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corner.py
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import numpy as np
from cv2 import cv2
import os
import math
import sys
from yolo6D.utils import get_camera_intrinsic
from Kmeans import Cluster
import copy
from skimage import morphology
def draw(img, corner, imgpts):
'''绘制坐标系'''
img = cv2.line(img, corner, tuple(imgpts[0].ravel()), (255, 0, 0), 3)
cv2.putText(img, "X", tuple(
imgpts[0].ravel()), cv2.FONT_HERSHEY_COMPLEX, 0.5, (100, 149, 237), 2)
img = cv2.line(img, corner, tuple(imgpts[1].ravel()), (0, 255, 0), 3)
cv2.putText(img, "Y", tuple(imgpts[1].ravel()),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 127), 2)
img = cv2.line(img, corner, tuple(imgpts[2].ravel()), (0, 0, 255), 3)
cv2.putText(img, "Z", tuple(imgpts[2].ravel()),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 140, 0), 2)
return img
def getcrosspoint(rho1, theta1, rho2, theta2):
cos_1 = np.cos(theta1)
sin_1 = np.sin(theta1)
K1 = -(cos_1 / sin_1)
cos_2 = np.cos(theta2)
sin_2 = np.sin(theta2)
K2 = -(cos_2 / sin_2)
x_1 = cos_1 * rho1
y_1 = sin_1 * rho1
x_2 = cos_2 * rho2
y_2 = sin_2 * rho2
b_1 = -(K1 * x_1) + y_1
b_2 = -(K2 * x_2) + y_2
corss_x = (b_1 - b_2)/(K2 - K1)
cross_y = K1 * corss_x + b_1
corss_x = int(corss_x)
cross_y = int(cross_y)
# print(corss_x, cross_y)
return (corss_x, cross_y)
def thres(img, x):
'''二值化'''
red_low = x[0]
red_high = x[1]
green_low = x[2]
green_high = x[3]
blue_low = x[4]
blue_high = x[5]
height, width = img.shape[:2] #j:高 i:宽
im_new = np.zeros((height, width, 1), np.uint8) #粗糙的二值化图像
for i in range(width):
for j in range(height):
judger = 1
temp = img[j, i] #j:高 i:宽
if temp[0] < red_low or temp[0] > red_high:
judger = 0
if temp[1] < green_low or temp[1] > green_high:
judger = 0
if temp[2] < blue_low or temp[2] > blue_high:
judger = 0
if judger:
im_new[j, i] = 255 #j:高 i:宽
return im_new
def remove_small_objects(img):
kernel = np.ones((5, 5), np.uint8)
erosion = cv2.erode(img, kernel, iterations = 1)
# erosion = cv2.dilate(erosion,kernel,iterations = 1)
erosion = erosion > 127
# opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
erosion = morphology.remove_small_objects(erosion, min_size=500, connectivity=1) #0 1
chull = morphology.convex_hull_image(erosion)
chull = chull.astype(np.uint8)
chull *= 255
return chull
def square_canny(img, canny):
img = cv2.resize(img, (640, 480), interpolation=cv2.INTER_CUBIC)
# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# threshold
# lower_gray = np.array([140])
# upper_gray = np.array([200])
# test_gray = cv2.inRange(gray,lower_gray,upper_gray)
kernel = np.ones((9, 9), np.uint8)
test_gray = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
test_gray = cv2.morphologyEx(test_gray, cv2.MORPH_OPEN, kernel)
kernel2 = np.ones((9, 9), np.uint8)
test_gray = cv2.morphologyEx(test_gray, cv2.MORPH_CLOSE, kernel2)
edges = cv2.Canny(test_gray, canny[0], canny[1], canny[2])
return edges
def square_line(origin, edges, hough):
'''
输入：原始图片，黑白边线图，hough数组
输出：带坐标的原图，Table_2D
'''
internal_calibration = get_camera_intrinsic()
internal_calibration = np.array(internal_calibration, dtype='float32')
distCoeffs = np.zeros((8, 1), dtype='float32')
img = copy.copy(origin)
lines = cv2.HoughLines(edges, hough[0], hough[1], hough[2])
# rho：ρ，图片左上角向直线所做垂线的长度
# theta：Θ，图片左上角向直线所做垂线与顶边夹角
# 垂足高于原点时，ρ为负，Θ为垂线关于原点的对称线与顶边的夹角
top_line_theta = []
top_line_rho = []
left_line_theta = []
left_line_rho = []
right_line_theta = []
right_line_rho = []
bottom_line_theta = []
bottom_line_rho = []
horizon = []
summ = 0
final_lines = np.zeros((4, 2))
if len(lines) < 4:
print("\033[0;31m未检测到方桌！\033[0m")
return edges
else:
for line in lines:
for rho, theta in line:
if (theta > math.pi / 3 and theta < math.pi * 2 / 3): # 横线
horizon.append(line)
elif rho < 0: # 右边
right_line_rho.append(rho)
right_line_theta.append(theta)
else: # 左边
left_line_theta.append(theta)
left_line_rho.append(rho)
top, bottom = Cluster(horizon, 180, 360) # 将横线依据abs(rho)分为上下
for line in top:
for rho, theta in line:
top_line_rho.append(rho)
top_line_theta.append(theta)
for line in bottom:
for rho, theta in line:
bottom_line_rho.append(rho)
bottom_line_theta.append(theta)
for i in right_line_theta:
summ += i
right_line_theta_average = summ / len(right_line_theta)
final_lines[0, 1] = right_line_theta_average
summ = 0
for i in right_line_rho:
summ += i
right_line_rho_average = summ / len(right_line_rho)
final_lines[0, 0] = right_line_rho_average
summ = 0
for i in left_line_theta:
summ += i
left_line_theta_average = summ / len(left_line_theta)
final_lines[1, 1] = left_line_theta_average
summ = 0
for i in left_line_rho:
summ += i
left_line_rho_average = summ / len(left_line_rho)
final_lines[1, 0] = left_line_rho_average
summ = 0
for i in top_line_theta:
summ += i
top_line_theta_average = summ / len(top_line_theta)
final_lines[2, 1] = top_line_theta_average
summ = 0
for i in top_line_rho:
summ += i
top_line_rho_average = summ / len(top_line_rho)
final_lines[2, 0] = top_line_rho_average
summ = 0
for i in bottom_line_theta:
summ += i
bottom_line_theta_average = summ / len(bottom_line_theta)
final_lines[3, 1] = bottom_line_theta_average
summ = 0
for i in bottom_line_rho:
summ += i
bottom_line_rho_average = summ / len(bottom_line_rho)
final_lines[3, 0] = bottom_line_rho_average
summ = 0
# print(final_lines)
final_lines = np.array(final_lines)
for i in range(4):
theta = final_lines[i, 1]
rho = final_lines[i, 0]
a = np.cos(theta)
b = np.sin(theta)
x0 = a*rho
y0 = b*rho
x1 = int(x0 + 1000*(-b))
y1 = int(y0 + 1000*(a))
x2 = int(x0 - 1000*(-b))
y2 = int(y0 - 1000*(a))
cv2.line(img, (x1, y1), (x2, y2), (200, 135, 100), 2)
# 标记直线编号
string = str(i)
cv2.putText(img, string, (int(x0), int(y0)),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 0, 255), 1)
left_top_point_x, left_top_point_y = getcrosspoint(
left_line_rho_average, left_line_theta_average,
top_line_rho_average, top_line_theta_average)
left_bottom_point_x, left_bottom_point_y = getcrosspoint(
left_line_rho_average, left_line_theta_average,
bottom_line_rho_average, bottom_line_theta_average)
right_top_point_x, right_top_point_y = getcrosspoint(
right_line_rho_average, right_line_theta_average,
top_line_rho_average, top_line_theta_average)
right_bottom_point_x, right_bottom_point_y = getcrosspoint(
right_line_rho_average, right_line_theta_average,
bottom_line_rho_average, bottom_line_theta_average)
Table_2D = []
Table_2D.append([left_top_point_x, left_top_point_y])
Table_2D.append([left_bottom_point_x, left_bottom_point_y])
Table_2D.append([right_top_point_x, right_top_point_y])
Table_2D.append([right_bottom_point_x, right_bottom_point_y])
cv2.circle(img, (left_top_point_x, left_top_point_y),
3, (255, 0, 0), -1)
cv2.circle(img, (left_bottom_point_x, left_bottom_point_y),
3, (255, 0, 0), -1)
cv2.circle(img, (right_top_point_x, right_top_point_y),
3, (255, 0, 0), -1)
cv2.circle(img, (right_bottom_point_x,
right_bottom_point_y), 3, (255, 0, 0), -1)
Table_3D = []
Table_3D.append([0, 0, 0])
Table_3D.append([0, 55, 0])
Table_3D.append([55, 0, 0])
Table_3D.append([55, 55, 0])
Table_3D = np.array(Table_3D, dtype='float32')
Table_2D = np.array(Table_2D, dtype='float32')
_, rvector, tvector = cv2.solvePnP(
Table_3D, Table_2D, internal_calibration, distCoeffs)
axis = np.float32([[55, 0, 0], [0, 55, 0], [0, 0, -20]]).reshape(-1, 3)
imgpts, _ = cv2.projectPoints(
axis, rvector, tvector, internal_calibration, distCoeffs,)
lined = draw(img, (left_top_point_x, left_top_point_y), imgpts)
return lined, Table_2D
def square_desk(num, x, canny, hough):
'''
输入：图片编号
输出：坐标系
导出：带坐标系图片
'''
img_path = 'JPEGImages/' + str(num) + '.jpg'
img = cv2.imread(img_path, 1)
thresed = thres(img, x)
thresed_new = remove_small_objects(thresed)
edges = square_canny(thresed_new, canny)
lined, Table_2D = square_line(img, edges, hough)
# affine_table_2D = np.float32([[0,0],[0,550],[550,0],[550,550]])
# M = cv2.getPerspectiveTransform(Table_2D,affine_table_2D)
# marked = cv2.warpPerspective(lined,M,(550,550)) # Perspective_Transformation
cv2.imwrite('JPEGImages/marked' + str(num) + '.jpg', lined)
return lined, Table_2D
def square_trans(Table_2D: '桌子四角', points: '物体底部四点', lined_img=[]):
'''
若最后一个参数非空（调试模式），则显示图片
'''
affine_table_2D = np.float32(
[[0, 0], [0, 550], [550, 0], [550, 550]]) # 方桌边长550mm
M = cv2.getPerspectiveTransform(Table_2D, affine_table_2D) # 获取透视变换矩阵
transed_points = np.matmul(points, np.transpose(M))
for i in range(4):
transed_points[i][0] = transed_points[i][0] / transed_points[i][2]
transed_points[i][1] = transed_points[i][1] / transed_points[i][2]
a = [0 for i in range(4)]
for i in range(4):
a[i] = (int(transed_points[i][0]), int(transed_points[i][1]))
angle = np.degrees(np.arccos(
(a[0][0] - a[0][1]) / (((a[0][0] - a[0][1])**2 + (a[1][0] - a[1][1])**2) ** 0.5)))
if len(lined_img):
# Perspective_Transformation
transed = cv2.warpPerspective(lined_img, M, (550, 550))
cv2.line(transed, a[0], a[1], (0, 255, 0), 1)
cv2.line(transed, a[0], a[2], (0, 255, 0), 1)
cv2.line(transed, a[1], a[3], (0, 255, 0), 1)
cv2.line(transed, a[2], a[3], (0, 255, 0), 1)
cv2.imshow('perspective', transed)
return transed_points, angle