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250 changes: 179 additions & 71 deletions ransac.py
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Original file line number Diff line number Diff line change
@@ -1,71 +1,179 @@
import sys, os.path, json, numpy as np


def generate_data(
img_size: tuple, line_params: tuple,
n_points: int, sigma: float, inlier_ratio: float
) -> np.ndarray:
pass # insert your code here


def compute_ransac_threshold(
alpha: float, sigma: float
) -> float:
pass # insert your code here


def compute_ransac_iter_count(
conv_prob: float, inlier_ratio: float
) -> int:
pass # insert your code here


def compute_line_ransac(
data: np.ndarray, threshold: float, iter_count: int
) -> tuple:
pass # insert your code here


def detect_line(params: dict) -> tuple:
data = generate_data(
(params['w'], params['h']),
(params['a'], params['b'], params['c']),
params['n_points'], params['sigma'], params['inlier_ratio']
)
threshold = compute_ransac_threshold(
params['alpha'], params['sigma']
)
iter_count = compute_ransac_iter_count(
params['conv_prob'], params['inlier_ratio']
)
detected_line = compute_line_ransac(data, threshold, iter_count)
return detected_line


def main():
assert len(sys.argv) == 2
params_path = sys.argv[1]
assert os.path.exists(params_path)
with open(params_path) as fin:
params = json.load(fin)
assert params is not None

"""
params:
line_params: (a,b,c) - line params (ax+by+c=0)
img_size: (w, h) - size of the image
n_points: count of points to be used

sigma - Gaussian noise
alpha - probability of point is an inlier

inlier_ratio - ratio of inliers in the data
conv_prob - probability of convergence
"""

detected_line = detect_line(params)
print(detected_line)


if __name__ == '__main__':
main()
import sys, os.path, json, random, math, cv2, numpy as np


def generate_data(
img_size: tuple, line_params: tuple,
n_points: int, sigma: float, inlier_ratio: float
) -> np.ndarray:
img = np.zeros((img_size[0], img_size[1]), dtype=int)
data = np.zeros((n_points,3),dtype= int)
inp = int(round(n_points * inlier_ratio))
A = line_params[0]
B = -line_params[1]
C = -line_params[2]
Pepe = 0

x0,y0,x1,y1 = 0,0,0,0
if C * A < 0:
x0 = 0
y0 = C/B
elif C * A > 0:
y0 = 0
x0 = -C/A
else:
x0 = 0
y0 = 0


if (B * img_size[0] + C) / A < img_size[1]:
y1 = img_size[0]
x1 = (B * img_size[0] + C) / A

elif (A * img_size[1] + C) / B < img_size[0]:
x1 = img_size[1]
y1 = (A * img_size[1] + C) / B
else:
x1 = img_size[1] - 1
y1 = img_size[0] - 1
if (A < 0 and B > 0) or (A > 0 and B < 0):
x1 = C/B

for i in range(inp):
rhek = int(round(random.random() * abs(x1 - x0)))
sig = int(round(random.random() * abs(2 * sigma) - sigma))
X = rhek + x0
Y = X*(A/B) + C
X = X + sig
if X >= x1:
X = x1 - 1
if X <= x0:
X = x0 + 1
if Y >= img_size[0]:
Y = img_size[0] - 1
if Y <= 0:
Y = 1
data[Pepe][0] = int(Y)
data[Pepe][1] = int(X)
Pepe += 1

img[int(Y)][int(X)] = 255
G = ((img_size[0] * img_size[1]) / (n_points - inp))
for i in range((n_points - inp)):
G1 = int((random.random() * G))
img[round(i * G + G1) // img_size[1]][round(i * G + G1) % img_size[1]] = 255
data[Pepe][0] = round(i * G + G1) // img_size[1]
data[Pepe][1] = round(i * G + G1) % img_size[1]
Pepe += 1
cv2.imwrite('out.png', img)
return data




def compute_ransac_threshold(
alpha: float, sigma: float
) -> float:
return abs(math.sqrt(alpha * sigma**2))


def compute_ransac_iter_count(
conv_prob: float, inlier_ratio: float
) -> int:
N = math.log(0.05)/math.log(1 - inlier_ratio ** conv_prob)
return N


def compute_line_ransac(
data: np.ndarray, threshold: float, iter_count: int, N, N2
) -> tuple:

NN = N
NNN = int(round(NN * N2))
max, MAX = 0, 0
A, AA = 0, 0
B, BB = 0, 0
C, CC = 0, 0
for i in range(iter_count):
preflop = np.zeros(NNN)
tt = 0
while tt < NNN:
RR = int(round(random.random() * NN)) - 1
if data[RR][2] != i + 1:
preflop[tt] = RR
data[RR][2] = i + 1
tt += 1
MAX = 0
for h in range(iter_count):
RR0 = int(round(random.random() * NNN))
RR1 = RR0
while (RR1 == RR0):
RR1 = int(round(random.random() * NNN))
A0 = data[RR1][0] - data[RR0][0]
B0 = -(data[RR1][1] - data[RR0][1])
C0 = -data[RR0][0] * A0 - data[RR0][1] * B0
counter = 0
for k in range(NNN):
KKona = int(preflop[k])

if (abs(A0 * data[KKona][1] + B0 * data[KKona][0] + C0) / math.sqrt(A0 ** 2 + B0 ** 2)) < threshold:
counter += 1

if (counter > MAX):
MAX = counter
AA = A0
BB = B0
CC = C0
counter = 0
for k in range(NN):
if (abs(AA * data[k][1] + BB * data[k][0] + CC) / math.sqrt(AA ** 2 + BB ** 2)) < threshold:
counter += 1
if counter > max:
A = AA
B = BB
C = CC
return[A, B, C]


def detect_line(params: dict) -> tuple:
data = generate_data(
(params['w'], params['h']),
(params['a'], params['b'], params['c']),
params['n_points'], params['sigma'], params['inlier_ratio']
)
threshold = compute_ransac_threshold(
params['alpha'], params['sigma']
)
iter_count = round(compute_ransac_iter_count(
params['conv_prob'], params['inlier_ratio']
))


detected_line = compute_line_ransac(data, threshold, iter_count, params['n_points'], params['inlier_ratio'])
return detected_line


def main():
assert len(sys.argv) == 2
params_path = sys.argv[1]
assert os.path.exists(params_path)
with open(params_path) as fin:
params = json.load(fin)
assert params is not None

"""
params:
line_params: (a,b,c) - line params (ax+by+c=0)
img_size: (w, h) - size of the image
n_points: count of points to be used
sigma - Gaussian noise
alpha - probability of point is an inlier
inlier_ratio - ratio of inliers in the data
conv_prob - probability of convergence
"""

detected_line = detect_line(params)
print(detected_line)


if __name__ == '__main__':
main()