def load_tile_lowres(self, tile):
'''load a lower resolution tile from cache to fill in a
map while waiting for a higher resolution tile'''
if tile.zoom == self.min_zoom:
return None
# find the equivalent lower res tile
(lat,lon) = tile.coord()
width2 = TILES_WIDTH
height2 = TILES_HEIGHT
for zoom2 in range(tile.zoom-1, self.min_zoom-1, -1):
width2 /= 2
height2 /= 2
if width2 == 0 or height2 == 0:
break
tile_info = self.coord_to_tile(lat, lon, zoom2)
# see if its in the tile cache
key = tile_info.key()
if key in self._tile_cache:
img = self._tile_cache[key]
if img == self._unavailable:
continue
else:
path = self.tile_to_path(tile_info)
try:
img = cv.LoadImage(path)
# add it to the tile cache
self._tile_cache[key] = img
while len(self._tile_cache) > self.cache_size:
self._tile_cache.popitem(0)
except IOError as e:
continue
# copy out the quadrant we want
availx = min(TILES_WIDTH - tile_info.offsetx, width2)
availy = min(TILES_HEIGHT - tile_info.offsety, height2)
if availx != width2 or availy != height2:
continue
cv.SetImageROI(img, (tile_info.offsetx, tile_info.offsety, width2, height2))
img2 = cv.CreateImage((width2,height2), 8, 3)
try:
cv.Copy(img, img2)
except Exception:
continue
cv.ResetImageROI(img)
# and scale it
scaled = cv.CreateImage((TILES_WIDTH, TILES_HEIGHT), 8, 3)
cv.Resize(img2, scaled)
#cv.Rectangle(scaled, (0,0), (255,255), (0,255,0), 1)
return scaled
return None
def crop(self, image, subRect):
cv.SetImageROI(image, subRect)
img2 = cv.CreateImage(cv.GetSize(image), image.depth, image.nChannels)
#img3 = cv.CreateImage(cv.GetSize(image), image.depth, image.nChannels);
cv.Copy(image, img2)
#img3 = self.equilizer.normalize(img2)
cv.ResetImageROI(image)
return img2
def DetectRedEyes(image, faceCascade, eyeCascade): min_size = (20,20) image_scale = 2 haar_scale = 1.2 min_neighbors = 2 haar_flags = 0 # Allocate the temporary images gray = cv.CreateImage((image.width, image.height), 8, 1) smallImage = cv.CreateImage((cv.Round(image.width / image_scale),cv.Round (image.height / image_scale)), 8 ,1) # Convert color input image to grayscale cv.CvtColor(image, gray, cv.CV_BGR2GRAY) # Scale input image for faster processing cv.Resize(gray, smallImage, cv.CV_INTER_LINEAR) # Equalize the histogram cv.EqualizeHist(smallImage, smallImage) # Detect the faces faces = cv.HaarDetectObjects(smallImage, faceCascade, cv.CreateMemStorage(0), haar_scale, min_neighbors, haar_flags, min_size) # If faces are found if faces: for ((x, y, w, h), n) in faces: # the input to cv.HaarDetectObjects was resized, so scale the # bounding box of each face and convert it to two CvPoints pt1 = (int(x * image_scale), int(y * image_scale)) pt2 = (int((x + w) * image_scale), int((y + h) * image_scale)) cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0) face_region = cv.GetSubRect(image,(x,int(y + (h/4)),w,int(h/2))) cv.SetImageROI(image, (pt1[0], pt1[1], pt2[0] - pt1[0], int((pt2[1] - pt1[1]) * 0.7))) eyes = cv.HaarDetectObjects(image, eyeCascade, cv.CreateMemStorage(0), haar_scale, min_neighbors, haar_flags, (15,15)) if eyes: # For each eye found for eye in eyes: # Draw a rectangle around the eye cv.Rectangle(image, (eye[0][0], eye[0][1]), (eye[0][0] + eye[0][2], eye[0][1] + eye[0][3]), cv.RGB(255, 0, 0), 1, 8, 0) cv.ResetImageROI(image) return image
def dect_image(filename):
img = cv.LoadImage(filename)
gray = cv.CreateImage(cv.GetSize(img), 8, 1)
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
cv.EqualizeHist(gray, gray)
rects = detect(img, cascade)
if len(rects) != 0:
rect = (rects[0][0], rects[0][1], rects[0][2] - rects[0][0],
rects[0][3] - rects[0][1])
cv.SetImageROI(img, rect)
cv.SaveImage(filename, img)
def area_to_image(self,
lat,
lon,
width,
height,
ground_width,
zoom=None,
ordered=True):
'''return an RGB image for an area of land, with ground_width
in meters, and width/height in pixels.
lat/lon is the top left corner. The zoom is automatically
chosen to avoid having to grow the tiles'''
img = cv.CreateImage((width, height), 8, 3)
tlist = self.area_to_tile_list(lat, lon, width, height, ground_width,
zoom)
# order the display by distance from the middle, so the download happens
# close to the middle of the image first
if ordered:
(midlat, midlon) = self.coord_from_area(width / 2, height / 2, lat,
lon, width, ground_width)
tlist.sort(key=lambda d: d.distance(midlat, midlon), reverse=True)
for t in tlist:
scaled_tile = self.scaled_tile(t)
w = min(width - t.dstx, scaled_tile.width - t.srcx)
h = min(height - t.dsty, scaled_tile.height - t.srcy)
if w > 0 and h > 0:
cv.SetImageROI(scaled_tile, (t.srcx, t.srcy, w, h))
cv.SetImageROI(img, (t.dstx, t.dsty, w, h))
cv.Copy(scaled_tile, img)
cv.ResetImageROI(img)
cv.ResetImageROI(scaled_tile)
# return as an RGB image
cv.CvtColor(img, img, cv.CV_BGR2RGB)
return img
def find_squares4(color_img):
"""
Finds multiple squares in image
Steps:
-Use Canny edge to highlight contours, and dilation to connect
the edge segments.
-Threshold the result to binary edge tokens
-Use cv.FindContours: returns a cv.CvSequence of cv.CvContours
-Filter each candidate: use Approx poly, keep only contours with 4 vertices,
enough area, and ~90deg angles.
Return all squares contours in one flat list of arrays, 4 x,y points each.
"""
#select even sizes only
width, height = (color_img.width & -2, color_img.height & -2)
timg = cv.CloneImage(color_img) # make a copy of input image
gray = cv.CreateImage((width, height), 8, 1)
# select the maximum ROI in the image
cv.SetImageROI(timg, (0, 0, width, height))
# down-scale and upscale the image to filter out the noise
pyr = cv.CreateImage((width / 2, height / 2), 8, 3)
cv.PyrDown(timg, pyr, 7)
cv.PyrUp(pyr, timg, 7)
tgray = cv.CreateImage((width, height), 8, 1)
squares = []
# Find squares in every color plane of the image
# Two methods, we use both:
# 1. Canny to catch squares with gradient shading. Use upper threshold
# from slider, set the lower to 0 (which forces edges merging). Then
# dilate canny output to remove potential holes between edge segments.
# 2. Binary thresholding at multiple levels
N = 11
for c in [0, 1, 2]:
#extract the c-th color plane
cv.SetImageCOI(timg, c + 1)
cv.Copy(timg, tgray, None)
cv.Canny(tgray, gray, 0, 50, 5)
cv.Dilate(gray, gray)
squares = squares + find_squares_from_binary(gray)
# Look for more squares at several threshold levels
for l in range(1, N):
cv.Threshold(tgray, gray, (l + 1) * 255 / N, 255,
cv.CV_THRESH_BINARY)
squares = squares + find_squares_from_binary(gray)
return squares
def draw(self, img, pixmapper, bounds):
'''draw the thumbnail on the image'''
thumb = self.img()
(px,py) = pixmapper(self.latlon)
# find top left
px -= thumb.width/2
py -= thumb.height/2
w = thumb.width
h = thumb.height
(px, py, sx, sy, w, h) = self.clip(px, py, w, h, img)
cv.SetImageROI(thumb, (sx, sy, w, h))
cv.SetImageROI(img, (px, py, w, h))
cv.Copy(thumb, img)
cv.ResetImageROI(img)
cv.ResetImageROI(thumb)
# remember where we placed it for clicked()
self.posx = px+w/2
self.posy = py+h/2
def blend_views(bldIm, frame, mask, frec, max_rec):
maskMat = cv.fromarray(mask)
dispX = int(np.round(frec.left - max_rec.left))
dispY = int(np.round(frec.top - max_rec.top))
bldROI = cv.GetImage(bldIm)
cv.SetImageROI(bldROI, (dispX, dispY, int(np.round(
frec.width())), int(np.round(frec.height()))))
cv.Add(bldROI, cv.fromarray(frame), bldROI, maskMat)
cv.ResetImageROI(bldROI)
cv.Copy(bldROI, bldIm)
return bldIm
def calibrate(self, img, mask_range = (50, 100)):
# mask: ignore pixels that are really close or far away, like the
# Nao (close, low values) and kinect noise (far far away, high values)
_, _, width, height = cv.GetImageROI(img)
mask = cv.CreateImage((width, height), cv.IPL_DEPTH_8U, 1)
cv.InRangeS(img, mask_range[0], mask_range[1], mask)
# using the mask, create a new image containing only the pixels of
# interest.
self.base_img = cv.CreateImage((width, height), cv.IPL_DEPTH_8U, 1)
cv.SetImageROI(self.base_img, cv.GetImageROI(img))
# get the minimum value, and subtract that from all pixels so only
# the difference in depth in the image remains.
minimum, _, _, _ = cv.MinMaxLoc(img, mask)
cv.SubS(img, minimum, self.base_img)
def addText(self, frame, textTop, textBottom): s = cv.GetSize(frame) offset = 8 ## add space for text notations textSize = cv.GetTextSize(textTop, self._font) textframe = cv.CreateImage( (s[0], s[1] + 4*textSize[1] + 2*offset), frame.depth, frame.channels) cv.Set(textframe, 0) cv.SetImageROI(textframe, (0, 2*textSize[1] + offset, s[0], s[1])) cv.Copy(frame, textframe) cv.ResetImageROI(textframe) ## write text cv.PutText(textframe, textTop, (5, 2*textSize[1] + offset/2), self._font, self._fontcolor) cv.PutText(textframe, textBottom, (5, int(s[1] + 4*textSize[1] + 1.5 * offset)), self._font, self._fontcolor) return textframe
def detect_and_draw(img, cascade, mask):
# allocate temporary images
gray = cv.CreateImage((img.width, img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(
img.width / image_scale), cv.Round(img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if (cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
print "detection time = %gms" % (t / (cv.GetTickFrequency() * 1000.))
if faces:
for ((x, y, w, h), n) in faces:
# Affichage du carré de recherche
xmoustache = int((x * image_scale) + w * 0.5)
ymoustache = int((y * image_scale) + h * 1.25)
wmoustache = int(w * 0.5 * image_scale)
hmoustache = int(h * 0.19 * image_scale)
img_mask = cv.CreateImage((wmoustache, hmoustache), mask.depth,
mask.nChannels)
cv.SetImageROI(
img, (xmoustache, ymoustache, wmoustache, hmoustache))
cv.Resize(mask, img_mask, cv.CV_INTER_LINEAR)
# Affichage du carré de recherche
cv.Sub(img, img_mask, img)
cv.ResetImageROI(img)
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
#cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
cv.ShowImage("result", img)
def save_clip_img():
img=cv.LoadImage('static/InterceptedIMG/clip.jpg')
vertical_distance_decimal,vertical_distance_integer = math.modf(float(640)/19)
parallel_distance_decimal,parallel_distance_integer = math.modf(float(480)/19)
#print vertical_distance_decimal,vertical_distance_integer,parallel_distance_decimal,parallel_distance_integer
draw_img = cv2.imread('static/InterceptedIMG/clip.jpg')
for i in range(19):
for j in range(19):
cv2.rectangle(draw_img,(0+int(33.68*i),int(25.26*j)),(int(33.68*(i+1)),int(25.26*(j+1))),(0,255,0),1)
cv2.imshow('image',draw_img)
k = cv2.waitKey(0) & 0xFF
if k == 27:
cv2.destroyAllWindows()
for i in range(19):
for j in range(19):
wn_position =(int(vertical_distance_integer*i)+int(vertical_distance_decimal*i),int(parallel_distance_integer*j)+int(parallel_distance_decimal*j))
es_position =(int(vertical_distance_integer*(i+1)+int(vertical_distance_decimal*i)),int(parallel_distance_integer*(j+1))+int(parallel_distance_decimal*j))
img_backup=cv.CloneImage(img)
cv.SetImageROI(img_backup,(wn_position[0],wn_position[1],33,25))
cv.SaveImage('static/ClippedImg/%d_%d.jpg'%(j,i),img_backup)
#grabbing a frame, applying blur, flipping the image
frame = cv.QueryFrame(camera)
cv.Smooth(frame, frame, cv.CV_BLUR, 3)
cv.Flip(frame, frame, 1)
#drawing the rectangle and writing the text
temp1 = cv.CloneImage(frame)
cv.Rectangle(temp1, point1, point2, color, 1)
cv.PutText(temp1, "Place in box", (430, 240), font, color)
cv.PutText(temp1, "then hit q", (430, 260), font, color)
#taking snapshot after q is pressed
if cv.WaitKey(10) == 113:
flag = 1
cv.SetImageROI(temp1, (300, 200, 100, 100))
template = cv.CloneImage(temp1)
cv.ResetImageROI(temp1)
cv.DestroyWindow("Image")
if flag == 0:
cv.NamedWindow("Image", 1)
cv.MoveWindow("Image", 300, 0)
cv.ShowImage("Image", temp1)
continue
W, H = cv.GetSize(frame)
w, h = cv.GetSize(template)
width = W - w + 1
height = H - h + 1
values = [cv.QueryHistValue_1D(hist, n) for n in range(255)]
max_value = max(values)
for n, value in enumerate(values):
cv.Rectangle(canvas,
(0, n),
(int((value / max_value) * 200), n + 1),
(255), cv.CV_FILLED)
cv.SetImageROI(canvas, (0, 0, canvas.width, canvas.height))
return canvas
if len(sys.argv) < 3:
print "Usage: %s calibration-img.png test-img.png [test-img.png 2 ...]"
exit(0)
filter = TiltFilter()
input_img = cv.LoadImage(sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
cv.SetImageROI(input_img, (0, 0, 630, 440))
filter.calibrate(input_img)
for path in sys.argv[2:]:
img = cv.LoadImage(path, cv.CV_LOAD_IMAGE_GRAYSCALE)
cv.ShowImage("Normal", render_with_histogram(img))
filter.filter(img)
cv.ShowImage("Filtered", render_with_histogram(img))
if cv.WaitKey(0) in (-1, 27):
break
name = path.split("/")[-1].split(".")[0]
#printlist (image, annotations)
lst.write(path + " " + div_json_path + "/" + folder + "_" + name + ".json\n")
#dividepics
#print json for every image
out = open(div_json_path + "/" + folder + "_" + name + ".json", 'w')
img = cv.LoadImage(path)
count = 0
for group in anno["annotations"]:
count = count + 1
_x = int(math.floor(group["x"]))
_y = int(math.floor(group["y"]))
_width = int(math.ceil(group["width"]))
_height = int(math.ceil(group["height"]))
_class = group["class"].encode()
cv.SetImageROI(img,(_x, _y, _width, _height))
# print pics_path + "/" + folder + "_" + name + "_" + str(count) + "_" + _class + ".png"
group.clear()
group["x"] = _x
group["y"] = _y
group["width"] = _width
group["height"] = _height
group["class"] = _class
string = pics_path + "/" + folder + "_" + name + "_" + str(count) + "_" + _class + ".png"
group["imgname"] = string.encode()
cv.SaveImage(string, img)
out.write(json.dumps(anno))
#print anno
def SetImageROItoBlob(img, blob):
cv.SetImageROI(
img,
(blob.minx, blob.miny, blob.maxx - blob.minx, blob.maxy - blob.miny))
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))
contours, heirs = cv2.findContours(temp,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for tours in contours:
rc = cv2.boundingRect(tours)
if rc[2]/rc[3] >= 2:
#rc[0] 表示图像左上角的纵坐标，rc[1] 表示图像左上角的横坐标，rc[2] 表示图像的宽度，rc[3] 表示图像的高度，
cv2.rectangle(image, (rc[0],rc[1]),(rc[0]+rc[2],rc[1]+rc[3]),(255,0,255))
imageIp = cv.GetImage(cv.fromarray(image))
cv.SetImageROI(imageIp, rc)
imageCopy = cv.CreateImage((rc[2], rc[3]),cv2.IPL_DEPTH_8U, 3)
cv.Copy(imageIp, imageCopy)
cv.ResetImageROI(imageIp)
cv.SaveImage('D:/pic/result/' + str(i) + '.jpg',imageCopy)
i = i +1
# cv2.imshow("黑底白字",image)
cv2.waitKey(0) #暂停用于显示图片
result.append((r[0][0], r[0][1], r[0][0] + r[0][2], r[0][1] + r[0][3]))
if result[0][2] > 300 and result[0][3] > 300:
return result
else:
return []
def draw_rects(img, rects, color):
if rects:
for i in rects:
cv.Rectangle(img, (int(rects[0][0]), int(rects[0][1])),
(int(rects[0][2]), int(rects[0][3])),
cv.CV_RGB(0, 255, 0), 1, 8, 0)
cascade = cv.Load(
"/home/flyvideo/caffe-master/examples/Face_rect/haarcascade_frontalface_alt.xml"
)
if __name__ == '__main__':
img = cv.LoadImage('/home/flyvideo/caffe-master/examples/Face_rect/4.jpg')
gray = cv.CreateImage(cv.GetSize(img), 8, 1)
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
cv.EqualizeHist(gray, gray)
rects = detect(img, cascade)
rect = (rects[0][0], rects[0][1], rects[0][2] - rects[0][0],
rects[0][3] - rects[0][1])
#draw_rects(img, rects, (0, 255, 0))
cv.SetImageROI(img, rect)
cv.SaveImage('face.jpg', img)
def detectFace(self, cam_img, faceCascade, eyeCascade,
mouthCascade): #cam_img should be cv2.cv.iplcam_img
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
image_width = int(cam_img.get(cv.CV_CAP_PROP_FRAME_WIDTH))
image_height = int(cam_img.get(cv.CV_CAP_PROP_FRAME_HEIGHT))
# Allocate the temporary images
gray = cv.CreateImage((image_width, image_height), 8,
1) #tuple as the first arg
smallImage = cv.CreateImage((cv.Round(
image_width / image_scale), cv.Round(image_height / image_scale)),
8, 1)
(ok, img) = cam_img.read()
#print 'gray is of ',type(gray) >>> gray is of <type 'cv2.cv.iplimage'>
#print type(smallImage) >>> <type 'cv2.cv.iplimage'>
#print type(image) >>> <type 'cv2.VideoCapture'>
#print type(img) >>> <type 'numpy.ndarray'>
#convert numpy.ndarray to iplimage
ipl_img = cv2.cv.CreateImageHeader((img.shape[1], img.shape[0]),
cv.IPL_DEPTH_8U, 3)
cv2.cv.SetData(ipl_img, img.tostring(),
img.dtype.itemsize * 3 * img.shape[1])
# Convert color input image to grayscale
cv.CvtColor(ipl_img, gray, cv.CV_BGR2GRAY)
# Scale input image for faster processing
cv.Resize(gray, smallImage, cv.CV_INTER_LINEAR)
# Equalize the histogram
cv.EqualizeHist(smallImage, smallImage)
# Detect the faces
faces = cv.HaarDetectObjects(smallImage, faceCascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
# => The function returns a list of tuples, (rect, neighbors) , where rect is a CvRect specifying the object’s extents and neighbors is a number of neighbors.
# => CvRect cvRect(int x, int y, int width, int height)
# If faces are found
if faces:
face = faces[0]
self.faceX = face[0][0]
self.faceY = face[0][1]
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(ipl_img, pt1, pt2, cv.RGB(0, 0, 255), 3, 8, 0)
#face_region = cv.GetSubRect(ipl_img,(x,int(y + (h/4)),w,int(h/2)))
cv.SetImageROI(
ipl_img,
(pt1[0], pt1[1], pt2[0] - pt1[0], int(
(pt2[1] - pt1[1]) * 0.7)))
eyes = cv.HaarDetectObjects(ipl_img, eyeCascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, (15, 15))
if eyes:
# For each eye found
for eye in eyes:
# Draw a rectangle around the eye
cv.Rectangle(
ipl_img, #image
(
eye[0][0], #vertex pt1
eye[0][1]),
(
eye[0][0] + eye[0][2], #vertex pt2 opposite to pt1
eye[0][1] + eye[0][3]),
cv.RGB(255, 0, 0),
1,
4,
0) #color,thickness,lineType(8,4,cv.CV_AA),shift
cv.ResetImageROI(ipl_img)
return ipl_img
def parse_detail_page(self, page, link):
print 3
doc_id = link.split('/')[
-1] #http://openlaw.cn/judgement/ea86414b0cac4075a3b88fcd9f8d4139
d = pq(page) #判断是否出现验证码
try:
if u'请输入验证码' in d.text():
print u'等待输入验证码 > '
imgfoler = '/img/'
basic = 'img'
webimage = imgfoler + utility.get_unique_name()
uniquename = basic + webimage
self.driver.save_screenshot(uniquename + '_s.png') #截屏 _s.png
captcha_image = self.driver.find_element_by_xpath(
'//img[@id="kaptcha"]')
loc = captcha_image.location
loc['x'] = int(loc['x'])
loc['y'] = int(loc['y'])
image = cv.LoadImage(uniquename + '_s.png', True)
out = cv.CreateImage((200, 50), image.depth, 3)
cv.SetImageROI(image, (loc['x'], loc['y'], 200, 50))
cv.Resize(image, out)
imgname = uniquename + '.jpg'
cv.SaveImage(imgname, out)
# 使用外部服务解码
result = captchaservice.getCaptcha(imgname)
dictresult = json.loads(result)
if dictresult.has_key('Error'):
resultno = 1
raise Exception('service does not work well !')
#endif
code = dictresult['Result']
inputkey = self.driver.find_element_by_xpath(
'//input[@class="search-field"]')
inputkey.clear()
inputkey.send_keys(code)
time.sleep(2)
searchbtn = self.driver.find_element_by_xpath(
'//input[@type="submit"]')
searchbtn.click()
time.sleep(10)
except:
pass
data = {}
title = d('h2.entry-title').text()
if '404' in d('title').text():
print ' [!] ERROR page, 404 not found, %s' % link
return
if not title:
print ' [!] Empty page, resend %s' % link #如果页面为空，则将链接再发送到队列
self.channel.basic_publish(
exchange='',
routing_key='doc_queue',
body=link,
properties=pika.BasicProperties(
delivery_mode=2, # 使消息持久化
))
time.sleep(.5)
return
#print title
print 4
#提取结构化信息 侧边栏(sidebar)
reason = trim_colon(
d('aside#sidebar section').eq(0).find('li').filter(
lambda i: u'案由' in pq(this).text()).text())
court = trim_colon(
d('aside#sidebar section').eq(0).find('li').filter(
lambda i: u'法院' in pq(this).text()).text())
doc_type = trim_colon(
d('aside#sidebar section').eq(0).find('li').filter(
lambda i: u'类型' in pq(this).text()).text())
status = trim_colon(
d('aside#sidebar section').eq(0).find('li').filter(
lambda i: u'程序' in pq(this).text()).text())
date = trim_colon(d('li.ht-kb-em-date').text()).strip() #strip() 去前后空格
regx = re.match(r'\d{4}-\d{2}-\d{2}', date)
if not regx:
date = '1970-01-01'
case_id = trim_colon(d('li.ht-kb-em-category').text())
content = d('div#entry-cont').text().strip(u' 允许所有人 查看 该批注 \
允许所有人 编辑 该批注 取消 保存 Annotate')
# 人物侧边栏
persons = d('aside#sidebar section').eq(1)
# 原告
accuser = filter_person(persons, [u'原告', u'审请人', u'上诉人', u'再审申请人'])
# 被告
accused = filter_person(persons, [u'被告', u'被审请人', u'被上诉人'])
# 审判长
chief_judge = filter_person(persons, [u'审判长'])
# 律师
lawyers = filter_lawyers(persons)
data['title'] = title
data['title_search'] = title
data['reason'] = reason
data['court'] = court
data['date'] = date
data['doc_type'] = doc_type
data['status'] = status
data['content'] = content
data['case_id'] = case_id
data['lawyers'] = lawyers
data['accuser'] = accuser
data['accused'] = accused
data['chief_judge'] = chief_judge
data['url'] = link
#导入elasticsearch
#self.es.index(index=ES_INDEX, doc_type=ES_TYPE, id=doc_id, body=data)
#print 'data',data
#convertedDict = xmltodict.parse(data);
realpath = link
extraction = {}
extraction['realpath'] = realpath
extraction['data'] = data
data1 = {}
data1['extraction'] = extraction
convertedXml = xmltodict.unparse(data1)
# print "convertedXml=",convertedXml;
try:
folder = './result/'
filename = folder + data['case_id'] + '.xml'
f = open(filename, 'w')
f.write(convertedXml)
f.close()
except:
print 'error...'
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import cv2.cv as cv
import os
img = cv.LoadImage("d:/flappybird/atlas.png", -1) #读取原图（这就是那张拼过的图片，我改了一下名字）
mask = cv.LoadImage("d:/flappybird/atlas.png", 0) #读取mask
width, height = cv.GetSize(img)
img2 = cv.CreateImage((width, height), 8, 4) #创建一张背景透明的图片
cv.Copy(img, img2, mask) #copy过去
file = open("d:/flappybird/atlas.txt") #这是输入的文件
for line in file:
strs = line.split(" ")
fileName = strs[0]
imgWidth = int(strs[1])
imgHeight = int(strs[2])
imgX = float(strs[3]) * width
imgY = float(strs[4]) * height
cv.SetImageROI(img2, (int(imgX), int(imgY), imgWidth, imgHeight))
#设置感兴趣的区域
cv.SaveImage("d:/flappybird/" + fileName + ".png",
img2) #根据读取到的文件名保存到这个路径下
def get_next_link(self, page, link):
"""获取下一页链接"""
d = pq(page)
next_link = d('a.next.page-numbers').attr('href')
if not next_link:
# 如果不存在next_link，可能是最后一页
# 也可能是网站问题，默认重试获取这个链接3次
if u'请输入验证码' in d.text():
print u'等待输入验证码 >'
cpt_tip = self.driver.find_element_by_xpath('//img[@id="kaptcha"]')
imgfoler = '/img/'
basic = 'img'
webimage = imgfoler + utility.get_unique_name()
uniquename = basic + webimage
self.driver.save_screenshot(uniquename + '_s.png') #截屏 _s.png
captcha_image = self.driver.find_element_by_xpath('//img[@id="kaptcha"]')
loc = captcha_image.location
loc['x']=int(loc['x'])
loc['y']=int(loc['y'])
image = cv.LoadImage(uniquename + '_s.png', True)
out = cv.CreateImage((200,50), image.depth, 3)
cv.SetImageROI(image,(loc['x'],loc['y'], 200, 50))
print '5'
cv.Resize(image,out)
imgname = uniquename + '.jpg'
cv.SaveImage(imgname, out)
# 使用外部服务解码
result = captchaservice.getCaptcha(imgname)
dictresult = json.loads(result)
if dictresult.has_key('Error') :
resultno = 1
raise Exception('service does not work well !')
#endif
code = dictresult['Result']
inputkey = self.driver.find_element_by_xpath('//input[@class="search-field"]')
inputkey.clear()
inputkey.send_keys(code)
time.sleep(10)
searchbtn = self.driver.find_element_by_xpath('//input[@type="submit"]')
searchbtn.click()
time.sleep(1)
#return 'err'
# 如果没有next_link，则判断3次，如果有则继续，没有就说明是最后一页
for i in range(3):
self.driver.get(link)
# time.sleep(5)
page = self.driver.find_element_by_xpath(
'//*').get_attribute('outerHTML')
d = pq(page)
next_link = d('a.next.page-numbers').attr('href')
if next_link:
break
# 再重新判断
if next_link:
next_link = urlparse.urljoin(link, next_link)
return next_link
def DetectRedEyes(image, faceCascade, smileCascade):
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
# Allocate the temporary images
gray = cv.CreateImage((image.width, image.height), 8, 1)
smallImage = cv.CreateImage((cv.Round(
image.width / image_scale), cv.Round(image.height / image_scale)), 8,
1)
# Convert color input image to grayscale
cv.CvtColor(image, gray, cv.CV_BGR2GRAY)
# Scale input image for faster processing
cv.Resize(gray, smallImage, cv.CV_INTER_LINEAR)
# Equalize the histogram
cv.EqualizeHist(smallImage, smallImage)
# Detect the faces
faces = cv.HaarDetectObjects(smallImage, faceCascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
# If faces are found
if faces:
#print faces
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
#print "face"
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
# print pt1
# print pt2
#cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 1, 8, 0)
#cv.PutText(image, "face", pt1, font, cv.RGB(255, 0, 0))
face_region = cv.GetSubRect(image,
(x, int(y + (h / 4)), w, int(h / 2)))
#split face
#cv.Rectangle(image, (pt1[0],(pt1[1] + (abs(pt1[1]-pt2[1]) / 2 ))), pt2, cv.RGB(0,255,0), 1, 8, 0)
#cv.PutText(image, "lower", (pt1[0],(pt1[1] + (abs(pt1[1]-pt2[1]) / 2 ))), font, cv.RGB(0, 255, 0))
cv.SetImageROI(
image, (pt1[0],
(pt1[1] + (abs(pt1[1] - pt2[1]) / 2)), pt2[0] - pt1[0],
int((pt2[1] - (pt1[1] + (abs(pt1[1] - pt2[1]) / 2))))))
smiles = cv.HaarDetectObjects(image, smileCascade,
cv.CreateMemStorage(0), 1.1, 5, 0,
(15, 15))
if smiles:
#print smiles
for smile in smiles:
cv.Rectangle(
image, (smile[0][0], smile[0][1]),
(smile[0][0] + smile[0][2], smile[0][1] + smile[0][3]),
cv.RGB(0, 0, 255), 1, 8, 0)
cv.PutText(image, "smile", (smile[0][0], smile[0][1]),
font, cv.RGB(0, 0, 255))
cv.PutText(image, str(smile[1]),
(smile[0][0], smile[0][1] + smile[0][3]), font,
cv.RGB(0, 0, 255))
#print ((abs(smile[0][1] - smile[0][2]) / abs(pt1[0] - pt2[0])) * 100)
global smileness
smileness = smile[1]
cv.ResetImageROI(image)
#if smile[1] > 90:
# mqttc.publish("smiles", "got smile", 1)
# time.sleep(5)
#eyes = cv.HaarDetectObjects(image, eyeCascade,
#cv.CreateMemStorage(0),
#haar_scale, min_neighbors,
#haar_flags, (15,15))
#if eyes:
# For each eye found
#print eyes
#for eye in eyes:
# Draw a rectangle around the eye
# cv.Rectangle(image,
# (eye[0][0],
# eye[0][1]),
# (eye[0][0] + eye[0][2],
# eye[0][1] + eye[0][3]),
# cv.RGB(255, 0, 0), 1, 8, 0)
cv.ResetImageROI(image)
return image
for e in SignsList:
imagesList[e] = cv.LoadImage("signs/" + e, cv.CV_LOAD_IMAGE_GRAYSCALE)
#imagesList.append(cv.LoadImage("signs/"+e,cv.CV_LOAD_IMAGE_GRAYSCALE))
cv.NamedWindow("Input", cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow("Gesture Space", cv.CV_WINDOW_AUTOSIZE)
matchresult = 1
p_capWebcam = cv.CaptureFromCAM(0)
while 1:
p_imgOriginal = cv.QueryFrame(p_capWebcam)
cv.Flip(p_imgOriginal, p_imgOriginal, 1)
# capture from webcam
p_gray = cv.CreateImage(cv.GetSize(p_imgOriginal), 8, 1)
cv.CvtColor(p_imgOriginal, p_gray, cv.CV_BGR2GRAY)
cv.SetImageROI(p_gray, (400, 200, 200, 200))
# Region setting of fixed interest
cv.Threshold(p_gray, p_gray, 100, 255, cv.CV_THRESH_BINARY_INV)
cv.Rectangle(p_imgOriginal, (400, 200), (600, 400), (255, 0, 0), 4)
j = 0
for imageI in imagesList: # path of the image list and test each image with the ROI (region of interest)
#image_to_test=cv.LoadImage("signs/"+image_path,cv.CV_LOAD_IMAGE_GRAYSCALE)
matchresult = compare_2_formes(p_gray, imagesList[imageI]) #comparison
#print("le match est "+str(matchresult))
if matchresult < 0.13 and matchresult != 0:
sign_name = imageI.split('.')[0]
print("letter :" + sign_name + ",with a matching of :" +
str(matchresult))
cv.PutText(p_imgOriginal, sign_name, (5, 120), font, 255)
def DetectEyes(image, faceCascade, eyeCascade):
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 3
haar_flags = cv.CV_HAAR_DO_CANNY_PRUNING
# Allocate the temporary images
gray = cv.CreateImage((image.width, image.height), 8, 1)
smallImage = cv.CreateImage((cv.Round(
image.width / image_scale), cv.Round(image.height / image_scale)), 8,
1)
# Convert color input image to grayscale
cv.CvtColor(image, gray, cv.CV_BGR2GRAY)
# Scale input image for faster processing
cv.Resize(gray, smallImage, cv.CV_INTER_LINEAR)
# Equalize the histogram
cv.EqualizeHist(smallImage, smallImage)
# Detect the faces
faces = cv.HaarDetectObjects(smallImage, faceCascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
# If faces are found
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
face_region = cv.GetSubRect(image,
(x, int(y + (h / 4)), w, int(h / 2)))
cv.SetImageROI(
image,
(pt1[0], pt1[1], pt2[0] - pt1[0], int((pt2[1] - pt1[1]) * 0.7)))
# If there are no faces found there's no reason to continue
else:
sys.exit("No faces were found")
# NOTE: This returns the eye regions we're interested in
eyes = cv.HaarDetectObjects(image, eyeCascade, cv.CreateMemStorage(0), 1.3,
min_neighbors, haar_flags, (15, 15))
## Draw rectangles around the eyes found ##
if eyes:
# For each eye found
for eye in eyes:
# Draw a rectangle around the eye
cv.Rectangle(image, (eye[0][0], eye[0][1]),
(eye[0][0] + eye[0][2], eye[0][1] + eye[0][3]),
cv.RGB(255, 0, 0), 1, 8, 0)
# The following is commented out because as we're debugging we don't
# need to see the original image, just the regions of interest we have.
#cv.ResetImageROI(image)
return image
import cv2.cv as cv
im = cv.LoadImage("../img/lena.jpg", 3)
cv.SetImageROI(im, (50, 50, 150, 150))
cv.Zero(im)
#cv.Set(im, cv.RGB(100, 100, 100))
cv.ResetImageROI(im)
cv.ShowImage("Image", im)
cv.WaitKey(0)
import cv2.cv as cv
im = cv.LoadImage("meinv.jpg", cv.CV_8U)
cv.SetImageROI(im, (1, 1, 30, 30))
histsize = 256 #Because we are working on grayscale pictures
hist = cv.CreateHist([histsize], cv.CV_HIST_ARRAY, [[0, histsize]], 1)
cv.CalcHist([im], hist)
cv.NormalizeHist(
hist, 1) # The factor rescale values by multiplying values by the factor
_, max_value, _, _ = cv.GetMinMaxHistValue(hist)
if max_value == 0:
max_value = 1.0
cv.NormalizeHist(hist, 256 / max_value)
cv.ResetImageROI(im)
res = cv.CreateMat(im.height, im.width, cv.CV_8U)
cv.CalcBackProject([im], res, hist)
cv.Rectangle(im, (1, 1), (30, 30), (0, 0, 255), 2, cv.CV_FILLED)
cv.ShowImage("Original Image", im)
cv.ShowImage("BackProjected", res)
cv.WaitKey(0)
def DetectRedEyes(image, faceCascade, smileCascade, eyeCascade):
min_size = (20,20)
image_scale = 2
haar_scale = 1.1
min_neighbors = 2
haar_flags = 0
# Allocate the temporary images
gray = cv.CreateImage((image.width, image.height), 8, 1)
smallImage = cv.CreateImage((cv.Round(image.width / image_scale),cv.Round (image.height / image_scale)), 8 ,1)
# Convert color input image to grayscale
cv.CvtColor(image, gray, cv.CV_BGR2GRAY)
# Scale input image for faster processing
cv.Resize(gray, smallImage, cv.CV_INTER_LINEAR)
# Equalize the histogram
cv.EqualizeHist(smallImage, smallImage)
# Detect the faces
faces = cv.HaarDetectObjects(smallImage, faceCascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
global norm
# If faces are found
if faces:
#print faces
ratio = 1.
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
#print "face"
if h!=0:
ratio = h/norm
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
# print pt1
# print pt2
#cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 1, 8, 0)
#cv.PutText(image, "face"+str(h), pt1, font, cv.RGB(255, 0, 0))
face_region = cv.GetSubRect(image,(x,int(y + (h/4)),w,int(h/2)))
#split face
#cv.Rectangle(image, (pt1[0],(pt1[1] + (abs(pt1[1]-pt2[1]) / 2 ))), pt2, cv.RGB(0,255,0), 1, 8, 0)
#cv.PutText(image, "lower", (pt1[0],(pt1[1] + (abs(pt1[1]-pt2[1]) / 2 ))), font, cv.RGB(0, 255, 0))
cv.SetImageROI(image, (pt1[0],
(pt1[1] + int(abs(pt1[1]-pt2[1]) * 0.625 )),
pt2[0] - pt1[0],
int((pt2[1] - (pt1[1] + int(abs(pt1[1]-pt2[1]) * 0.625 ))))))
smiles = cv.HaarDetectObjects(image, smileCascade, cv.CreateMemStorage(0), 1.1, 5, 0, (15,15))
if smiles:
#print smiles
for smile in smiles:
cv.Rectangle(image,
(smile[0][0],smile[0][1]),
(smile[0][0] + smile[0][2], smile[0][1] + smile[0][3]),
cv.RGB(0, 0, 255), 1, 8, 0)
sizer = (smile[0][2]/ratio+smile[0][3]/ratio)#+(smile[1]/ratio))
#sizer = math.trunc(sizer)
#cv.PutText(image, "smile", (smile[0][0],smile[0][1]), font, cv.RGB(0, 0, 255))
cv.PutText(image,str(math.trunc(sizer**2)), (smile[0][0], smile[0][1] + smile[0][3] + 10), font, cv.RGB(0, 0, 255))
#print ((abs(smile[0][1] - smile[0][2]) / abs(pt1[0] - pt2[0])) * 100)
global smileneighbour
smileneighbour = sizer**2*2
cv.ResetImageROI(image)
#############################################################################
#############################################################################
cv.SetImageROI(image, (pt1[0], pt1[1], int(pt2[0]-pt1[0]), int(pt2[1] - pt1[1])) )
eyes = cv.HaarDetectObjects(image, eyeCascade,cv.CreateMemStorage(0),haar_scale, 5,haar_flags, (15,15))
if eyes:
# For each eye found
iii = 0
#print eyes
for eye in eyes:
# Draw a rectangle around the eye
cv.Rectangle(image,(eye[0][0],eye[0][1]),(eye[0][0] + eye[0][2],eye[0][1] + eye[0][3]), cv.RGB(0, 0, 255), 1, 8, 0)
a = math.trunc(float(eye[1])/ratio)
cv.PutText(image,str(a), (eye[0][0], eye[0][1] + eye[0][3]), font, cv.RGB(0, 0, 255))
global eyetot
eyetot += float(eye[1]*eye[1])/ratio
iii+=1
if iii==2:
iii = 0
break
cv.ResetImageROI(image)
cv.ResetImageROI(image)
return image
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
print h_pos
cv2.waitKey(0)
