def patternDetection(cnts):
#Initialize classes
sd = ShapeDetector()
cl = ColorLabeler()
contours = []
for c in cnts:
shape = sd.detect(c, 3)
if shape != "":
color = cl.label(lab, c)
x,y,w,h = cv2.boundingRect(c)
cX = x+w/2
cY = y+h/2
contours.append([cX, cY, shape, color])
row = []
pattern = []
rowMin = contours[0][1]-10
rowMax = contours[0][1]+10
for c in contours:
cX, cY, shape, color = c
if rowMin <= cY and cY <= rowMax:
row.append([cX, cY, shape+' '+color])
else:
row.sort()
pattern.append(row)
row = [[cX, cY, shape+' '+color]]
rowMin = cY-10
rowMax = cY+10
return pattern
def l23shape(self):
# 读取图片
image = cv2.imread(self.imgName)
cv2.imshow("images", image)
# 进行裁剪操作
resized = imutils.resize(image, width=300)
ratio = image.shape[0] / float(resized.shape[0])
# 进行高斯模糊操作
blurred = cv2.GaussianBlur(resized, (5, 5), 0)
# 进行图片灰度化
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
# 进行颜色空间的变换
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
# 进行阈值分割
thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)[1]
cv2.imshow("Thresh", thresh)
# 在二值图片中寻找轮廓
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# 初始化形状检测器和颜色标签
sd = ShapeDetector()
cl = ColorLabeler()
# 遍历每一个轮廓
for c in cnts:
# 计算每一个轮廓的中心点
M = cv2.moments(c)
cX = int((M["m10"] / M["m00"]) * ratio)
cY = int((M["m01"] / M["m00"]) * ratio)
# 进行颜色检测和形状检测
shape = sd.detect(c)
color = cl.label(lab, c)
# 进行坐标变换
c = c.astype("float")
c *= ratio
c = c.astype("int")
text = "{} {}".format(color, shape)
# 绘制轮廓并显示结果
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, text, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255), 2)
cv2.imshow("Image", image)
cv2.waitKey(0)
show = cv2.resize(image, (640, 480)) # 把读到的帧的大小重新设置为 640x480
show = cv2.cvtColor(show,
cv2.COLOR_BGR2RGB) # 视频色彩转换回RGB，这样才是现实的颜色
self.showImage = QtGui.QImage(
show.data, show.shape[1], show.shape[0],
QtGui.QImage.Format_RGB888) # 把读取到的视频数据变成QImage形式
self.lImg1.setPixmap(QtGui.QPixmap.fromImage(
self.showImage)) # 往显示视频的Label里 显示QImage
def __init__(self):
self.pub = rospy.Publisher('pub_with_sub', String, queue_size=10)
# self.bridge = CvBridge()
# self.img = rospy.Subscriber("color_dect",Image,self.callback)
image = cv2.imread("talker_screenshot_25.08.2021.png")
# if ret == True:
cv2.imshow("talker", image)
cv2.waitKey(1)
resized = imutils.resize(image, width=300)
blurred = cv2.GaussianBlur(resized, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
cv2.imshow("gray is ", gray)
cv2.waitKey(1)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
# thresh = cv2.threshold(gray,20,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)[1]
# thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
# cv2.THRESH_BINARY_INV,11,2)[1]
thresh = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY_INV)[1]
cv2.imshow("ROS_Thresh", thresh)
cv2.waitKey(1)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cv2.drawContours(resized, cnts, -1, (0, 255, 0), 2)
cl = ColorLabeler()
# --------------------------------------------------------#
# for c in cnts:
# area = [cv2.contourArea(c)]
# max_area = max(area)
# if max_area > 2500:
# color = cl.label(lab, c)
# cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
# -------------------------------------------------------#
areas = []
for c in cnts:
area = [cv2.contourArea(c)]
areas.append(area)
max_area = max(areas)
max_idx = np.argmax(areas)
print("max_area", max_area)
# if max_area > 2500:
color = cl.label(lab, cnts, max_idx)
# print cnts[max_idx]
rospy.loginfo("The s is %s", color)
self.pub.publish(color)
return color
def findcolors(nc, im):
cont = 0
image = cv2.imread(im)
blurred = cv2.GaussianBlur(image, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cl = ColorLabeler()
sd = ShapeDetector()
for c in cnts:
if cv2.contourArea(c) < 800:
continue
else:
M = cv2.moments(c)
cX = int((M["m10"] / M["m00"]))
cY = int((M["m01"] / M["m00"]))
shape = sd.detect(c)
color = cl.label(lab, c)
print(shape)
print(color)
if (shape == "square" or shape == "rectangle"):
if (color == nc):
text = "{}".format(color)
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, text, (cX, cY),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255),
2)
cont = cont + 1
cv2.imshow("Image", image)
cv2.waitKey(0)
return cont
def alltheCV(image):
#resized = imutils.resize(image, width=300)
#ratio = image.shape[0] / float(resized.shape[0])
#height, width = image.shape[:2]
print("runningCV")
#Convert white background to black
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
(thresh, baw) = cv2.threshold(gray, 128, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
wab = cv2.bitwise_not(baw)
wabrgb = cv2.cvtColor(wab, cv2.COLOR_GRAY2RGB)
noBackground = cv2.bitwise_and(image, wabrgb)
#Blur and threshold the image for curve detection
blurred = cv2.GaussianBlur(noBackground, (5, 5), 0)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
gray = cv2.cvtColor(noBackground, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)[1]
#Detect contours
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0]
#Initialize classes
sd = ShapeDetector()
cl = ColorLabeler()
#Finding centers using bounding rectangle
centers = []
for c in cnts:
shape, perimeter = sd.detect(c, 1)
color = cl.label(lab, c)
x, y, w, h = cv2.boundingRect(c)
cX = x + w / 2
cY = y + h / 2
centers.append([cX, cY, perimeter, shape, color])
return centers
def findcolors(nc,im):
cont=0
image = cv2.imread(im)
blurred = cv2.GaussianBlur(image, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cl = ColorLabeler()
sd = ShapeDetector()
for c in cnts:
if cv2.contourArea(c)< 800:
continue
else:
M = cv2.moments(c)
cX = int((M["m10"] / M["m00"]))
cY = int((M["m01"] / M["m00"]))
shape = sd.detect(c)
color = cl.label(lab, c)
print(shape)
print(color)
if (shape=="square" or shape=="rectangle"):
if (color==nc):
text = "{}".format(color)
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, text, (cX, cY),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
cont=cont+1
cv2.imshow("Image", image)
cv2.waitKey(0)
return cont
wab = cv2.bitwise_not(baw)
wabrgb = cv2.cvtColor(wab, cv2.COLOR_GRAY2RGB)
noBackground = cv2.bitwise_and(resized, wabrgb)
lab = cv2.cvtColor(noBackground, cv2.COLOR_BGR2LAB)
blurred_3 = cv2.GaussianBlur(noBackground, (1, 1), 0)
gray_3 = cv2.cvtColor(blurred_3, cv2.COLOR_BGR2GRAY)
thresh_3 = cv2.threshold(gray_3, 30, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh_3.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
#Initialize classes
sd = ShapeDetector()
cl = ColorLabeler()
contours = []
for c in cnts:
shape = sd.detect(c, 3)
if shape != "":
color = cl.label(lab, c)
x, y, w, h = cv2.boundingRect(c)
cX = x + w / 2
cY = y + h / 2
contours.append([cX, cY, shape, color])
# cv2.imshow("Image", img)
# cv2.circle(img, (cX, cY), 3, (255, 255, 255), -1)
# cv2.waitKey(0)
# cv2.imshow("Image", img)
# cv2.circle(img, (cX, cY), 3, (255, 255, 255), -1)
from colorlabeler import ColorLabeler
lowerBound = np.array([136, 87, 111])
upperBound = np.array([180, 255, 255])
kernelOpen = np.ones((5, 5))
kernelClose = np.ones((20, 20))
contours = {}
approx = []
scale = 1
#camera
cap = cv2.VideoCapture(0)
cl = ColorLabeler()
#calculate angle
def angle(pt1, pt2, pt0):
dx1 = pt1[0][0] - pt0[0][0]
dy1 = pt1[0][1] - pt0[0][1]
dx2 = pt2[0][0] - pt0[0][0]
dy2 = pt2[0][1] - pt0[0][1]
return float((dx1 * dx2 + dy1 * dy2)) / math.sqrt(
float((dx1 * dx1 + dy1 * dy1)) * (dx2 * dx2 + dy2 * dy2) + 1e-10)
while (cap.isOpened()):
ret, frame = cap.read()
from imageai.Detection import ObjectDetection
from mrcnn import utils
import numpy as np
import cv2
from sklearn.cluster import KMeans
import matplotlib.pylab as plt
import matplotlib.patches as patches
from colorlabeler import ColorLabeler
from maskrcnn import *
import warnings
warnings.filterwarnings("ignore")
cl = ColorLabeler()
lst_intensities = []
def preprocess(rgb):
blurred = cv2.GaussianBlur(rgb, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
# print ('gray_shape', gray.shape)
binary = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)[1]
# binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \
# cv2.THRESH_BINARY, 11, 2)
# binary = binary - binary.max()
#binary[binary==255] = 1
return binary
# blur the resized image slightly, then convert it to both
# graysale and the L*a*b* color spaces
blurred = cv2.GaussianBlur(resized, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)[1]
# find contours in the thresholded image
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# init
sd = ShapeDetector()
cl = ColorLabeler()
# loop over cnts
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
try:
cX = int((M['m10'] / M['m00']) * ratio)
cY = int((M['m01'] / M['m00']) * ratio)
except:
cX = 0
cY = 0
from colorlabeler import ColorLabeler
import argparse
import imutils
import cv2
ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True, help='/path/to/img')
args = vars(ap.parse_args())
image = cv2.imread(args['image'])
resized = imutils.resize(image, width=300)
ratio = image.shape[0] / float(resized.shape[0])
blurred = cv2.GaussianBlur(resized, (5,5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if imutils.is_cv2() else contours[1]
# Initialize shape and color detectors
sd = ShapeDetector()
cl = ColorLabeler()
for c in contours:
M = cv2.moments(c)
cX = int((M['m10'] / (M['m00'] + 1e-7)
rows, cols = (len(cnts), 2)
tmp = [[0]*cols]*rows
for c in cnts:
mask = np.zeros(resized.shape[:2], dtype="uint8")
cv2.drawContours(mask, [c[0]], -1, 255, -1)
mean = cv2.mean(resized, mask=mask)[:3]
tmp[i] = [c,mean]
i += 1
cnts = tmp
#if it is the first time
if cl == None:
cl = ColorLabeler(cnts,nbrClasses)
continue
#colors classification
#cnts = cl.getCenters(cnts)
#every contour with its label
i = 0
labels = cl.getLabel(cnts,supervised = supervised)
rows, cols = (len(cnts), 3)
tmp = [[0]*cols]*rows
for c in cnts:
tmp[i] = [c[0],c[1],labels[i]]
i += 1
blurred = cv2.GaussianBlur(resized, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 170, 255, cv2.THRESH_BINARY)[1]
# thresh = cv2.Canny(gray, 50, 100)
# thresh = cv2.dilate(thresh, None, iterations=2)
# thresh = cv2.erode(thresh, None, iterations=2)
cv2.imshow("Thresh", thresh)
cv2.waitKey(0)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
(cnts, _) = contours.sort_contours(cnts)
sd = ShapeDetector()
cl = ColorLabeler()
# for c in cnts:
if 1:
if len(cnts) > 1:
c = cnts[1]
else:
c = cnts[0]
M = cv2.moments(c)
cX = int((M["m10"] / M["m00"]) * ratio)
cY = int((M["m01"] / M["m00"]) * ratio)
shape, approx = sd.detect(c)
color, color_value = cl.label(lab, c)
# mean = cl2.get_mean(lab, c)
# color = cl2.getColorName(mean)
