(image.shape) == 2: # grayscale
image = gray2rgb(image)
H, W = image.shape[0:2]
ratio = max(max_H * 1.0 / H, max_W * 1.0 / W)
ratio = int(np.ceil(ratio))
if ratio > 1:
image2 = d8_image_zoom_linear(image, ratio)
else:
image2 = image
d[k] = image2
return d
def yellow(x):
x = gray2rgb(x)
x[:, :, R] *= 1
x[:, :, G] *= 1
x[:, :, B] *= 0
return x
def red(x):
x = gray2rgb(x)
x[:, :, R] *= 1
x[:, :, G] *= 0
x[:, :, B] *= 0
return x
def white(x):
x = gray2rgb(x)
return x