def poc():
dset = dataset.dataset('data/basil/front')
converted = cv2.cvtColor(dset[1], cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(converted)
mask = (h > 45) & (h < 90)
mask = np.invert(mask)
v[mask] = 0
converted = cv2.merge([h, s, v])
cvutil.display_image(cv2.cvtColor(converted, cv2.COLOR_HSV2BGR))
def poc() :
dset = dataset.dataset('data/basil/front')
converted = cv2.cvtColor(dset[1], cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(converted)
mask = (h > 45) & (h < 90)
mask = np.invert(mask)
v[mask] = 0
converted = cv2.merge([h,s,v])
cvutil.display_image(cv2.cvtColor(converted, cv2.COLOR_HSV2BGR))
def poc_dense() :
dset = dataset.dataset('data/basil/front')
first = cv2.cvtColor(dset[1],cv2.COLOR_BGR2GRAY, dstCn=1)
last = cv2.cvtColor(dset[2], cv2.COLOR_BGR2GRAY, dstCn=1)
canvas = np.zeros_like(dset[1])
canvas[...,1] = 255
flow = cv2.calcOpticalFlowFarneback(first,last ,flow=None, pyr_scale=0.5,levels= 3, winsize=15, iterations=2, poly_n=5, poly_sigma=1.1, flags=0)
mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
canvas[...,0] = ang * 180/np.pi/2
canvas[...,2] = cv2.normalize(mag, None, 0, 255,cv2.NORM_MINMAX)
print(canvas)
cvutil.display_image(cv2.cvtColor(canvas, cv2.COLOR_HSV2BGR))
def poc():
image = dataset.dataset('edge')[2]
orig = np.copy(image)
checker = gen_bound_checker(image)
traces = []
cvutil.display_image(image)
for y in range(image.shape[0]):
for x in range(image.shape[1]):
if not (checker(x, y) or trace.in_any_traces(x, y)):
t = trace(x, y, checker)
t.trace_bounds()
t.fill_bounds()
traces.append(t)
dataset.print_status_bar(y * image.shape[1] + x,
image.shape[0] * image.shape[1])
print('\n coloring')
for i, t in enumerate(traces):
if len(t) < 50:
for p in t.points:
image[p.y][p.x][0] = 200
image[p.y][p.x][1] = 200
image[p.y][p.x][2] = 200
else:
randcolor = (random.choice(range(255)), random.choice(range(255)),
random.choice(range(255)))
for p in t.points:
image[p.y][p.x][0] = randcolor[0]
image[p.y][p.x][1] = randcolor[1]
image[p.y][p.x][2] = randcolor[2]
image[t.init_p.y][t.init_p.x][0] = 0
image[t.init_p.y][t.init_p.x][1] = 0
image[t.init_p.y][t.init_p.x][2] = 255
dataset.print_status_bar(i, len(traces))
cvutil.comp_images(orig, image)
def poc() :
dset = dataset.dataset('data/basil/front')
display = np.copy(dset[-1])
tracking = cv2.goodFeaturesToTrack(cv2.cvtColor(dset[1], cv2.COLOR_BGR2GRAY), maxCorners = 100, qualityLevel = 0.3, minDistance = 7, blockSize = 7)
old = tracking
for i in range(2,len(dset)) :
result, status, error = cv2.calcOpticalFlowPyrLK(dset[i - 1], dset[i], old, None, winSize=(15,15), maxLevel= 2)
old = result
for i in range(tracking.shape[0]) :
for j in range(tracking.shape[1]) :
cv2.circle(display, (old[i][j][0], old[i][j][1]), 4, (0,0,255))
cvutil.display_image(display)
def poc() :
image = dataset.dataset('edge')[2]
orig = np.copy(image)
checker = gen_bound_checker(image)
traces = []
cvutil.display_image(image)
for y in range(image.shape[0]) :
for x in range(image.shape[1]) :
if not (checker(x, y) or trace.in_any_traces(x, y)) :
t = trace(x, y, checker)
t.trace_bounds()
t.fill_bounds()
traces.append(t)
dataset.print_status_bar(y * image.shape[1] + x, image.shape[0] * image.shape[1])
print('\n coloring')
for i,t in enumerate(traces) :
if len(t) < 50 :
for p in t.points :
image[p.y][p.x][0] = 200
image[p.y][p.x][1] = 200
image[p.y][p.x][2] = 200
else :
randcolor = (random.choice(range(255)),random.choice(range(255)),random.choice(range(255)))
for p in t.points :
image[p.y][p.x][0] = randcolor[0]
image[p.y][p.x][1] = randcolor[1]
image[p.y][p.x][2] = randcolor[2]
image[t.init_p.y][t.init_p.x][0] = 0
image[t.init_p.y][t.init_p.x][1] = 0
image[t.init_p.y][t.init_p.x][2] = 255
dataset.print_status_bar(i, len(traces))
cvutil.comp_images(orig, image)
def poc() :
image = dataset.dataset('data//basil/front')[2]
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(image, 100,200)
cvutil.display_image(cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR))