def poc2():
dset = dataset.dataset('data/basil/front', dtype=float)
#h, s, v = cv2.split(cv2.cvtColor(dset[1], cv2.COLOR_BGR2HSV))
past = np.copy(dset[1])
current = np.copy(dset[2])
future = np.copy(dset[3])
kernel = np.array([[0, 0, 0], [0, 18, 0], [0, 0, 0]], dtype=float)
time_kernel = np.array([[-1, -1, -1], [-1, -1, -1], [-1, -1, -1]],
dtype=float)
past = cv2.filter2D(past, -1, time_kernel)
current = cv2.filter2D(current, -1, kernel)
future = cv2.filter2D(future, -1, time_kernel)
result = past + current + future
mask = ((result > 100) | (result < -100))
result[mask] = 0
mask = (result != 0)
result[mask] = 255
mask = ((result[:, :, 0] == 0) & (result[:, :, 1] == 0) &
(result[:, :, 2] == 0))
mask = np.invert(mask)
result[mask] = 255
dset.set_dtype(np.uint8)
cvutil.comp_images(dset[2], result.astype(np.uint8))
def poc() :
dset = dataset.dataset('data/basil/front')
#h, s, v = cv2.split(cv2.cvtColor(dset[1], cv2.COLOR_BGR2HSV))
result = np.copy(dset[1])
kernel = np.array([[0,-1,0],
[-1,5,-1],
[0,-1,0]], dtype=float)
#kernel *= 1/(kernel.shape[0] * kernel.shape[1])
#for p in (b, g, r) :
# p[...] = ndimage.correlate(p, kernel)
# p[...] = np.clip(p, 0, 255)
#v = ndimage.convolve(v, kernel)
result = cv2.filter2D(result, -1, kernel)
#v = np.clip(v, 0, 255)
#result = cv2.merge((h, s, v))
#result = cv2.cvtColor(result, cv2.COLOR_HSV2BGR)
cvutil.comp_images(dset[1], result)
def pofconcept():
dset = dataset.dataset('data/basil/front')
buffer = np.copy(dset[2])
buffer -= dset[1]
buffer2 = np.copy(dset[2])
buffer2 = buffer2.astype(float)
buffer2 -= dset[1]
bmask = ((buffer2 > 0) & (buffer2 < 255))
buffer2[bmask] = 0
bmask = (buffer2 != 0)
buffer2[bmask] = 255
bmask = (buffer2[:, :, 0] == 255) & (buffer2[:, :, 1]
== 255) & (buffer2[:, :, 2] == 255)
bmask = np.invert(bmask)
buffer2[bmask] = 0
cvutil.comp_images(buffer, buffer2.astype(np.uint8))
def main():
dset = dataset.dataset('data/basil/front')
for i in range(len(dset) - 1, 0, -1):
dset[i] = dset[i].astype(float)
dset[i] -= dset[i - 1]
threshold(dset[i])
dset[i] = dset[i].astype(np.uint8)
cvutil.comp_images(dset[1], dset[2])
dset.write_images('movement')
def main() :
dset = dataset.dataset('data/basil/front')
for i in range(len(dset) - 1, 0, -1) :
dset[i] = dset[i].astype(float)
dset[i] -= dset[i - 1]
threshold(dset[i])
dset[i] = dset[i].astype(np.uint8)
cvutil.comp_images(dset[1], dset[2])
dset.write_images('movement')
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 poc2() :
dset = dataset.dataset('data/basil/front', dtype=float)
#h, s, v = cv2.split(cv2.cvtColor(dset[1], cv2.COLOR_BGR2HSV))
past = np.copy(dset[1])
current = np.copy(dset[2])
future = np.copy(dset[3])
kernel = np.array([[0,0,0],
[0,18,0],
[0,0,0]], dtype=float)
time_kernel = np.array([[-1,-1,-1],
[-1,-1,-1],
[-1,-1,-1]], dtype=float)
past = cv2.filter2D(past, -1, time_kernel)
current = cv2.filter2D(current, -1, kernel)
future = cv2.filter2D(future, -1, time_kernel)
result = past + current + future
mask = ((result > 100) | (result < -100))
result[mask] = 0
mask = (result != 0)
result[mask] = 255
mask = ((result[:,:,0] == 0) & (result[:,:,1] == 0) & (result[:,:,2] == 0))
mask = np.invert(mask)
result[mask] = 255
dset.set_dtype(np.uint8)
cvutil.comp_images(dset[2], result.astype(np.uint8))
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')
#h, s, v = cv2.split(cv2.cvtColor(dset[1], cv2.COLOR_BGR2HSV))
result = np.copy(dset[1])
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], dtype=float)
#kernel *= 1/(kernel.shape[0] * kernel.shape[1])
#for p in (b, g, r) :
# p[...] = ndimage.correlate(p, kernel)
# p[...] = np.clip(p, 0, 255)
#v = ndimage.convolve(v, kernel)
result = cv2.filter2D(result, -1, kernel)
#v = np.clip(v, 0, 255)
#result = cv2.merge((h, s, v))
#result = cv2.cvtColor(result, cv2.COLOR_HSV2BGR)
cvutil.comp_images(dset[1], result)
def pofconcept() :
dset = dataset.dataset('data/basil/front')
buffer = np.copy(dset[2])
buffer -= dset[1]
buffer2 = np.copy(dset[2])
buffer2 = buffer2.astype(float)
buffer2 -= dset[1]
bmask = ((buffer2 > 0) & (buffer2 < 255))
buffer2[bmask] = 0
bmask = (buffer2 != 0)
buffer2[bmask] = 255
bmask = (buffer2[:,:,0] == 255) & (buffer2[:,:,1] == 255) & (buffer2[:,:,2] == 255)
bmask = np.invert(bmask)
buffer2[bmask] = 0
cvutil.comp_images(buffer, buffer2.astype(np.uint8))
def poc():
dset = dataset.vidset('data/vid_top.avi')
image = np.copy(dset[1]).astype(float)
antia = np.array([[1, 2, 4, 2, 1], [2, 4, 8, 4, 2], [4, 8, 16, 8, 4],
[2, 4, 8, 4, 2], [1, 2, 4, 2, 1]],
dtype=float)
big_antia = np.array([[1, 2, 4, 8, 4, 2, 1], [2, 4, 8, 16, 8, 4, 2],
[4, 8, 16, 32, 16, 8, 4], [8, 16, 32, 64, 32, 16, 8],
[4, 8, 16, 32, 16, 8, 4], [2, 4, 8, 16, 8, 4, 2],
[1, 2, 4, 8, 4, 2, 1]],
dtype=float)
med = np.array([[1, 1, 1], [1, 0, 1], [1, 1, 1]], dtype=float)
big_med = np.array([
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
],
dtype=float)
sharp = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], dtype=float)
big_sharp = np.array([
[-1, 0, 0, -1, 0, 0, -1],
[0, -1, 0, -1, 0, -1, 0],
[0, 0, -1, -1, -1, 0, 0],
[-1, -1, -1, 25, -1, -1, -1],
[0, 0, -1, -1, -1, 0, 0],
[0, -1, 0, -1, 0, -1, 0],
[-1, 0, 0, -1, 0, 0, -1],
],
dtype=float)
med *= (1 / 8)
big_med *= (1 / 48)
antia *= (1 / 104)
big_antia *= (1 / np.sum(big_antia))
sharp_image = cv2.filter2D(image, -1, big_sharp)
blur_image = cv2.filter2D(image, -1, big_med)
sharp_image = np.clip(sharp_image, 0, 255)
blur_image = np.clip(blur_image, 0, 255)
cvutil.comp_images(sharp_image.astype(np.uint8),
blur_image.astype(np.uint8))
result1 = dset[1] - blur_image
result1[result1 < -3] = 0
result1[result1 > 3] = 0
result1[result1 != 0] = 255
mask = (result1[..., 0] == 0) & (result1[..., 1] == 0) & (result1[..., 2]
== 0)
mask = np.invert(mask)
result1[mask] = 255
result2 = sharp_image - blur_image
result2[result2 < -40] = 0
result2[result2 > 40] = 0
result2[result2 != 0] = 255
mask = (result2[..., 0] == 0) & (result2[..., 1] == 0) & (result2[..., 2]
== 0)
mask = np.invert(mask)
result2[mask] = 255
cvutil.comp_images(result1.astype(np.uint8), result2.astype(np.uint8))
def poc() :
dset = dataset.vidset('data/vid_top.avi')
image = np.copy(dset[1]).astype(float)
antia = np.array( [[1, 2, 4, 2 ,1],
[2, 4, 8, 4, 2],
[4, 8, 16, 8, 4],
[2, 4, 8, 4, 2],
[1, 2, 4, 2, 1]], dtype=float)
big_antia = np.array( [[1, 2, 4, 8 ,4, 2, 1],
[2, 4, 8, 16, 8, 4, 2],
[4, 8, 16, 32, 16, 8, 4],
[8, 16, 32, 64, 32, 16, 8],
[4, 8, 16, 32, 16, 8, 4],
[2, 4, 8, 16, 8, 4, 2],
[1, 2, 4, 8 ,4, 2, 1]], dtype=float)
med = np.array( [ [1,1,1],
[1,0,1],
[1,1,1]], dtype=float)
big_med = np.array( [ [1, 1, 1, 1 ,1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1], ], dtype = float)
sharp = np.array( [ [0,-1,0],
[-1,5,-1],
[0,-1,0]], dtype=float)
big_sharp = np.array( [ [-1, 0, 0, -1 ,0, 0, -1],
[0, -1, 0, -1, 0, -1, 0],
[0, 0, -1, -1, -1, 0, 0],
[-1, -1, -1, 25, -1, -1, -1],
[0, 0, -1, -1, -1, 0, 0],
[0, -1, 0, -1, 0, -1, 0],
[-1, 0, 0, -1, 0, 0, -1], ], dtype = float)
med *= (1/8)
big_med *= (1/48)
antia *= (1/104)
big_antia *= (1/np.sum(big_antia))
sharp_image = cv2.filter2D(image, -1, big_sharp)
blur_image = cv2.filter2D(image, -1, big_med)
sharp_image = np.clip(sharp_image, 0, 255)
blur_image = np.clip(blur_image, 0, 255)
cvutil.comp_images(sharp_image.astype(np.uint8), blur_image.astype(np.uint8))
result1 = dset[1] - blur_image
result1[result1 < -3] = 0
result1[result1 > 3] = 0
result1[result1 != 0] = 255
mask = (result1[...,0] == 0) & (result1[...,1] == 0) & (result1[...,2] == 0)
mask = np.invert(mask)
result1[mask] = 255
result2 = sharp_image - blur_image
result2[result2 < -40] = 0
result2[result2 > 40] = 0
result2[result2 != 0] = 255
mask = (result2[...,0] == 0) & (result2[...,1] == 0) & (result2[...,2] == 0)
mask = np.invert(mask)
result2[mask] = 255
cvutil.comp_images(result1.astype(np.uint8), result2.astype(np.uint8))