def draw_court_lines_from_detections(image,
boxes,
classes,
scores,
min_score_thresh=.5):
# first we have to find the corners. in (x,y) format
image_height, image_witdh, channels = image.shape
corners_points = []
for i in range(boxes.shape[0]):
if scores is None or scores[i] > min_score_thresh:
box = tuple(boxes[i].tolist())
ymin, xmin, ymax, xmax = box
ymin = ymin * image_height
ymax = ymax * image_height
xmax = xmax * image_witdh
xmin = xmin * image_witdh
corners_points.append((int(
(xmax + xmin) / 2), int((ymax + ymin) / 2)))
# now we are going to paint de points.
image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
draw = ImageDraw.Draw(image_pil)
for p in corners_points:
r = 2
draw.ellipse((720 - r, 100 - r, 720 + r, 100 + r), fill=(255, 0, 0))
draw.ellipse((p[0] - r, p[1] - r, p[0] + r, p[1] + r),
fill=(255, 0, 0))
draw.point(p, fill="yellow")
def print_court_polylines(best_homography_matrix):
for line in BadmintonCourt.court_lines():
pts = np.float32([line]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, best_homography_matrix)
draw.line([(dst[0][0][0], dst[0][0][1]),
(dst[1][0][0], dst[1][0][1])],
fill="red")
if len(corners_points) == 4:
# find homography matrix
court = BadmintonCourt()
ground_truth_corners = court.court_external_4_corners()
src_pts = np.array(ground_truth_corners, np.float32)
# puede que tengan que tener el mismo orden
cp = corners_points
# TODO test si puedo cambiar la lista directamente.
homography_candidates = order_corner_points_for_homography(
corners_points)
dst_pts = np.array(homography_candidates, np.float32)
M, mask = cv2.findHomography(src_pts, dst_pts)
print_court_polylines(M)
homography_ok_points = 0
np.copyto(image, np.array(image_pil))
def print_court_polylines(best_homography_matrix):
for line in BadmintonCourt.court_lines():
pts = np.float32([line]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, best_homography_matrix)
draw.line([(dst[0][0][0], dst[0][0][1]),
(dst[1][0][0], dst[1][0][1])],
fill="red")
def print_court_polylines(best_homography_matrix, sufix):
#h = 1340
#w = 610
img = cv2.imread('C:\\TFM\\imagenes\\finalRioWS-35880-897.jpeg')
for line in BadmintonCourt.court_lines():
pts = np.float32([line]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, best_homography_matrix)
cv2.line(img, (dst[0][0][0], dst[0][0][1]),
(dst[1][0][0], dst[1][0][1]), 255, 3)
#img2 = cv2.polylines(img, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)
cv2.imwrite(
"C:\\TFM\\ws1\\test_final_alg\\results\\final_court_finder_lines{}.jpg"
.format(sufix), img, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
from model.badminton.court_model import BadmintonCourt
import numpy as np
import cv2
if __name__ == "__main__":
img = cv2.imread('C:\\TFM\\imagenes\\finalRioWS-35880-897.jpeg')
c = BadmintonCourt()
ground_truth_corners = c.court_external_4_corners()
fixed_points_example = [(620, 498), (369, 1019), (1548, 1019), (1288, 498)]
src_pts = np.array(ground_truth_corners, np.float32)
dst_pts = np.array(fixed_points_example, np.float32)
M, mask = cv2.findHomography(src_pts, dst_pts)
matchesMask = mask.ravel().tolist()
h = 1340
w = 610
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1],
[w - 1, 0]]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, M)
img2 = cv2.polylines(img, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)
cv2.imwrite("C:\\TFM\\ws1\\test_final_alg\\final_court_example_finder.jpg",
img2, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
def main():
img = cv2.imread('C:\\TFM\\imagenes\\finalRioWS-35880-897.jpeg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh3 = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
cv2.imwrite("C:\\TFM\\ws1\\test_final_alg\\threshold.jpg", thresh3,
[int(cv2.IMWRITE_JPEG_QUALITY), 100])
edges = cv2.Canny(thresh3, 100, 200, apertureSize=3)
# cv2.imwrite("C:\\TFM\\ws1\\test_final_alg\\canny.jpg", edges, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
"""
line ecuation: y = m*x +n
polar:
punto(3,6): indica que estamos a una distancia de 3 desde el origen de coordenadas con un angulo de 6
p = x * cos(o) + y* sin(o)
p =
"""
lines = cv2.HoughLines(thresh3, 1, np.pi / 180, 400)
line_equations = []
shape = img.shape
img_rows = shape[0]
img_cols = shape[1]
for line in lines:
for rho, theta in line:
a = np.cos(theta)
b = np.sin(theta)
#line_equations.append([[a, b], rho])
line_equations.append(Line(a, b, rho))
# calculate intersection for all lines
# se puede hacer también con dos bucles for.
combinations = itertools.combinations(line_equations, 2)
intersections = []
for combination in combinations:
try:
intersections.append(
LineIntersection(combination[0], combination[1], img_cols,
img_rows))
except:
# parallel lines or out of image.
pass
#### HOMOGRAPHY
homography_candidates_ini = itertools.combinations(intersections, 4)
homography_candidates_filter = []
sum = 0
area_threshold = 150000
print(time.asctime(time.localtime(time.time())))
for candidates_set in homography_candidates_ini:
sum += 1
if sum % 100000 == 0:
print(sum)
intersections_line_set = set()
for intersecion in candidates_set:
intersections_line_set.add(intersecion.line_1)
intersections_line_set.add(intersecion.line_2)
#print(len(intersections_line_set))
if len(intersections_line_set) != 4:
continue
valid_candidate = True
candidates_set_points = [
candidates_set[0].intersection, candidates_set[1].intersection,
candidates_set[2].intersection, candidates_set[3].intersection
]
triangles = itertools.combinations(candidates_set_points, 3)
for triangle in triangles:
area = triangle_area(triangle[0], triangle[1], triangle[2])
if area < area_threshold:
valid_candidate = False
break
if valid_candidate:
homography_candidates_filter.append(candidates_set_points)
print(time.asctime(time.localtime(time.time())))
print(sum)
print(len(homography_candidates_filter))
# homography
min_error = None
best_homography_matrix = None
c = BadmintonCourt()
ground_truth_corners = c.court_external_4_corners()
homography_checked = 0
intersection_points = []
for line_intersection in intersections:
intersection_points.append(line_intersection.intersection)
for homography_candidates in homography_candidates_filter:
homography_checked += 1
print(homography_checked)
src_pts = np.array(ground_truth_corners, np.float32)
dst_pts = np.array(homography_candidates, np.float32)
M, mask = cv2.findHomography(src_pts, dst_pts)
pts = np.array(c.court_medium_corners(), np.float32).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, M)
homography_err = 0
for dst_h in dst:
min_dst = None
for dst_r in intersection_points:
d = euqlidean_distance(dst_h, dst_r)
if min_dst == None or d < min_dst:
min_dst = d
homography_err += min_dst
print_court_polylines(M, str(homography_checked))
if min_error == None or homography_err < min_error:
min_error = homography_err
print(min_error)
best_homography_matrix = M
print(time.asctime(time.localtime(time.time())))
print_court_polylines(best_homography_matrix, "final")