def draw_splash(self):
print('[back] draw_splash()')
img = imread('_frontend/splash.png')
fig = df2.figure()
print(fig)
print(fig is self.win.plotWidget.figure)
df2.imshow(img)
df2.update()
def _plotarrow(x, y, dx, dy, color=df2.BLUE, label=''):
ax = df2.gca()
arrowargs = dict(head_width=.5, length_includes_head=True, label='')
arrow = df2.FancyArrow(x, y, dx, dy, **arrowargs)
arrow.set_edgecolor(color)
arrow.set_facecolor(color)
ax.add_patch(arrow)
df2.update()
def draw_splash(self):
print('[back] draw_splash()')
img = imread('_frontend/splash.png')
fig = df2.figure()
print(fig)
print(fig is self.win.plotWidget.figure)
df2.imshow(img)
df2.update()
def _plotarrow(x, y, dx, dy, color=df2.BLUE, label=''):
ax = df2.gca()
arrowargs = dict(head_width=.5, length_includes_head=True, label='')
arrow = df2.FancyArrow(x, y, dx, dy, **arrowargs)
arrow.set_edgecolor(color)
arrow.set_facecolor(color)
ax.add_patch(arrow)
df2.update()
def segment(img_path, roi):
img = cv2.imread(img_path)
# Grab cut parametrs
(w, h) = img.shape[:2]
[rx, ry, rw, rh] = roi
expand = 10
rect = (rx - expand, rw + expand, ry - expand, rh + expand)
mask_input = np.zeros((w, h), dtype=np.uint8)
bg_model = np.zeros((1, 13 * 5))
fg_model = np.zeros((1, 13 * 5))
num_iters = 5
# Make mask specify the ROI
mask_input[ry:(ry + rh), rx:(rx + rw)] = cv2.GC_PR_FGD
grabcut_mode = cv2.GC_INIT_WITH_MASK
# Do grab cut
(mask, bg_model, fg_model) = cv2.grabCut(img,
mask_input,
rect,
bg_model,
fg_model,
num_iters,
mode=grabcut_mode)
# Make the segmented chip
# Normalize mask
mask = np.array(mask, np.float64)
mask[mask == cv2.GC_BGD] = 0 #.1
mask[mask == cv2.GC_PR_BGD] = 0 #.25
mask[mask == cv2.GC_PR_FGD] = 1 #.75
mask[mask == cv2.GC_FGD] = 1
# Clean the mask
element = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10))
tmp_ = mask
tmp_ = cv2.erode(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
df2.imshow(tmp_)
df2.update()
mask = tmp_
# Reshape chip_mask to NxMx3
color_mask = np.tile(chip_mask.reshape(tuple(list(chip_mask.shape) + [1])),
(1, 1, 3))
seg_chip = np.array(chip, dtype=np.float) * color_mask
seg_chip = np.array(np.round(seg_chip), dtype=np.uint8)
return seg_chip
def segment(img_path, roi):
img = cv2.imread(img_path)
# Grab cut parametrs
(w, h) = img.shape[:2]
[rx, ry, rw, rh] = roi
expand = 10
rect = (rx-expand, rw+expand, ry-expand, rh+expand)
mask_input = np.zeros((w,h), dtype=np.uint8)
bg_model = np.zeros((1, 13 * 5))
fg_model = np.zeros((1, 13 * 5))
num_iters = 5
# Make mask specify the ROI
mask_input[ry:(ry+rh), rx:(rx+rw)] = cv2.GC_PR_FGD
grabcut_mode = cv2.GC_INIT_WITH_MASK
# Do grab cut
(mask, bg_model, fg_model) = cv2.grabCut(img, mask_input, rect,
bg_model, fg_model,
num_iters, mode=grabcut_mode)
# Make the segmented chip
# Normalize mask
mask = np.array(mask, np.float64)
mask[mask == cv2.GC_BGD] = 0#.1
mask[mask == cv2.GC_PR_BGD] = 0#.25
mask[mask == cv2.GC_PR_FGD] = 1#.75
mask[mask == cv2.GC_FGD] = 1
# Clean the mask
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(10,10))
tmp_ = mask
tmp_ = cv2.erode(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
tmp_ = cv2.dilate(tmp_, element)
df2.imshow(tmp_); df2.update()
mask = tmp_
# Reshape chip_mask to NxMx3
color_mask = np.tile(chip_mask.reshape(tuple(list(chip_mask.shape)+[1])), (1, 1, 3))
seg_chip = np.array(chip, dtype=np.float) * color_mask
seg_chip = np.array(np.round(seg_chip), dtype=np.uint8)
return seg_chip
#kpts, desc = filter_kpts_scale(kpts, desc) df2.figure(1, doclf=True) df2.DARKEN = .5 #---- df2.imshow(rchip, plotnum=(2,3,1), title='0 before (inverse square root)') draw_kpts3(kpts0.copy(),0) #---- df2.imshow(rchip, plotnum=(2,3,2), title='1 just inverse') draw_kpts3(kpts1.copy(),1) #---- df2.imshow(rchip, plotnum=(2,3,3), title='2 identity') draw_kpts3(kpts1.copy(), 2) df2.update() #---- df2.imshow(rchip, plotnum=(2,3,4), title='3 identity') draw_kpts3(kpts1.copy(), 3) df2.update() #---- df2.imshow(rchip, plotnum=(2,3,5), title='inv sqrtm 4') draw_kpts3(kpts1.copy(), 4) df2.update() #---- df2.imshow(rchip, plotnum=(2,3,6), title='inv sqrtm 5') draw_kpts3(kpts1.copy(), 5) df2.update()
def query_last_feature():
viz.show_nearest_descriptors(hs, qcx, last_state.last_fx, df2.next_fnum())
fig3 = df2.gca()
df2.connect_callback(fig3, 'button_press_event', _click_chipres_click)
df2.update()
def _plotpts(data, px, color=df2.BLUE, label=''):
#df2.figure(9003, docla=True, pnum=(1, 1, px))
df2.plot2(data.T[0], data.T[1], '-', '', color=color, label=label)
df2.update()
def _plotpts(data, px, color=df2.BLUE, label=''):
#df2.figure(9003, docla=True, pnum=(1, 1, px))
df2.plot2(data.T[0], data.T[1], '-', '', color=color, label=label)
df2.update()
