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cal the scaler by gf4 and make it better

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Tang1705
2020-07-07 17:38:59 +08:00
parent 867cfa0036
commit c7e3a46148
3 changed files with 181 additions and 40 deletions
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Class.zip
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Class/detection/Calibration.py
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@@ -1,8 +1,13 @@
import math
import time
import cv2
import numpy as np
from GFmatrix import GF
np.seterr(invalid='ignore')
class Calibration:
def __init__(self):
@@ -47,10 +52,10 @@ class Calibration:
def get_candidate_points(self, frame):
candidate = np.zeros((frame.shape[0], frame.shape[1])) # 初始化图像点位置矩阵为0
b, g, r = cv2.split(frame)
i = 0
while i < frame.shape[0] - 7:
i = int(frame.shape[0] / 2)
while i < int(7 * frame.shape[0] / 8):
j = 0
while j < frame.shape[1] - 7:
while j < int(frame.shape[1] / 2):
sum = [0, 0, 0] # 存储r,g,b三个通道的差值结果
tempxy = [0, 0, 0, 0, 0, 0] # 存储三个通道纵向和横向的临时求和结果
k = -6
@@ -67,17 +72,24 @@ class Calibration:
sum[2] = sum[2] + abs(tempxy[4] - tempxy[5]) # b通道差值
d = max(sum[0], sum[1], sum[2]) # 选择差值最大的通道
# print(d)
if d > 350: # tq和zyy人工学习调参选阈值，阈值增大，候选点集中于球体中央
if d > 1300: # tq和zyy人工学习调参选阈值，阈值增大，候选点集中于球体中央
candidate[i][j] = -1 # -1标记为候选点
# point_size = 1
# point_color = (0, 0, 255)
# thickness = 0 # 可以为 0 、4、8
#
# cv2.circle(frame, (j,i), point_size, point_color, thickness)
else:
candidate[i][j] = -3 # -3标记为非候选点
j = j + 1
i = i + 1
# cv2.imshow('1', frame)
# cv2.waitKey(0) # 按0退出
return candidate
"""
get_grid_points:
将RGB图像根据公式Gray = 0.2989 * R + 0.5907 * G + 0.1140 * B 转为灰度图像
将RGB图像转为灰度图像
根据真正的角点具有严格的中心对称性，将相关系数大于选定阈值的点选做特征点
并根据P1和P2类型点的特征（左右或上下为模式元素）将特征点分类为两类特征点
白色背景的灰度值高于颜色元素的灰度值
@@ -87,26 +99,32 @@ class Calibration:
"""
def get_grid_points(self, frame, candidate):
GrayImage = np.zeros((1024, 1280))
b, g, r = cv2.split(frame)
GrayImage = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
i = 0
while i < frame.shape[0]:
j = 0
while j < frame.shape[1]:
# 转灰度图像，提高绿色通道的比例，使得白色背景与绿色元素易于区分
GrayImage[i][j] = 0.2989 * r[i][j] + 0.5907 * g[i][j] + 0.1140 * b[i][j]
j = j + 1
i = i + 1
gridpoints = np.zeros((frame.shape[0], frame.shape[1]))
size = 7
counter = 0
circle_neighborhood = np.zeros((size, size))
for i in range(0, size):
for j in range(0, size):
if i < (size - 1) / 2:
if j >= (size - 1) / 2 - i - (size - 3) / 4 and j <= (size - 1) / 2 + i + + (size - 3) / 4:
counter += 1
circle_neighborhood[i][j] = 1
elif i >= (size + 5) / 4 and i <= (3 * size - 5) / 4:
counter += 1
circle_neighborhood[i][j] = 1
else:
if j >= (size - 1) / 2 - (size - 1 - i) - (size - 3) / 4 and j <= (size - 1) / 2 + (
size - 1 - i) + (
size - 3) / 4:
counter += 1
circle_neighborhood[i][j] = 1
gridpoints = np.zeros((1024, 1280))
circle_neighborhood = [[0, 0, 1, 1, 1, 0, 0], [0, 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], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0]]
i = 0
while i < frame.shape[0]:
i = int(frame.shape[0] / 2)
while i < int(7 * frame.shape[0] / 8):
j = 0
while j < frame.shape[1]:
while j < int(frame.shape[1] / 2):
"""
element元素说明
便于计算圆形邻域的相关系数，引入变量element
@@ -140,16 +158,23 @@ class Calibration:
q = q + 1
p = p + 1
pc = (37 * element[0] - element[1] * element[2]) / (
np.sqrt(37 * element[3] - element[1] ** 2) * np.sqrt(37 * element[4] - element[2] ** 2))
if pc > 0.1: # 相关系数足够大的点被判断为特征点(对称系数为0的为特征点——A Twofold...)
pc = (counter * element[0] - element[1] * element[2]) / (
np.sqrt(counter * element[3] - element[1] ** 2) * np.sqrt(
counter * element[4] - element[2] ** 2))
if pc > 0.5: # 相关系数足够大的点被判断为特征点(对称系数为0的为特征点——A Twofold...)
gridpoints[i][j] = 1
# point_size = 1
# point_color = (0, 0, 255)
# thickness = 0 # 可以为 0 、4、8
#
# cv2.circle(frame, (j, i), point_size, point_color, thickness)
else:
gridpoints[i][j] = 0
j = j + 1
i = i + 1
# cv2.imshow('2', frame)
# cv2.waitKey(0) # 按0退出
return gridpoints
"""
@@ -197,10 +222,10 @@ class Calibration:
def get_feature_point(self, frame, gps):
q = []
i = 0
while i < frame.shape[0]:
i = int(frame.shape[0] / 2)
while i < int(7 * frame.shape[0] / 8):
j = 0
while j < frame.shape[1]:
while j < int(frame.shape[1] / 2):
if self.flag[i][j] == 1 or gps[i][j] == 0:
j = j + 1
continue
@@ -210,6 +235,89 @@ class Calibration:
i = i + 1
return q
"""
decode:
根据颜色解码
确定特征点在矩阵的位置
"""
def decode(self, frame, feature_point):
points = []
position = []
color_map = np.zeros((frame.shape[0], frame.shape[1]))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(frame)
for i in range(0, frame.shape[0]):
for j in range(0, frame.shape[1]):
if l[i][j] < 50:
color_map[i][j] = 3
frame[i][j] = np.array([0, 0, 0])
elif l[i][j] > 140:
frame[i][j] = np.array([255, 255, 255])
color_map[i][j] = 255
else:
if b[i][j] < 125:
color_map[i][j] = 0
frame[i][j] = np.array([255, 0, 0])
else:
if a[i][j] > 125:
color_map[i][j] = 1
frame[i][j] = np.array([0, 0, 255])
else:
color_map[i][j] = 2
frame[i][j] = np.array([0, 255, 0])
# cv2.imshow('lab', frame)
# cv2.waitKey(0) # 按0退出
data = GF(1, 1, 1, 1, 1, 1)
# map = np.zeros((4, 4, 4, 4, 4, 4, 2))
map = np.zeros((4096, 2))
i = 0
while i < 64:
j = 1
while j < 62:
index = data[i][j - 1] * 4 ** 5 + data[i][j] * 4 ** 4 + data[i][j + 1] * 4 ** 3 + data[i + 1][
j - 1] * 4 ** 2 + data[i + 1][j] * 4 + data[i + 1][j + 1]
map[index] = [i, j]
j = j + 1
i = i + 1
for point in feature_point:
if color_map[int(point[0])][int(point[1]) + 9] != 255 and color_map[int(point[0])][
int(point[1]) - 9] != 255:
pass
else:
i = int(point[0])
j = int(point[1])
index = int(color_map[i - 9][j - 18]) * 4 ** 5 + int(color_map[i - 9][j]) * 4 ** 4 + int(
color_map[i - 9][j + 18]) * 4 ** 3 + int(color_map[i + 9][j - 18]) * 4 ** 2 + int(
color_map[i + 9][j]) * 4 + int(color_map[i + 9][j + 18])
# point_size = 1
# point_color = (255, 255, 255)
# thickness = 0 # 可以为 0 、4、8
# print(color_map[i - 9][j - 18], color_map[i - 9][j], color_map[i - 9][j + 18], color_map[i + 9][j - 18],
# color_map[i + 9][j], color_map[i + 9][j + 18])
# print(index)
#
# cv2.circle(frame, (int(j - 18), int(i - 9)), point_size, point_color, thickness)
# cv2.circle(frame, (int(j), int(i - 9)), point_size, point_color, thickness)
# cv2.circle(frame, (int(j + 18), int(i - 9)), point_size, point_color, thickness)
# cv2.circle(frame, (int(j - 18), int(i + 9)), point_size, point_color, thickness)
# cv2.circle(frame, (int(j), int(i + 9)), point_size, point_color, thickness)
# cv2.circle(frame, (int(j + 18), int(i + 9)), point_size, point_color, thickness)
try:
if map[index][0] < 10 and map[index][1] < 8:
points.append(list(map[int(index)]))
position.append([i, j])
except:
pass
cv2.imshow('lab', frame)
cv2.waitKey(0) # 按0退出
return points, position
def process(self, stream):
while stream.isOpened():
flag, img_rd = stream.read() # Get camera video stream
@@ -224,8 +332,9 @@ class Calibration:
stream.release()
cv2.destroyAllWindows()
cv2.imwrite('testt.png',img_rd)
cv2.imwrite('testt.png', img_rd)
# img_rd = cv2.resize(img_rd, (img_rd.shape[0]//2, img_rd.shape[1]//2))
# 1024*1280 -1候选点，-3非候选点
candidate = self.get_candidate_points(frame=img_rd)
# 1024*1280 1角点，0非角点
@@ -234,20 +343,52 @@ class Calibration:
featurepoints_position = self.get_feature_point(frame=img_rd, gps=gridpoints)
# print(featurepoints_position)
# featurepoints_position = np.array(featurepoints_position)
# featurepoints_position = featurepoints_position[np.lexsort(featurepoints_position[:, :-1].T)]
feature_points, position = self.decode(frame=img_rd, feature_point=featurepoints_position)
# feature_points = np.array(feature_points)
# feature_points = feature_points[np.lexsort(feature_points[:, :-1].T)]
# print(feature_points)
# print(featurepoints_position)
# distance = []
# for i in range(0, len(featurepoints_position) - 1):
# distance.append(
# math.sqrt((featurepoints_position[i + 1][0] - featurepoints_position[i][0]) ** 2 +
# (featurepoints_position[i + 1][1] - featurepoints_position[i][1]) ** 2))
# print(distance)
# distance = sorted(distance)
index = len(feature_points) // 2
pixel_distance = math.sqrt((position[index + 1][0] - position[index][0]) ** 2 +
(position[index + 1][1] - position[index][1]) ** 2)
world_distance = math.sqrt((feature_points[index + 1][0] * 2 - feature_points[index][0] * 2) ** 2 +
(feature_points[index + 1][1] * 2 - feature_points[index][1] * 2) ** 2)
scale = world_distance / pixel_distance
# print(distance)
# for i in range(index - 1, index + 2):
# print(distance[i])
# 绘制特征点
point_size = 1
point_color = (0, 0, 255)
thickness = 0 # 可以为 0 、4、8
for point in featurepoints_position:
cv2.circle(img_rd, (int(point[1]), int(point[0])), point_size, point_color, thickness)
cv2.namedWindow("image")
cv2.imshow('image', img_rd)
cv2.waitKey(0) # 按0退出
# point_size = 1
# point_color = (0, 0, 255)
# thickness = 0 # 可以为 0 、4、8
# for i in range(0, len(featurepoints_position)):
# cv2.circle(img_rd, (int(featurepoints_position[i][1]), int(featurepoints_position[i][0])),
# point_size, point_color, thickness)
# for point in featurepoints_position:
# cv2.circle(img_rd, (int(point[1]), int(point[0])), point_size, point_color, thickness)
# cv2.namedWindow("image")
# cv2.imshow('image', img_rd)
# cv2.waitKey(0) # 按0退出
return scale
self.draw_note(img_rd)
self.update_fps()
cv2.imshow("camera", img_rd)
cv2.imshow("image", img_rd)
def run(self):
cap = cv2.VideoCapture(0)

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