def leerPlaca(self, binarizado):
"""
Args:
img: imagen que contiene el texto, obtenida con la
funcion cv2.imread.
Returns:
El string del texto en la imagen.
"""
cvmat_image = cv.fromarray(binarizado)
imgbincv = cv.GetImage(cvmat_image)
# GetImage fuente de un error, revisar
# Configuración del tesseract
api = tesseract.TessBaseAPI()
api.Init(".", "eng", tesseract.OEM_DEFAULT)
api.SetPageSegMode(tesseract.PSM_AUTO)
# Enviando imagen binarizada al tesseract
tesseract.SetCvImage(imgbincv, api)
text = api.GetUTF8Text()
text = re.sub(r'\W+', '', text)
return text
def getText(image0):
#thicken the border in order to make tesseract feel happy
offset = textOffset
height, width, channel = image0.shape
image1=cv2.copyMakeBorder(image0, offset, offset,offset,offset,\
cv2.BORDER_CONSTANT,value=rgbWhite)
#set up tesseract API
api = tesseract.TessBaseAPI()
api.Init(".", "eng", tesseract.OEM_DEFAULT)
api.SetPageSegMode(tesseract.PSM_AUTO)
height1, width1, channel1 = image1.shape
#star text extraction
cvmat_image = cv.fromarray(image1)
iplimage = cv.GetImage(cvmat_image)
#extract texts
tesseract.SetCvImage(iplimage, api)
text = api.GetUTF8Text()
conf = api.MeanTextConf()
return text, conf
def hs_histogram(src):
# Convert to HSV
hsv = cv.CreateImage(cv.GetSize(src), 8, 3)
cv.CvtColor(src, hsv, cv.CV_BGR2HSV)
# Extract the H and S planes
h_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
s_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
cv.Split(hsv, h_plane, s_plane, None, None)
planes = [h_plane, s_plane]
h_bins = 30
s_bins = 32
hist_size = [h_bins, s_bins]
# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
h_ranges = [0, 180]
# saturation varies from 0 (black-gray-white) to
# 255 (pure spectrum color)
s_ranges = [0, 255]
ranges = [h_ranges, s_ranges]
scale = 10
hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1)
cv.CalcHist([cv.GetImage(i) for i in planes], hist)
(_, max_value, _, _) = cv.GetMinMaxHistValue(hist)
hist_img = cv.CreateImage((h_bins*scale, s_bins*scale), 8, 3)
for h in range(h_bins):
for s in range(s_bins):
bin_val = cv.QueryHistValue_2D(hist, h, s)
intensity = cv.Round(bin_val * 255 / max_value)
cv.Rectangle(hist_img,
(h*scale, s*scale),
((h+1)*scale - 1, (s+1)*scale - 1),
cv.RGB(intensity, intensity, intensity),
cv.CV_FILLED)
return hist_img
def createHist(img):
#cv.CvtColor(img,img,cv.CV_BGR2HSV)
b_plane = cv.CreateImage((img.width, img.height), 8, 1)
g_plane = cv.CreateImage((img.width, img.height), 8, 1)
r_plane = cv.CreateImage((img.width, img.height), 8, 1)
cv.Split(img, b_plane, g_plane, r_plane, None)
planes = [b_plane, g_plane, r_plane]
bins = 4
b_bins = bins
g_bins = bins
r_bins = bins
hist_size = [b_bins, g_bins, r_bins]
b_range = [0, 255]
g_range = [0, 255]
r_range = [0, 255]
ranges = [b_range, g_range, r_range]
hist = cv.CreateHist(hist_size, cv.CV_HIST_ARRAY, ranges, 1)
cv.CalcHist([cv.GetImage(i) for i in planes], hist)
cv.NormalizeHist(hist, 1)
return hist
def leerPlaca(self, binarizado):
"""
Args:
img: imagen que contiene el texto, obtenida con la
funcion cv2.imread.
Returns:
El string del texto en la imagen.
"""
cvmat_image = cv.fromarray(binarizado)
imgbincv = cv.GetImage(cvmat_image)
# GetImage fuente de un error, revisar
# Configuración del tesseract
api = tesseract.TessBaseAPI()
api.Init('.', 'eng', tesseract.OEM_DEFAULT)
api.SetVariable("tessedit_char_whitelist",
"0123456789ABCDFGHIJKLMNOPQRSTVWXYZ-UE")
api.SetPageSegMode(tesseract.PSM_AUTO)
# Enviando imagen binarizada al tesseract
tesseract.SetCvImage(imgbincv, api)
text = api.GetUTF8Text()
text = re.sub(r'\W+', '', text)
return text
def transmit_thread():
'''thread for image transmit to GCS'''
state = mpstate.camera_state
tx_count = 0
skip_count = 0
bsend = block_xmit.BlockSender(0, bandwidth=state.settings.bandwidth, debug=False)
state.bsocket = MavSocket(mpstate.mav_master[0])
state.bsend2 = block_xmit.BlockSender(mss=96, sock=state.bsocket, dest_ip='mavlink', dest_port=0, backlog=5, debug=False)
state.bsend2.set_bandwidth(state.settings.bandwidth2)
while not state.unload.wait(0.02):
bsend.tick(packet_count=1000, max_queue=state.settings.maxqueue1)
state.bsend2.tick(packet_count=1000, max_queue=state.settings.maxqueue2)
check_commands()
if state.transmit_queue.empty():
continue
(frame_time, regions, im_full, im_640) = state.transmit_queue.get()
if state.settings.roll_stabilised:
roll=0
else:
roll=None
pos = get_plane_position(frame_time, roll=roll)
# this adds the latlon field to the regions
log_joe_position(pos, frame_time, regions)
# filter out any regions outside the boundary
if state.boundary_polygon:
regions = cuav_region.filter_boundary(regions, state.boundary_polygon, pos)
regions = cuav_region.filter_regions(im_full, regions, min_score=state.settings.minscore)
state.xmit_queue = bsend.sendq_size()
state.xmit_queue2 = state.bsend2.sendq_size()
state.efficiency = bsend.get_efficiency()
state.bandwidth_used = bsend.get_bandwidth_used()
state.rtt_estimate = bsend.get_rtt_estimate()
jpeg = None
if len(regions) > 0:
lowscore = 0
highscore = 0
for r in regions:
lowscore = min(lowscore, r.score)
highscore = max(highscore, r.score)
if state.settings.transmit:
# send a region message with thumbnails to the ground station
thumb = None
if state.settings.send1:
thumb_img = cuav_mosaic.CompositeThumbnail(cv.GetImage(cv.fromarray(im_full)),
regions,
thumb_size=state.settings.thumbsize)
thumb = scanner.jpeg_compress(numpy.ascontiguousarray(cv.GetMat(thumb_img)), state.settings.quality)
pkt = ThumbPacket(frame_time, regions, thumb, state.frame_loss, state.xmit_queue, pos)
buf = cPickle.dumps(pkt, cPickle.HIGHEST_PROTOCOL)
bsend.set_bandwidth(state.settings.bandwidth)
bsend.set_packet_loss(state.settings.packet_loss)
bsend.send(buf,
dest=(state.settings.gcs_address, state.settings.gcs_view_port),
priority=1)
# also send thumbnails via 900MHz telemetry
if state.settings.send2 and highscore >= state.settings.minscore2:
if thumb is None or lowscore < state.settings.minscore2:
# remove some of the regions
regions = cuav_region.filter_regions(im_full, regions, min_score=state.settings.minscore2)
thumb_img = cuav_mosaic.CompositeThumbnail(cv.GetImage(cv.fromarray(im_full)),
regions,
thumb_size=state.settings.thumbsize)
thumb = scanner.jpeg_compress(numpy.ascontiguousarray(cv.GetMat(thumb_img)), state.settings.quality)
pkt = ThumbPacket(frame_time, regions, thumb, state.frame_loss, state.xmit_queue, pos)
buf = cPickle.dumps(pkt, cPickle.HIGHEST_PROTOCOL)
state.bsend2.set_bandwidth(state.settings.bandwidth2)
state.bsend2.send(buf, priority=highscore)
# Base how many images we send on the send queue size
send_frequency = state.xmit_queue // 3
if send_frequency == 0 or (tx_count+skip_count) % send_frequency == 0:
jpeg = scanner.jpeg_compress(im_640, state.settings.quality)
if jpeg is None:
skip_count += 1
continue
# keep filtered image size
state.jpeg_size = 0.95 * state.jpeg_size + 0.05 * len(jpeg)
tx_count += 1
if state.settings.gcs_address is None:
continue
bsend.set_packet_loss(state.settings.packet_loss)
bsend.set_bandwidth(state.settings.bandwidth)
pkt = ImagePacket(frame_time, jpeg, state.xmit_queue, pos)
str = cPickle.dumps(pkt, cPickle.HIGHEST_PROTOCOL)
bsend.send(str,
dest=(state.settings.gcs_address, state.settings.gcs_view_port))
from cv2 import cv
import numpy as np
from pylab import *
import glob
import os
i = 0
for files in glob.glob('D:/pic/car/*.jpg'):
filepath,filename = os.path.split(files)
image = cv2.imread(filepath + '/' + filename)
# image = cv2.imread('D:/pic/car2.jpg')
h,w = image.shape[:2]
#灰度化
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
grayIPimage = cv.GetImage(cv.fromarray(gray))
sobel = cv.CreateImage((w, h),cv2.IPL_DEPTH_16S, 1) #创建一张深度为16位有符号（-65536~65535）的的图像区域保持处理结果
cv.Sobel(grayIPimage,sobel,2,0,7) # 进行x方向的sobel检测
temp = cv.CreateImage(cv.GetSize(sobel),cv2.IPL_DEPTH_8U, 1) #图像格式转换回8位深度已进行下一步处理
cv.ConvertScale(sobel, temp,0.00390625, 0)
cv.Threshold(temp, temp, 0, 255, cv2.THRESH_OTSU)
kernal = cv.CreateStructuringElementEx(3,1, 1, 0, 0)
cv.Dilate(temp, temp,kernal,2)
cv.Erode(temp, temp,kernal,4)
cv.Dilate(temp, temp,kernal,2)
# cv.ShowImage('1', temp)
kernal = cv.CreateStructuringElementEx(1,3, 0, 1, 0)
cv.Erode(temp, temp,kernal,1)
cv.Dilate(temp, temp,kernal,3)
# cv.ShowImage('2', temp)
temp = np.asarray(cv.GetMat(temp))
def writeImage(self):
if(self._videoWriter != None):
cv.WriteFrame(self._videoWriter, cv.GetImage(self._currentImage))
# print cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_POS_FRAMES) # frame == index
# 2. 当前帧frame2赋给frame2gray
frame2 = cv.QueryFrame(capture)
cv.CvtColor(frame2, frame2gray, cv.CV_RGB2GRAY) # 原图frame2转换为frame2gray格式
# 3. 图像做帧差法，结果为res
cv.AbsDiff(frame1gray, frame2gray, res) # 比较frame1gray 和 frame2gray 的差，结果输出给res
# res = cv2.absdiff(frame1gray, frame2gray) # 此为cv2新版api
cv.ShowImage("After AbsDiff", res)
# 4. 保存res作为前景目标foreground
# cv.Convert(res, gray)
# out_foreground.write(np.uint8(res)) # 保存为前景目标
# out_foreground.write(np.asarray(cv.GetMat(res)))
cv.WriteFrame(out_foreground, cv.GetImage(res)) # res格式为cvmat格式，转化为iplimage格式
# 5. 平滑处理
# cv.Smooth(res, res, cv.CV_BLUR, 5, 5) # 光滑一下res
# 6. 形态学变换，开闭操作
element = cv.CreateStructuringElementEx(5*2+1, 5*2+1, 5, 5, cv.CV_SHAPE_RECT) # CreateStructuringElementEx(cols, rows, anchorX, anchorY, shape, values=None)
cv.MorphologyEx(res, res, None, None, cv.CV_MOP_OPEN) # cv2.morphologyEx(src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]])
cv.MorphologyEx(res, res, None, None, cv.CV_MOP_CLOSE) # 形态学变换相应的开 闭操作
# 7. 二值化阈值处理：对得到的前景进行阈值选择，去掉伪前景
cv.Threshold(res, res, 10, 255, cv.CV_THRESH_BINARY) # 二值化 cv.Threshold(src, dst, threshold, maxValue, thresholdType)
# 8. blob提取电梯区域 ------------- 未完
print image1.dtype.itemsize
width_step = width * image1.dtype.itemsize
print width_step
#method 1
iplimage = cv.CreateImageHeader((width1, height1), cv.IPL_DEPTH_8U, channel1)
cv.SetData(iplimage, image1.tostring(),
image1.dtype.itemsize * channel1 * (width1))
tesseract.SetCvImage(iplimage, api)
text = api.GetUTF8Text()
conf = api.MeanTextConf()
image = None
print "..............."
print "Ocred Text: %s" % text
print "Cofidence Level: %d %%" % conf
#method 2:
cvmat_image = cv.fromarray(image1)
iplimage = cv.GetImage(cvmat_image)
print iplimage
tesseract.SetCvImage(iplimage, api)
#api.SetImage(m_any,width,height,channel1)
text = api.GetUTF8Text()
conf = api.MeanTextConf()
image = None
print "..............."
print "Ocred Text: %s" % text
print "Cofidence Level: %d %%" % conf
api.End()
#s_plane = cv.Threshold(s_plane, s_plane, minSat, 255, cv.CV_THRESH_BINARY)
h_bins = 30
s_bins = 32
hist_size = [h_bins, s_bins]
# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
h_ranges = [0, 180]
# saturation varies from 0 (black-gray-white) to
# 255 (pure spectrum color)
s_ranges = [0, 255]
ranges = [h_ranges, s_ranges]
scale = 10
hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1)
cv.CalcHist([cv.GetImage(i) for i in planes], hist)
#SetHistogramm ???
#Normalize
im2 = cv.LoadImage("../img/build.png")
hsv2 = cv.CreateImage(cv.GetSize(im2), 8, 3)
cv.CvtColor(im2, hsv2, cv.CV_BGR2HSV)
h_plane2 = cv.CreateImage(cv.GetSize(im2), 8, 1)
s_plane2 = cv.CreateImage(cv.GetSize(im2), 8, 1)
cv.Split(hsv2, h_plane2, s_plane2, None, None)
res = cv.CreateImage((im2.width, im2.height), 8, 3)
cv.CalcBackProject([h_plane2, s_plane2], res, hist)
cv.MeanShift(res, (10, 10, 30, 30),
pre_y_flag = y_flag
if count > 0:
y_flag = 1
else:
y_flag = 0
if pre_y_flag != y_flag:
h_pos.append(y)
match_img = []
tmp_mg = []
for i in range(0, leng / 2):
cv2.waitKey(3)
cv2.rectangle(image, (w_pos[i], h_pos[i]), (w_pos[i + 1], h_pos[i + 1]),
(255, 255, 0))
imgIP = cv.GetImage(cv.fromarray(image))
cv.SetImageROI(imgIP, (w_pos[2 * i], h_pos[2 * i], w_pos[2 * i + 1] -
w_pos[2 * i], h_pos[2 * i + 1] - h_pos[2 * i]))
img_cache = cv.CreateImage(
(w_pos[2 * i + 1] - w_pos[2 * i], h_pos[2 * i + 1] - h_pos[2 * i]),
cv.IPL_DEPTH_8U, 3)
cv.Copy(imgIP, img_cache)
match_img.append(img_cache)
# cv.SaveImage('ss'+bytes(i)+'.jpg',match_img[i])
cv.ResetImageROI(imgIP)
# cv.ShowImage('window'+bytes(i),match_img[i])
#aa=image.copy((range(10,20),range(20,100)))
#cv2.imshow("hello",aa)
#cv2.imshow("img",image)
print w_pos
