def show_image(self,
gain=1,
processed=False,
compare=False,
median_filter=False,
zero_bg=False,
save_as=None):
img = self.image.astype('float')
if processed:
if zero_bg:
img = np.clip(
img - np.min(img) - (0 if zero_bg is True else zero_bg), 0,
np.inf)
img *= gain
if median_filter:
img = cv2.medianBlur(img.astype('uint16'), median_filter)
img = ImageProc.color_correct(img,
self.applied_bgr_mx,
max_val=self.max_val)
img = ImageProc.adjust_gamma(img,
self.applied_gamma,
self.applied_gamma_break,
max_val=self.max_val)
else:
img = np.clip(img * gain, 0, 2**self.bits - 1)
img = ImageProc.change_color_depth(img, self.bits, 8).astype('uint8')
if save_as is not None:
cv2.imwrite(save_as, img)
s = self.image.shape
if compare:
img = np.hstack((self.raw_image.astype(img.dtype),
np.ones((s[0], 1, s[2]), dtype=img.dtype), img))
sc = 1
plt.imshow(np.flip(img, axis=2))
plt.show()
return img, sc
def write_img(raw_imgs, outfile):
imgs = []
for raw in raw_imgs:
img = ImageProc.change_color_depth(raw.astype('float'), 8, bits)
img = ImageProc.adjust_gamma(img,
gamma,
gamma_break=gamma_break,
inverse=True,
max_val=max_val)
if bgr_cc_mx is not None:
img = ImageProc.color_correct(img,
bgr_cc_mx,
inverse=True,
max_val=max_val)
imgs.append(np.expand_dims(img, axis=0))
if len(imgs) == 1:
imgs = imgs[0]
stacked = np.stack(imgs, axis=0)
reduced = np.median(stacked, axis=0) if len(imgs) > 2 else np.min(
stacked, axis=0)
bg_img = np.round(reduced).squeeze().astype('uint16')
cv2.imwrite(outfile, bg_img, (cv2.CV_16U, ))
def __init__(self,
cam,
gain,
exposure,
timestamp,
raw_image,
background_img,
bg_offset=0,
bits=8,
applied_gamma=1.0,
applied_gamma_break=0.0,
applied_bgr_mx=None,
debug=False):
self.id = Frame.CURRENT_ID
Frame.CURRENT_ID += 1
self.cam = [cam] if isinstance(cam, Camera) else cam
self.resize_scale = raw_image.shape[1] / self.cam[0].width
for c in self.cam:
c.height, c.width = raw_image.shape[:2]
self.bits = bits = int(bits)
self.gain = gain
self.exposure = exposure
self.timestamp = timestamp
self.raw_image = raw_image
self.applied_gamma = applied_gamma
self.applied_gamma_break = applied_gamma_break
self.applied_bgr_mx = applied_bgr_mx
self.debug = debug
img_bits = int(str(raw_image.dtype)[4:])
max_val = 2**img_bits - 1
img = raw_image.astype('float')
# NOTE: NanoCam has this, doesnt make sense in general!
operation_order = reversed((
'ex_gamma',
'depth',
'color',
'gamma',
))
for op in operation_order:
if op == 'depth' and img_bits != bits:
img = ImageProc.change_color_depth(img, img_bits, bits)
max_val = 2**bits - 1
if op == 'gamma' and applied_gamma != 1.0:
img = ImageProc.adjust_gamma(img,
applied_gamma,
gamma_break=applied_gamma_break,
inverse=True,
max_val=max_val)
if op == 'color' and applied_bgr_mx is not None:
img = ImageProc.color_correct(img,
applied_bgr_mx,
inverse=True,
max_val=max_val)
# if op == 'ex_gamma' and GAMMA_ADJUSTMENT:
# img = ImageProc.adjust_gamma(img, GAMMA_ADJUSTMENT, inverse=True, max_val=max_val)
self.background_img = background_img
if background_img is not None:
self.image = ImageProc.remove_bg(img,
background_img,
gain=1,
offset=bg_offset,
max_val=max_val)
elif self.MISSING_BG_REMOVE_STRIPES:
for k in range(img.shape[2]):
img[:, :, k] -= np.percentile(img[:, :, k], 50,
axis=0).reshape((1, -1))
img[:, :, k] -= np.percentile(img[:, :, k], 50,
axis=1).reshape((-1, 1))
img += bg_offset - np.min(img)
self.image = np.clip(img, 0, max_val)
else:
self.image = img
if bg_offset is not False:
self.image = np.round(self.image).astype('uint16')
self.measures = []
