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
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)
# cv2.waitKey(0)
# print(contours)
# img = cv2.imread('../cropping/test6.jpg')
# num_rows, num_cols = img.shape[:2]
True)
#Skip small or non-convex objects
if (abs(cv2.contourArea(contours[i])) < 100):
continue
#triangle
if (len(approx) == 3):
x, y, w, h = cv2.boundingRect(contours[i])
cv2.putText(resized, 'TRI', (x, y), cv2.FONT_HERSHEY_SIMPLEX,
scale, (255, 255, 255), 2, cv2.LINE_AA)
cv2.rectangle(resized, (x, y), (x + w, y + h), (0, 0, 255), 2)
M = cv2.moments(contours[i])
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
color = cl.label(mask, contours[i])
contours[i] = contours[i].astype("float")
contours[i] *= ratio
contours[i] = contours[i].astype("int")
cv2.drawContours(resized, [contours[i]], -1, (0, 255, 0), 2)
cv2.putText(image, color, (cx, cy), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (255, 255, 255), 2)
elif (len(approx) >= 4 and len(approx) <= 6):
#nb vertices of a polygonal curve
vtc = len(approx)
#get cos of all corners
cos = []
for j in range(2, vtc + 1):
cos.append(
angle(approx[j % vtc], approx[j - 2], approx[j - 1]))
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.rectangle(img, (array[0], array[1]), (array[2], array[3]),
(0, 0, 255), 3)
cv2.imshow('Detection result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
list_intensities = find_pixels(rcnn_result, mask)
bgr_arry = run_kmeans(list_intensities)
# rgb_arry_init = np.zeros((1, 1, 3), dtype="uint8")
# rgb_arry_init[0] = [r, g, b]
# print ('bgr val', bgr_arry, type(bgr_arry))
val = cl.label(bgr_arry[::-1])
color_name = val['Generic Color Name']
print('index', color_name)
cv2.imshow('Segmentation result', rcnn_result)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.putText(rcnn_result, str(color_name), (600, 500),
cv2.FONT_HERSHEY_SIMPLEX, 5, (255, 255, 255), 2)
cv2.imshow('Final result', rcnn_result)
cv2.waitKey(0)
cv2.destroyAllWindows()
else:
print('No car found')
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
# detect the shape of the contour and label the color
shape = sd.detect(c)
color = cl.label(lab, c)
# multiply the contour (x, y) coords by the resize ratio,
# then draw the contours and the name of the shape and labeled
# color on the image
c = c.astype('float')
c *= ratio
c = c.astype('int')
text = '{} {}'.format(color, shape)
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)
# show image
cv2.imshow('image', image)
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)
c = c.astype("float")
c *= ratio
c = c.astype("int")
text = "{} {}".format(color, shape)
print(text)
with open('result.txt', 'w') as f:
f.write(text)
f.write('\n')
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, text, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.fillConvexPoly(image1, np.array(approx, np.int32), color_value)