def demosaicing_CFA_Bayer_bilinear(raw: RawImageInfo, output):
"""
Bilinear Bayer CFA Demosaicing
==============================
*Bayer* CFA (Colour Filter Array) bilinear demosaicing.
References
----------
- :cite:`Losson2010c` : Losson, O., Macaire, L., & Yang, Y. (2010).
Comparison of Color Demosaicing Methods. In Advances in Imaging and
Electron Physics (Vol. 162, pp. 173-265). doi:10.1016/S1076-5670(10)62005-8
"""
R_m, G_m, B_m = raw.masks_CFA_Bayer()
CFA = raw.get_raw_data()
H_G = np.float32(
[[0, 1, 0],
[1, 4, 1],
[0, 1, 0]]) * 0.25
H_RB = np.float32(
[[1, 2, 1],
[2, 4, 2],
[1, 2, 1]]) * 0.25
output[:, :, 2] = convolve(CFA * R_m, H_RB)
output[:, :, 1] = convolve(CFA * G_m, H_G)
output[:, :, 0] = convolve(CFA * B_m, H_RB)
del R_m, G_m, B_m, H_RB, H_G
return
def __init__(self, parmas, process_bar=None):
self.old_pipeline = []
self.pipeline = []
self.params = parmas
# img_list存储了pipeline中途所有的图像
# img_list长度比pipeline长1
self.img_list = [RawImageInfo()]
self.process_bar = process_bar
self.imglist_mutex = Lock()
self.ispProcthread = ISPProc(self.params, self.img_list, self.imglist_mutex)
def pipeline_reset(self):
"""
func: 重新开始一个pipeline，把以前的图像清除
"""
if(len(self.img_list) > 1):
self.imglist_mutex.acquire()
self.img_list = [RawImageInfo()]
self.imglist_mutex.release()
self.old_pipeline = []
self.pipeline = []
return True
return False
def __init__(self, name='RawImageEditor', parent=None):
super().__init__(name, parent, Ui_ImageEditor(), need_processBar=True)
self.scene = QGraphicsScene()
self.imageview = ImageView(self.scene, parent)
self.img_params = RawImageParams()
self.img_params = self.load_params(RawImageParams())
self.img_pipeline = IspPipeline(self.img_params,
process_bar=self.progress_bar)
self.img = RawImageInfo()
self.point_data = np.array([0])
self.scale_ratio = 100
self.show_img = None
self.select_awb = False
self.histView = None
# 由于graphicsView被自定义了，需要重新定义一下UI，gridlayout还需要重新加一下widget
self.ui.graphicsView.addWidget(self.imageview, 0, 1, 3, 1)
self.imageview.sigDragEvent.connect(self.__init_img)
self.imageview.sigMouseMovePoint.connect(self.show_point_rgb)
self.imageview.sigWheelEvent.connect(self.update_wheel_ratio)
# 回调函数初始化
self.ui.pipeline.doubleClicked.connect(self.update_img_index)
self.ui.pipeline_ok.clicked.connect(self.update_pipeline)
self.ui.open_image.clicked.connect(self.open_image)
self.ui.analysis_img.clicked.connect(self.openHistView)
self.ui.select_from_raw.clicked.connect(self.select_awb_from_raw)
self.imageview.rubberBandChanged.connect(self.update_awb_from_raw)
self.ui.save_image.clicked.connect(self.save_now_image)
self.ui.reload.clicked.connect(self.img_pipeline.reload_isp)
self.ui.inputflatphoto.clicked.connect(
self.img_params.rolloff.set_flatphoto)
# ISP 处理线程回调
self.img_pipeline.ispProcthread.doneCB.connect(self.update_img)
self.img_pipeline.ispProcthread.processRateCB.connect(
self.update_process_bar)
self.img_pipeline.ispProcthread.costTimeCB.connect(
self.update_time_bar)
self.img_pipeline.ispProcthread.errorCB.connect(self.error_report)
def get_image(self, index):
"""
func: 获取pipeline中的一幅图像
如果输入-1，则返回最后一幅图像
"""
ret_img = None
self.imglist_mutex.acquire()
if (index < len(self.img_list) and index >= 0):
ret_img = self.img_list[index]
elif (index < 0
and len(self.pipeline) + 1 + index < len(self.img_list)):
ret_img = self.img_list[len(self.pipeline) + 1 + index]
self.imglist_mutex.release()
if (ret_img is not None):
return ret_img
else:
return RawImageInfo()
def demosaic(raw: RawImageInfo, params: RawImageParams):
"""
function: demosaic
input: raw:RawImageInfo() params:RawImageParams()
demosaic有两种算法，设置demosaic的算法
0: Malvar-He-Cutler algorithm
1: directionally weighted gradient based interpolation algorithm
"""
ret_img = RawImageInfo()
ret_img.create_image('after demosaic', raw, depth=3)
if (params.get_demosaic_funct_type() == 0):
demosaicing_CFA_Bayer_bilinear(raw, ret_img.data)
elif (params.get_demosaic_funct_type() == 1):
demosaicing_CFA_Bayer_Malvar2004(raw, ret_img.data)
elif (params.get_demosaic_funct_type() == 2):
demosaicing_CFA_Bayer_Menon2007(raw, ret_img.data)
else:
return None
ret_img.clip_range()
ret_img.set_color_space("RGB")
return ret_img
def demosaicing_CFA_Bayer_Malvar2004(raw: RawImageInfo, output):
"""
*Bayer* CFA (Colour Filter Array) *Malvar (2004)* demosaicing.
References
----------
- :cite:`Malvar2004a` : Malvar, H. S., He, L.-W., Cutler, R., & Way, O. M.
(2004). High-Quality Linear Interpolation for Demosaicing of
Bayer-Patterned Color Images. International Conference of Acoustic, Speech
and Signal Processing, 5-8.
http://research.microsoft.com/apps/pubs/default.aspx?id=102068
"""
R_m, G_m, B_m = raw.masks_CFA_Bayer()
CFA = raw.get_raw_data()
GR_GB = np.float32(
[[0, 0, -1, 0, 0],
[0, 0, 2, 0, 0],
[-1, 2, 4, 2, -1],
[0, 0, 2, 0, 0],
[0, 0, -1, 0, 0]]) * 0.125
Rg_RB_Bg_BR = np.float32(
[[0, 0, 0.5, 0, 0],
[0, -1, 0, -1, 0],
[-1, 4, 5, 4, - 1],
[0, -1, 0, -1, 0],
[0, 0, 0.5, 0, 0]]) * 0.125
Rg_BR_Bg_RB = np.transpose(Rg_RB_Bg_BR)
Rb_BB_Br_RR = np.float32(
[[0, 0, -1.5, 0, 0],
[0, 2, 0, 2, 0],
[-1.5, 0, 6, 0, -1.5],
[0, 2, 0, 2, 0],
[0, 0, -1.5, 0, 0]]) * 0.125
R = CFA * R_m
G = CFA * G_m
B = CFA * B_m
del G_m
G = np.where(np.logical_or(R_m == 1, B_m == 1), convolve(CFA, GR_GB), G)
RBg_RBBR = convolve(CFA, Rg_RB_Bg_BR)
RBg_BRRB = convolve(CFA, Rg_BR_Bg_RB)
RBgr_BBRR = convolve(CFA, Rb_BB_Br_RR)
del GR_GB, Rg_RB_Bg_BR, Rg_BR_Bg_RB, Rb_BB_Br_RR
# Red rows.
R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape, dtype=np.float32)
# Red columns.
R_c = np.any(R_m == 1, axis=0)[np.newaxis] * np.ones(R.shape, dtype=np.float32)
# Blue rows.
B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape, dtype=np.float32)
# Blue columns
B_c = np.any(B_m == 1, axis=0)[np.newaxis] * np.ones(B.shape, dtype=np.float32)
del R_m, B_m
R = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_RBBR, R)
R = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_BRRB, R)
B = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_RBBR, B)
B = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_BRRB, B)
R = np.where(np.logical_and(B_r == 1, B_c == 1), RBgr_BBRR, R)
B = np.where(np.logical_and(R_r == 1, R_c == 1), RBgr_BBRR, B)
del RBg_RBBR, RBg_BRRB, RBgr_BBRR, R_r, R_c, B_r, B_c
output[:, :, 2] = R
output[:, :, 1] = G
output[:, :, 0] = B
del R,G,B
return
def demosaicing_CFA_Bayer_Menon2007(raw: RawImageInfo, output):
"""
DDFAPD - Menon (2007) Bayer CFA Demosaicing
===========================================
*Bayer* CFA (Colour Filter Array) DDFAPD - *Menon (2007)* demosaicing.
References
----------
- :cite:`Menon2007c` : Menon, D., Andriani, S., & Calvagno, G. (2007).
Demosaicing With Directional Filtering and a posteriori Decision. IEEE
Transactions on Image Processing, 16(1), 132-141.
doi:10.1109/TIP.2006.884928
"""
R_m, G_m, B_m = raw.masks_CFA_Bayer()
CFA = raw.get_raw_data()
h_0 = np.array([0, 0.5, 0, 0.5, 0], dtype=np.float32)
h_1 = np.array([-0.25, 0, 0.5, 0, -0.25], dtype=np.float32)
R = CFA * R_m
G = CFA * G_m
B = CFA * B_m
G_H = np.where(G_m == 0, _cnv_h(CFA, h_0) + _cnv_h(CFA, h_1), G)
G_V = np.where(G_m == 0, _cnv_v(CFA, h_0) + _cnv_v(CFA, h_1), G)
C_H = np.where(R_m == 1, R - G_H, 0)
C_H = np.where(B_m == 1, B - G_H, C_H)
C_V = np.where(R_m == 1, R - G_V, 0)
C_V = np.where(B_m == 1, B - G_V, C_V)
D_H = np.abs(C_H - np.pad(C_H, ((0, 0),
(0, 2)), mode=str('reflect'))[:, 2:])
D_V = np.abs(C_V - np.pad(C_V, ((0, 2),
(0, 0)), mode=str('reflect'))[2:, :])
del h_0, h_1, CFA, C_V, C_H
k = np.array(
[[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 3, 0, 3],
[0, 0, 0, 1, 0],
[0, 0, 1, 0, 1]], dtype=np.float32)
d_H = convolve(D_H, k, mode='constant')
d_V = convolve(D_V, np.transpose(k), mode='constant')
del D_H, D_V
mask = d_V >= d_H
G = np.where(mask, G_H, G_V)
M = np.where(mask, 1, 0)
del d_H, d_V, G_H, G_V
# Red rows.
R_r = np.transpose(np.any(R_m == 1, axis=1)[np.newaxis]) * np.ones(R.shape, dtype=np.float32)
# Blue rows.
B_r = np.transpose(np.any(B_m == 1, axis=1)[np.newaxis]) * np.ones(B.shape, dtype=np.float32)
k_b = np.array([0.5, 0, 0.5], dtype=np.float32)
R = np.where(
np.logical_and(G_m == 1, R_r == 1),
G + _cnv_h(R, k_b) - _cnv_h(G, k_b),
R,
)
R = np.where(
np.logical_and(G_m == 1, B_r == 1) == 1,
G + _cnv_v(R, k_b) - _cnv_v(G, k_b),
R,
)
B = np.where(
np.logical_and(G_m == 1, B_r == 1),
G + _cnv_h(B, k_b) - _cnv_h(G, k_b),
B,
)
B = np.where(
np.logical_and(G_m == 1, R_r == 1) == 1,
G + _cnv_v(B, k_b) - _cnv_v(G, k_b),
B,
)
R = np.where(
np.logical_and(B_r == 1, B_m == 1),
np.where(
M == 1,
B + _cnv_h(R, k_b) - _cnv_h(B, k_b),
B + _cnv_v(R, k_b) - _cnv_v(B, k_b),
),
R,
)
B = np.where(
np.logical_and(R_r == 1, R_m == 1),
np.where(
M == 1,
R + _cnv_h(B, k_b) - _cnv_h(R, k_b),
R + _cnv_v(B, k_b) - _cnv_v(R, k_b),
),
B,
)
del k_b, R_r, B_r
del M, R_m, G_m, B_m
output[:, :, 2] = R
output[:, :, 1] = G
output[:, :, 0] = B
del R,G,B
return
class RawImageEditor(SubWindow):
def __init__(self, name='RawImageEditor', parent=None):
super().__init__(name, parent, Ui_ImageEditor(), need_processBar=True)
self.scene = QGraphicsScene()
self.imageview = ImageView(self.scene, parent)
self.img_params = RawImageParams()
self.img_params = self.load_params(RawImageParams())
self.img_pipeline = IspPipeline(self.img_params,
process_bar=self.progress_bar)
self.img = RawImageInfo()
self.point_data = np.array([0])
self.scale_ratio = 100
self.show_img = None
self.select_awb = False
self.histView = None
# 由于graphicsView被自定义了，需要重新定义一下UI，gridlayout还需要重新加一下widget
self.ui.graphicsView.addWidget(self.imageview, 0, 1, 3, 1)
self.imageview.sigDragEvent.connect(self.__init_img)
self.imageview.sigMouseMovePoint.connect(self.show_point_rgb)
self.imageview.sigWheelEvent.connect(self.update_wheel_ratio)
# 回调函数初始化
self.ui.pipeline.doubleClicked.connect(self.update_img_index)
self.ui.pipeline_ok.clicked.connect(self.update_pipeline)
self.ui.open_image.clicked.connect(self.open_image)
self.ui.analysis_img.clicked.connect(self.openHistView)
self.ui.select_from_raw.clicked.connect(self.select_awb_from_raw)
self.imageview.rubberBandChanged.connect(self.update_awb_from_raw)
self.ui.save_image.clicked.connect(self.save_now_image)
self.ui.reload.clicked.connect(self.img_pipeline.reload_isp)
self.ui.inputflatphoto.clicked.connect(
self.img_params.rolloff.set_flatphoto)
# ISP 处理线程回调
self.img_pipeline.ispProcthread.doneCB.connect(self.update_img)
self.img_pipeline.ispProcthread.processRateCB.connect(
self.update_process_bar)
self.img_pipeline.ispProcthread.costTimeCB.connect(
self.update_time_bar)
self.img_pipeline.ispProcthread.errorCB.connect(self.error_report)
def error_report(self, value):
"""
func: 报告ISP算法错误
"""
critical(value, self)
def update_img(self):
"""
func: ISP 处理完成后的显示回调函数
"""
self.displayImage(self.img_pipeline.get_image(-1))
def update_process_bar(self, value):
"""
func: ISP 处理进度回调
"""
self.progress_bar.setValue(value)
def update_time_bar(self, value):
"""
func：ISP 处理时长回调
"""
self.time_bar.setText(value)
def show(self):
"""
func: 显示初始化
"""
super().show()
self.img_params.set_img_params_ui(self.ui)
self.ui.filename.repaint()
if (self.img_params.rawformat.filename != ""):
self.update_pipeline()
self.img = self.img_pipeline.get_image(-1)
self.displayImage(self.img)
self.rect = [
0, 0, self.img_params.rawformat.width,
self.img_params.rawformat.height
]
def displayImage(self, img):
"""
显示图像 输入需要是RawImageInfo
"""
self.scene.clear()
self.img = img
self.show_img = img.get_showimage()
if (self.show_img is not None):
showimg = QImage(self.show_img, self.show_img.shape[1],
self.show_img.shape[0], QImage.Format_BGR888)
self.scene.addPixmap(QPixmap(showimg))
self.ui.photo_title.setTitle(img.get_name())
if (self.histView is not None and self.histView.enable is True):
self.histView.update_rect_data(self.show_img, self.rect)
def select_awb_from_raw(self):
"""
func: 进入raw图选择模式，修改鼠标类型
"""
self.imageview.setDragMode(QGraphicsView.RubberBandDrag)
self.select_awb = True
def update_awb_from_raw(self, viewportRect, fromScenePoint, toScenePoint):
"""
func: 鼠标选中事件的回调：执行AWB的选择区域或者图像分析的选择区域
"""
if (toScenePoint.x() == 0 and toScenePoint.y() == 0
and self.rect[2] > self.rect[0]
and self.rect[3] > self.rect[1]):
if (self.select_awb == True):
self.imageview.setDragMode(QGraphicsView.ScrollHandDrag)
self.select_awb = False
awb_ratio = self.img.get_raw_img_rect(self.rect)
if (awb_ratio is not None):
self.img_params.awb.set_awb_ratio(awb_ratio)
awb_gain = self.img_params.awb.get_awb_gain()
self.ui.awb_r.setValue(awb_gain[0])
self.ui.awb_g.setValue(awb_gain[1])
self.ui.awb_b.setValue(awb_gain[2])
else:
critical("请在raw图上进行选择")
else:
if (self.histView is not None):
self.histView.update_rect_data(self.show_img, self.rect)
else:
self.rect = [
int(fromScenePoint.x()),
int(fromScenePoint.y()),
int(toScenePoint.x()),
int(toScenePoint.y())
]
def update_pipeline(self):
"""
func: 运行ISP pipeline
"""
self.img_params.get_img_params(self.ui)
self.img_pipeline.pipeline_clear()
for i in range(self.ui.pipeline.count()):
if (self.ui.pipeline.item(i).checkState() == Qt.Checked):
self.img_pipeline.add_pipeline_node(
self.ui.pipeline.item(i).data(0))
self.img_pipeline.run_pipeline()
def update_img_index(self, item):
"""
func: 更新当前画面的序号
"""
if (self.ui.pipeline.item(item.row()).checkState() == Qt.Checked):
index = self.img_pipeline.get_pipeline_node_index(item.data()) + 1
self.displayImage(self.img_pipeline.get_image(index))
def open_image(self):
"""
func: 打开图片的回调函数
"""
if (self.img_params.rawformat.filename != ''):
now_path = os.path.dirname(self.img_params.rawformat.filename)
else:
now_path = './'
imagepath = QFileDialog.getOpenFileName(None, '打开RAW图', now_path,
"raw (*.raw)")
self.__init_img(imagepath[0])
def __init_img(self, filename):
if (filename != ''):
self.ui.filename.setText(filename)
self.ui.filename.repaint()
self.update_pipeline()
self.img = self.img_pipeline.get_image(-1)
self.rect = [
0, 0, self.img_params.rawformat.width,
self.img_params.rawformat.height
]
def save_now_image(self):
"""
func: 保存图片的回调
"""
if (self.img.get_raw_data() is not None):
imagepath = QFileDialog.getSaveFileName(None, '保存图片', './',
"Images (*.jpg)")
if (imagepath[0] != ""):
self.img.save_image(imagepath[0])
def show_point_rgb(self, point):
"""
func: 鼠标移动的回调
"""
self.x = int(point.x())
self.y = int(point.y())
if (self.img.get_raw_data() is not None):
point_data = self.img.get_img_point(self.x, self.y)
if (point_data is not None):
self.point_data = point_data
self.set_img_info_show()
def update_wheel_ratio(self, ratio):
"""
func: 鼠标滚轮的回调
"""
if (self.img.get_raw_data() is not None):
self.scale_ratio = int(ratio * 100)
self.set_img_info_show()
def set_img_info_show(self):
"""
func: 显示像素点的值以及缩放比例
"""
if (self.point_data.size == 1):
self.info_bar.setText("x:{},y:{} : {}: 亮度:{} 缩放比例:{}%".format(
self.x, self.y,
self.img.get_img_point_pattern(self.y, self.x).upper(),
self.point_data, self.scale_ratio))
elif (self.point_data.size == 3):
if (self.img.get_color_space() == 'RGB'):
self.info_bar.setText(
"x:{},y:{} : R:{} G:{} B:{} 缩放比例:{}%".format(
self.x, self.y, self.point_data[2], self.point_data[1],
self.point_data[0], self.scale_ratio))
else:
self.info_bar.setText(
"x:{},y:{} : Y:{} Cr:{} Cb:{} 缩放比例:{}%".format(
self.x, self.y, self.point_data[0], self.point_data[1],
self.point_data[2], self.scale_ratio))
def openHistView(self):
self.histView = HistView(self.imageview)
rect = [0, 0, self.show_img.shape[1], self.show_img.shape[0]]
self.histView.update_rect_data(self.show_img, rect)
self.histView.show()