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@yoyoyo-yo
yoyoyo-yo committed Aug 18, 2019
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355 changes: 230 additions & 125 deletions Question_41_50/answers/answer_44.py
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import numpy as np
import matplotlib.pyplot as plt

def Canny(img):

# Gray scale
def BGR2GRAY(img):
b = img[:, :, 0].copy()
g = img[:, :, 1].copy()
r = img[:, :, 2].copy()

# Gray scale
out = 0.2126 * r + 0.7152 * g + 0.0722 * b
out = out.astype(np.uint8)

return out


# Gaussian filter for grayscale
def gaussian_filter(img, K_size=3, sigma=1.3):

if len(img.shape) == 3:
H, W, C = img.shape
else:
img = np.expand_dims(img, axis=-1)
H, W, C = img.shape

## Zero padding
pad = K_size // 2
out = np.zeros([H + pad * 2, W + pad * 2, C], dtype=np.float)
out[pad: pad + H, pad: pad + W] = img.copy().astype(np.float)

## prepare Kernel
K = np.zeros((K_size, K_size), dtype=np.float)
for x in range(-pad, -pad + K_size):
for y in range(-pad, -pad + K_size):
K[y+pad, x+pad] = np.exp( -(x ** 2 + y ** 2) / (2 * (sigma ** 2)))
K /= (sigma * np.sqrt(2 * np.pi))
K /= K.sum()

tmp = out.copy()

# filtering
for y in range(H):
for x in range(W):
for c in range(C):
out[pad + y, pad + x, c] = np.sum(K * tmp[y: y + K_size, x: x + K_size, c])

out = out[pad: pad + H, pad: pad + W].astype(np.uint8)
out = out[..., 0]

return out


# sobel filter
def sobel_filter(img, K_size=3):
H, W = img.shape

# Zero padding
pad = K_size // 2
out = np.zeros((H + pad * 2, W + pad * 2), dtype=np.float)
out[pad: pad + H, pad: pad + W] = gray.copy().astype(np.float)
tmp = out.copy()

out_v = out.copy()
out_h = out.copy()

## Sobel vertical
Kv = [[1., 2., 1.],[0., 0., 0.], [-1., -2., -1.]]
## Sobel horizontal
Kh = [[1., 0., -1.],[2., 0., -2.],[1., 0., -1.]]

# filtering
for y in range(H):
for x in range(W):
out_v[pad + y, pad + x] = np.sum(Kv * (tmp[y: y + K_size, x: x + K_size]))
out_h[pad + y, pad + x] = np.sum(Kh * (tmp[y: y + K_size, x: x + K_size]))

out_v = np.clip(out_v, 0, 255)
out_h = np.clip(out_h, 0, 255)

out_v = out_v[pad: pad + H, pad: pad + W].astype(np.uint8)
out_h = out_h[pad: pad + H, pad: pad + W].astype(np.uint8)

return out_v, out_h


def get_edge_tan(fx, fy):
# get edge strength
edge = np.sqrt(np.power(fx.astype(np.float32), 2) + np.power(fy.astype(np.float32), 2))
edge = np.clip(edge, 0, 255)

fx = np.maximum(fx, 1e-5)
#fx[np.abs(fx) <= 1e-5] = 1e-5

# get edge angle
tan = np.arctan(fy / fx)

return edge, tan


def angle_quantization(tan):
angle = np.zeros_like(tan, dtype=np.uint8)
angle[np.where((tan > -0.4142) & (tan <= 0.4142))] = 0
angle[np.where((tan > 0.4142) & (tan < 2.4142))] = 45
angle[np.where((tan >= 2.4142) | (tan <= -2.4142))] = 95
angle[np.where((tan > -2.4142) & (tan <= -0.4142))] = 135

return angle


def non_maximum_suppression(angle, edge):
H, W = angle.shape

for y in range(H):
for x in range(W):
if angle[y, x] == 0:
dx1, dy1, dx2, dy2 = -1, 0, 1, 0
elif angle[y, x] == 45:
dx1, dy1, dx2, dy2 = -1, 1, 1, -1
elif angle[y, x] == 90:
dx1, dy1, dx2, dy2 = 0, -1, 0, 1
elif angle[y, x] == 135:
dx1, dy1, dx2, dy2 = -1, -1, 1, 1
if x == 0:
dx1 = max(dx1, 0)
dx2 = max(dx2, 0)
if x == W-1:
dx1 = min(dx1, 0)
dx2 = min(dx2, 0)
if y == 0:
dy1 = max(dy1, 0)
dy2 = max(dy2, 0)
if y == H-1:
dy1 = min(dy1, 0)
dy2 = min(dy2, 0)
if max(max(edge[y, x], edge[y+dy1, x+dx1]), edge[y+dy2, x+dx2]) != edge[y, x]:
edge[y, x] = 0

return edge

def hysterisis(edge, HT=100, LT=30):
H, W = edge.shape

# Histeresis threshold
edge[edge >= HT] = 255
edge[edge <= LT] = 0

_edge = np.zeros((H+2, W+2), dtype=np.float32)
_edge[1:H+1, 1:W+1] = edge

## 8 - Nearest neighbor
nn = np.array(((1., 1., 1.), (1., 0., 1.), (1., 1., 1.)), dtype=np.float32)

for y in range(1, H+2):
for x in range(1, W+2):
if _edge[y, x] < LT or _edge[y, x] > HT:
continue
if np.max(_edge[y-1:y+2, x-1:x+2] * nn) >= HT:
_edge[y, x] = 255
else:
_edge[y, x] = 0

edge = _edge[1:H+1, 1:W+1]

return edge

# grayscale
gray = BGR2GRAY(img)

# gaussian filtering
gaussian = gaussian_filter(gray, K_size=5, sigma=1.4)

# sobel filtering
fy, fx = sobel_filter(gaussian, K_size=3)

# get edge strength, angle
edge, tan = get_edge_tan(fx, fy)

# angle quantization
angle = angle_quantization(tan)

# non maximum suppression
edge = non_maximum_suppression(angle, edge)

# hysterisis threshold
out = hysterisis(edge)

return out


def Hough_Line_step1(edge):
## Voting
def voting(edge):
H, W = edge.shape
drho = 1
dtheta = 1

# get rho max length
rho_max = np.ceil(np.sqrt(H ** 2 + W ** 2)).astype(np.int)

# hough table
hough = np.zeros((rho_max, 180), dtype=np.int)

# get index of edge
ind = np.where(edge == 255)

## hough transformation
for y, x in zip(ind[0], ind[1]):
for theta in range(0, 180, dtheta):
# get polar coordinat4s
t = np.pi / 180 * theta
rho = int(x * np.cos(t) + y * np.sin(t))

# vote
hough[rho, theta] += 1

out = hough.astype(np.uint8)

return out

# voting
out = voting(edge)

return out


# Read image
img = cv2.imread("thorino.jpg").astype(np.float32)
H, W, C = img.shape

# Gray
gray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]

# Gaussian Filter
K_size = 5
sigma = 1.4

## Zero padding
pad = K_size // 2
gau = np.zeros((H + pad*2, W + pad*2), dtype=np.float32)
#gau[pad:pad+H, pad:pad+W] = gray.copy().astype(np.float32)
gau = np.pad(gray, (pad, pad), 'edge')
tmp = gau.copy()

## Kernel
K = np.zeros((K_size, K_size), dtype=np.float32)
for x in range(-pad, -pad+K_size):
for y in range(-pad, -pad+K_size):
K[y+pad, x+pad] = np.exp( -(x**2 + y**2) / (2* (sigma**2)))
K /= (sigma * np.sqrt(2 * np.pi))
K /= K.sum()

for y in range(H):
for x in range(W):
gau[pad+y, pad+x] = np.sum(K * tmp[y:y+K_size, x:x+K_size])

## Sobel vertical
KSV = np.array(((-1., -2., -1.), (0., 0., 0.), (1., 2., 1.)), dtype=np.float32)
## Sobel horizontal
KSH = np.array(((-1., 0., 1.), (-2., 0., 2.), (-1., 0., 1.)), dtype=np.float32)

gau = gau[pad-1:H+pad+1, pad-1:W+pad+1]
fy = np.zeros_like(gau, dtype=np.float32)
fx = np.zeros_like(gau, dtype=np.float32)
K_size = 3
pad = K_size // 2

for y in range(H):
for x in range(W):
fy[pad+y, pad+x] = np.sum(KSV * gau[y:y+K_size, x:x+K_size])
fx[pad+y, pad+x] = np.sum(KSH * gau[y:y+K_size, x:x+K_size])

fx = fx[pad:pad+H, pad:pad+W]
fy = fy[pad:pad+H, pad:pad+W]

# Non-maximum suppression
edge = np.sqrt(np.power(fx, 2) + np.power(fy, 2))
fx[fx == 0] = 1e-5
tan = np.arctan(fy / fx)
## Angle quantization
angle = np.zeros_like(tan, dtype=np.uint8)
angle[np.where((tan > -0.4142) & (tan <= 0.4142))] = 0
angle[np.where((tan > 0.4142) & (tan < 2.4142))] = 45
angle[np.where((tan >= 2.4142) | (tan <= -2.4142))] = 95
angle[np.where((tan > -2.4142) & (tan <= -0.4142))] = 135

for y in range(H):
for x in range(W):
if angle[y, x] == 0:
dx1, dy1, dx2, dy2 = -1, 0, 1, 0
elif angle[y, x] == 45:
dx1, dy1, dx2, dy2 = -1, 1, 1, -1
elif angle[y, x] == 90:
dx1, dy1, dx2, dy2 = 0, -1, 0, 1
elif angle[y, x] == 135:
dx1, dy1, dx2, dy2 = -1, -1, 1, 1
if x == 0:
dx1 = max(dx1, 0)
dx2 = max(dx2, 0)
if x == W-1:
dx1 = min(dx1, 0)
dx2 = min(dx2, 0)
if y == 0:
dy1 = max(dy1, 0)
dy2 = max(dy2, 0)
if y == H-1:
dy1 = min(dy1, 0)
dy2 = min(dy2, 0)
if max(max(edge[y, x], edge[y+dy1, x+dx1]), edge[y+dy2, x+dx2]) != edge[y, x]:
edge[y, x] = 0


# Histeresis threshold
HT = 100
LT = 30
edge[edge >= HT] = 255
edge[edge <= LT] = 0

_edge = np.zeros((H+2, W+2), dtype=np.float32)
_edge[1:H+1, 1:W+1] = edge

## 8 - Nearest neighbor
nn = np.array(((1., 1., 1.), (1., 0., 1.), (1., 1., 1.)), dtype=np.float32)

for y in range(1, H+2):
for x in range(1, W+2):
if _edge[y, x] < LT or _edge[y, x] > HT:
continue
if np.max(_edge[y-1:y+2, x-1:x+2] * nn) >= HT:
_edge[y, x] = 255
else:
_edge[y, x] = 0

edge = _edge[1:H+1, 1:W+1].astype(np.uint8)

## Canny finish

# Canny
edge = Canny(img)

# Hough
out = Hough_Line_step1(edge)

out = out.astype(np.uint8)

## Voting
drho = 1
dtheta = 1
rho_max = np.ceil(np.sqrt(H**2 + W**2)).astype(np.int)
hough = np.zeros((rho_max, 180), dtype=np.int)

ind = np.where(edge == 255)

## hough transformation
for y, x in zip(ind[0], ind[1]):
for theta in range(0, 180, dtheta):
t = np.pi / 180 * theta
rho = int(x * np.cos(t) + y * np.sin(t))
hough[rho, theta] += 1

out = hough.astype(np.uint8)

# Save result
cv2.imwrite("out.jpg", out)
cv2.imshow("result", out)
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