def __sobel_image__(self,image,horizontal):
"""
apply the sobel operator to a given image on either the vertical or horizontal axis
basically copied from
http://stackoverflow.com/questions/10196198/how-to-remove-convexity-defects-in-a-sudoku-square
:param horizontal:
:return:
"""
if horizontal:
dy = cv2.Sobel(image,cv2.CV_16S,0,2)
dy = cv2.convertScaleAbs(dy)
cv2.normalize(dy,dy,0,255,cv2.NORM_MINMAX)
ret,close = cv2.threshold(dy,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(10,2))
else:
dx = cv2.Sobel(image,cv2.CV_16S,2,0)
dx = cv2.convertScaleAbs(dx)
cv2.normalize(dx,dx,0,255,cv2.NORM_MINMAX)
ret,close = cv2.threshold(dx,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(2,10))
close = cv2.morphologyEx(close,cv2.MORPH_CLOSE,kernel)
return close
def track2(bs,img_copy,img, avg): x = -1 y = -1 img_copy = cv2.GaussianBlur(img_copy,(5,5),0) cv2.accumulateWeighted(img_copy,avg,0.4) res = cv2.convertScaleAbs(avg) res = cv2.absdiff(img, res) _,processed_img = cv2.threshold( res, 7, 255, cv2.THRESH_BINARY ) processed_img = cv2.GaussianBlur(processed_img,(5,5),0) _,processed_img = cv2.threshold( processed_img, 240, 255, cv2.THRESH_BINARY ) processed_img = bs.bg_subtractor.apply(processed_img, None, 0.05) # img_thresh = cv2.morphologyEx(img_thresh, cv2.MORPH_OPEN, kernel) if np.count_nonzero(processed_img) > 5: # Get the largest contour contours, hierarchy = cv2.findContours(processed_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) areas = [cv2.contourArea(c) for c in contours] max_index = np.argmax(areas) # Make sure it's big enough if cv2.contourArea(contours[max_index]) >= MIN_BLOB_SIZE: cv2.drawContours(img, contours, max_index, (255, 255, 255), -1) x, y = getCentroid(contours[max_index]) return x, y
def SEU_Decode(SEU_Code):
SEU_Grey = cv2.cvtColor(SEU_Code, cv2.COLOR_BGR2GRAY)
cv2.threshold(SEU_Grey,255/2,255,cv2.THRESH_BINARY,SEU_Grey)
SEU_Code_Reshape4D = np.array(SEU_Grey).reshape((1, height, width, 1))
y_pred = model.predict(SEU_Code_Reshape4D)
return decode(y_pred)
def otsu(self, img):
# global thresholding
ret1,th1 = cv2.threshold(img,50,255,cv2.THRESH_BINARY)
per = np.percentile(img.ravel(), np.linspace(0,100,100))
print("percentile = {}".format(per))
# plt.hist(img.ravel(), 256)
# plt.figure()
# Otsu's thresholding
ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(img,(5,5),0)
ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
print("global = {}, ostu={}, gaussinaostu={}".format(ret1, ret2, ret3))
# plot all the images and their histograms
images = [img, 0, cv2.bitwise_not(th1),
img, 0, cv2.bitwise_not(th2),
blur, 0, cv2.bitwise_not(th3)]
titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)',
'Original Noisy Image','Histogram',"Otsu's Thresholding",
'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]
for i in range(3):
plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
plt.title(titles[i*3])
plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
plt.title(titles[i*3+1])
plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray')
plt.title(titles[i*3+2])
plt.show()
return
def border_mask(img, p1, p2, device, debug, color="black"):
# by using rectangle_mask to mask the edge of plotting regions you end up missing the border of the images by 1 pixel
# This function fills this region in
# note that p1 = (0,0) is the top left hand corner bottom right hand corner is p2 = (max-value(x), max-value(y))
# device = device number. Used to count steps in the pipeline
# debug = True/False. If True; print output image
if color=="black":
ix, iy = np.shape(img)
size = ix,iy
bnk = np.zeros(size, dtype=np.uint8)
cv2.rectangle(img = bnk, pt1 = p1, pt2 = p2, color = (255,255,255))
ret, bnk = cv2.threshold(bnk,127,255,0)
contour,hierarchy = cv2.findContours(bnk,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cv2.drawContours(bnk, contour, -1 ,(255,255,255), 5)
device +=1
if color=="gray":
ix, iy = np.shape(img)
size = ix,iy
bnk = np.zeros(size, dtype=np.uint8)
cv2.rectangle(img = bnk, pt1 = p1, pt2 = p2, color = (192,192,192))
ret, bnk = cv2.threshold(bnk,127,255,0)
contour,hierarchy = cv2.findContours(bnk,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cv2.drawContours(bnk, contour, -1 ,(192,192,192), 5)
device +=1
if debug:
print_image(bnk, (str(device) + '_brd_mskd_' + '.png'))
return device, bnk, contour, hierarchy
def rectangle_mask(img, p1, p2, device, debug, color="black"):
# takes an input image and returns a binary image masked by a rectangular area denoted by p1 and p2
# note that p1 = (0,0) is the top left hand corner bottom right hand corner is p2 = (max-value(x), max-value(y))
# device = device number. Used to count steps in the pipeline
# debug = True/False. If True; print output image
# get the dimensions of the input image
ix, iy = np.shape(img)
size = ix,iy
# create a blank image of same size
bnk = np.zeros(size, dtype=np.uint8)
# draw a rectangle denoted by pt1 and pt2 on the blank image
if color=="black":
cv2.rectangle(img = bnk, pt1 = p1, pt2 = p2, color = (255,255,255))
ret, bnk = cv2.threshold(bnk,127,255,0)
contour,hierarchy = cv2.findContours(bnk,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
# make sure entire rectangle is within (visable within) plotting region or else it will not fill with thickness = -1
# note that you should only print the first contour (contour[0]) if you want to fill with thickness = -1
# otherwise two rectangles will be drawn and the space between them will get filled
cv2.drawContours(bnk, contour, 0 ,(255,255,255), -1)
device +=1
if color=="gray":
cv2.rectangle(img = bnk, pt1 = p1, pt2 = p2, color = (192,192,192))
ret, bnk = cv2.threshold(bnk,127,255,0)
contour,hierarchy = cv2.findContours(bnk,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
# make sure entire rectangle is within (visable within) plotting region or else it will not fill with thickness = -1
# note that you should only print the first contour (contour[0]) if you want to fill with thickness = -1
# otherwise two rectangles will be drawn and the space between them will get filled
cv2.drawContours(bnk, contour, 0 ,(192,192,192), -1)
if debug:
print_image(bnk, (str(device) + '_roi.png'))
return device, bnk, contour, hierarchy
def _generate_training_set(img, image_file):
save_location = "images/training/"
_, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
_, regions = cv2.connectedComponents(img, img)
if not os.path.exists("../images/cc"):
os.makedirs("../images/cc")
cv2.imwrite("../images/cc/cc.png", regions)
cc = cv2.imread("../images/cc/cc.png", 0)
_, cc_vis = cv2.threshold(cc, 1, 255, cv2.THRESH_BINARY)
_, contours, hierarchy = cv2.findContours(cc_vis, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
idx = 0
for cnt in contours:
area = cv2.contourArea(cnt)
if area < 50 or area > 1000:
continue
if len(cnt) < 5:
continue
idx += 1
x, y, w, h = cv2.boundingRect(cnt)
roi = img[y: y + h, x: x + w]
name = image_file.split('.')[0]
inverted = (255 - roi)
cv2.imwrite(save_location + name + str(idx) + '.jpg', inverted)
cv2.waitKey(0)
def get_vessels(img,side):
#convert to grayscale
#img_gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#equilized = cv2.equalizeHist(img_gray)
green_channel = img[:,:,1]
threshold =np.max(green_channel)*0.9
#crop image
gch_crop1=green_channel[:, (green_channel != 0).sum(axis=0) != 0]
gch_crop2=gch_crop1[(gch_crop1 != 0).sum(axis=1) != 0,:]
green_channel=gch_crop2
#rotate by optical disc
dummy,gch_bin = cv2.threshold(green_channel, threshold,255 ,cv2.THRESH_BINARY)
i,j = np.unravel_index(gch_bin.argmax(), gch_bin.shape)
if ((gch_bin.shape[1]/2 < j) and side=='left') or ((gch_bin.shape[1]/2 > j) and side=='right'):
green_channel=np.rot90(green_channel,2)
#25 x 25 median filter
gch_mf = cv2.medianBlur(green_channel,35)
#gch_nl = cv2.fastNlMeansDenoising(green_channel,h=10)
gch_norm = green_channel - gch_mf
gch_norm_norm = cv2.medianBlur(gch_norm,35)
#convert to binary image
thresh,gch_norm_bin = cv2.threshold(gch_norm,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
gch_norm_bin_norm = cv2.medianBlur(gch_norm_bin,35)
return gch_norm_bin_norm
def find_hottest_points(cv_image):
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(3,3))
#gray = clahe.apply(img)
gray = clahe.apply(cv_image)
gray = cv2.GaussianBlur (gray, (21,21), 0)
min_thresh = cv2.threshold(gray, min_th, 255, cv2.THRESH_BINARY)[1]
max_thresh = cv2.threshold(gray, max_th, 255, cv2.THRESH_BINARY_INV)[1]
thresh = cv2.bitwise_and(min_thresh, max_thresh)
thresh = cv2.dilate(thresh, None, iterations = 2)
(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
if cv2.contourArea(c) > min_area and cv2.contourArea(c) < max_area:
(x,y,w,h) = cv2.boundingRect(c)
# cv2.rectangle(cv_image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.rectangle(cv_image, (x, y), (x+w, y+h), 0, 2)
continue
cv2.imshow("region_detector", cv_image)
cv2.moveWindow("region_detector",900,0)
cv2.imshow("band_threshold_image", thresh)
cv2.moveWindow("band_threshold_image",900,400)
cv2.waitKey(1)
def _divide(self):
block_size = self.spec.block_size # shortcut
half_block = (block_size-1)/2
rows, columns = self.dividing.nonzero()
for i in range(len(rows)):
row = rows[i]
column = columns[i]
write_block(self._cell_block, self.cells, row, column, block_size)
cv2.filter2D(self._cell_block, cv2.CV_32F, self._tension_kernel,
self._probability, borderType=cv2.BORDER_CONSTANT)
cv2.threshold(self._probability, self._tension_min, 0,
cv2.THRESH_TOZERO, self._probability)
self._probability[self._cell_block] = 0
self._probability **= self.spec.tension_power
self._probability *= self._distance_kernel
# optimized version of np.random.choice
np.cumsum(self._probability.flat, out=self._cumulative)
total = self._cumulative[-1]
if total < 1.0e-12:
# no viable placements, we'll have precision problems anyways
continue
self._cumulative /= total
index = self._indices[np.searchsorted(self._cumulative,
rdm.random())]
local_row, local_column = np.unravel_index(index,
self._probability.shape)
self.set_alive(row+(local_row-half_block),
column+(local_column-half_block))
def perform_changes_task2(img):
cv2.GaussianBlur(img, (3, 3), 0, img)
cv2.Laplacian(img, 0, img, 1)
cv2.GaussianBlur(img, (19, 3), 7, img)
cv2.threshold(img, 10, 255, cv2.THRESH_BINARY, img)
#cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 5, img)
return img
def otsuTwo(img, img_file, man_img, mask=None):
# blur = cv2.GaussianBlur(img, (5,5),0)
blur = cv2.bilateralFilter(img, 5, 100, 100)
thresholds = multithresholdOtsu(blur,mask)
th1 = thresholds[0]
th2 = thresholds[1]
if mask is None:
ret, thresh1 = cv2.threshold(blur,th1,255,cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(blur,th2,255,cv2.THRESH_BINARY_INV)
else:
combined_img = cv2.bitwise_and(blur, blur, mask=mask)
ret, thresh1 = cv2.threshold(combined_img,th1,255,cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(combined_img,th2,255,cv2.THRESH_BINARY_INV)
out_img_o = cv2.bitwise_and(thresh1, thresh2, mask=None)
out_info_o = "_otsu_%d-%d" % (th1, th2)
out_str_o = out_info_o + '.png'
out_file_o = re.sub(r'\.jpg', out_str_o, img_file)
cv2.imwrite(out_file_o, out_img_o)
t = evaluation.findTotals(out_img_o, man_img)
f = open('o2_all.txt', 'a')
f.write(img_file + " " + str(t[0]) + " " + str(t[1]) + " " + str(t[2]) + " " + str(t[3]) + "\n")
f.close()
def threshold_image(self, channel):
if channel == "hue":
minimum = self.hue_min
maximum = self.hue_max
elif channel == "saturation":
minimum = self.sat_min
maximum = self.sat_max
elif channel == "value":
minimum = self.val_min
maximum = self.val_max
(t, tmp) = cv2.threshold(
self.channels[channel], # src
maximum, # threshold value
0, # we dont care because of the selected type
cv2.THRESH_TOZERO_INV # t type
)
(t, self.channels[channel]) = cv2.threshold(
tmp, # src
minimum, # threshold value
255, # maxvalue
cv2.THRESH_BINARY # type
)
if channel == 'hue':
# only works for filtering red color because the range for the hue
# is split
self.channels['hue'] = cv2.bitwise_not(self.channels['hue'])
def GenCh(f,val, data_shape1, data_shape2, bg_gray, text_gray, text_position):
img=Image.new("L", (data_shape1,data_shape2),bg_gray)
draw = ImageDraw.Draw(img)
draw.text((0, 0),val,text_gray,font=f)
#draw.text((0, text_position),val.decode('utf-8'),0,font=f)
A = np.array(img)
#二值化,确定文字精确的左右边界
if bg_gray > text_gray:
ret,bin = cv2.threshold(A,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
else:
ret,bin = cv2.threshold(A,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#cv2.imshow('A',A)
#cv2.imshow('bin',bin)
left = -1
right = 10000
for i in range(0,bin.shape[1]):
if np.sum(bin[:,i]) > 0:
left = i
break
for i in range(bin.shape[1]-1,0,-1):
if np.sum(bin[:,i]) > 0:
right = i
break
dst = A[:,left:right+1]
#cv2.imshow('dst',dst)
#cv2.waitKey()
return dst
def main(argv):
# compress the image to < 1000 width first!
im = cv2.imread(argv[0])
hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
lower_yellow = np.array([20, 60, 60])
upper_yellow = np.array([40, 255, 255])
mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
res = cv2.bitwise_and(im, im, mask=mask)
ret, binary = cv2.threshold(res,127,255,cv2.THRESH_BINARY)
gray = cv2.cvtColor(binary,cv2.COLOR_BGR2GRAY)
ret,gray = cv2.threshold(gray,127,255,0)
im2 = im.copy()
contours, hier = cv2.findContours(gray,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
ct = 0
for cnt in contours:
if 800<cv2.contourArea(cnt)<10000:
(x,y,w,h) = cv2.boundingRect(cnt)
cv2.rectangle(im2,(x,y),(x+w,y+h),0,-1)
crop_im = im[y:y+h, x:x+w]
crop_im = cv2.cvtColor(crop_im, cv2.COLOR_BGR2GRAY)
cv2.imwrite("crop_"+str(ct)+".jpg", crop_im)
ct += 1
cv2.imshow("testing", im2)
cv2.waitKey(0)
def get_library():
# READ THE LIBRARY IMAGES
img_cube = cv2.imread('./Library/Cube.jpg')
img_hexagon = cv2.imread('./Library/Hexagon.jpg')
img_star = cv2.imread('./Library/Star.jpg')
# CANNY EDGE ALGORITHM
img_cube = cv2.Canny(img_cube, 1200, 100)
img_hexagon = cv2.Canny(img_hexagon, 1200, 100)
img_star = cv2.Canny(img_star, 1200, 100)
# OPENING (EROSION FOLLOWED BY DILATION)
kernel = np.ones((5, 5), np.uint8)
img_cube = cv2.morphologyEx(img_cube, cv2.MORPH_GRADIENT, kernel)
img_hexagon = cv2.morphologyEx(img_hexagon, cv2.MORPH_GRADIENT, kernel)
img_star = cv2.morphologyEx(img_star, cv2.MORPH_GRADIENT, kernel)
# THRESHOLD (INVERSE BINARY)
ret, img_cube = cv2.threshold(img_cube, 0, 255, cv2.THRESH_BINARY_INV)
ret, img_hexagon = cv2.threshold(
img_hexagon, 0, 255, cv2.THRESH_BINARY_INV)
ret, img_star = cv2.threshold(img_star, 0, 255, cv2.THRESH_BINARY_INV)
# SURF - FIND KEYPOINTS AND DESCRIPTORS
surf = cv2.SURF()
(cube_kpts, cube_dpts) = surf.detectAndCompute(img_cube, None)
(hexagon_kpts, hexagon_dpts) = surf.detectAndCompute(img_hexagon, None)
(star_kpts, star_dpts) = surf.detectAndCompute(img_star, None)
# LIBRARY IMAGE DICTIONARY - IMAGE:DESCRIPTORS
library = {CUBE: cube_dpts, HEXAGON: hexagon_dpts, STAR: star_dpts}
return library
def find_lines(image):
"""
Compute the layout from the image
"""
draw_image = numpy.array(image)
tmp_im = numpy.array(image)
ret, thres_im = cv2.threshold(tmp_im, 4, 255, cv2.THRESH_BINARY)
thres_im = ndimage.uniform_filter(thres_im, (1, 50))
ret, thres_im = cv2.threshold(thres_im, 50, 255, cv2.THRESH_BINARY)
lines = cv2.HoughLinesP(thres_im, 1, math.pi / 4.0, 100, None, 100, 10)
tmp_mask = np.zeros(thres_im.shape, np.uint8)
for l in lines[0]:
a = angle((l[0], l[1]), (l[2], l[3]))
if abs(a) < 1.0:
cv2.line(tmp_mask, (l[0], l[1]), (l[2], l[3]), 255, 1)
contours, hier = cv2.findContours(tmp_mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_TC89_L1)
boxes = []
mask = np.zeros(image.shape, np.uint8)
draw_image = cv2.cvtColor(draw_image, cv2.COLOR_GRAY2BGR)
for cnt in contours:
box = cv2.boundingRect(cnt)
x, y, width, height = box
if width > 20 and height > 2 and height < 40:
boxes.append((x, y, width, height))
return boxes
def mask_shiny(self):
pygrip.desaturate(self.ir, dst=self.tmp16_1)
into_uint8(self.tmp16_1, dst=self.tmp8_1)
pygrip.blur(self.tmp8_1, pygrip.MEDIAN_BLUR, 1, dst=self.tmp8_2)
cv2.threshold(self.tmp8_2, 80, 0xff, cv2.THRESH_BINARY, dst=self.mask8)
# grr threshold operates on matrices of unsigned bytes
into_uint16_mask(self.mask8, dst=self.mask16)
def imgToMove(msg):
#Convert it to an OpenCV image
bridge = cv_bridge.CvBridge()
cvImg = np.array(bridge.imgmsg_to_cv(msg, "bgr8"), dtype=np.uint8)
#Threshold it, parameters from ImageSlicer.cpp
hsvImg = cv2.cvtColor(cvImg, cv2.cv.CV_BGR2HSV)
H,S,V = cv2.split(hsvImg)
H = cv2.threshold(H, 165, 65536, cv2.cv.CV_THRESH_BINARY)
S = cv2.threshold(S, 45, 65536, cv2.cv.CV_THRESH_BINARY)
out = cv2.bitwise_and(H[1], S[1])
#Slice it and count white pixels
slices = 5
counts = []
regionWidth = out.shape[1]/slices
for ii in range(slices):
roi = out[0:out.shape[0],ii*regionWidth:(ii*regionWidth)+regionWidth]
counts.append(cv2.countNonZero(roi))
#Decide on a move
bestMove = "N"
leftCount = rightCount = 0
for ii in range(3):
leftCount += counts[ii]
rightCount += counts[4-ii]
if abs(leftCount - rightCount) > 500:
#Enough difference, we move
if leftCount > rightCount:
bestMove = "L"
else:
bestMove = "R"
else:
#Not different enough, no move
bestMove = "N"
return bestMove
def findHP(img):
statuses = {'none': -1, 'dead' : 0, 'lhalf' : 1, 'mhalf' : 2, 'full' : 3}
# hpcolor = [231, 73, 132]
hpcolor = [107, 101, 107]
hp = grabHP()
gray = cv2.cvtColor(hp, cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray,120,255,cv2.THRESH_TOZERO_INV)
ret,th1 = cv2.threshold(th1,100,255,cv2.THRESH_TOZERO)
# cv2.imwrite('th1' + str(int(time.time())) + '.png',th1)
(cnts, hierarchy) = cv2.findContours(th1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
if (len(cnts) == 0):
return statuses['dead']
left = list(cnts[0][cnts[0][:,:,0].argmin()][0])
px = hp[left[1], left[0]];
# print px
if (hpcolor != px).any():
return statuses['none']
leftx = list(cnts[0][cnts[0][:,:,0].argmin()][0])[0]
rightx = list(cnts[0][cnts[0][:,:,0].argmax()][0])[0]
diff = rightx - leftx
if diff > 140:
return statuses['full']
if diff >= 75:
return statuses['mhalf']
if diff < 75:
return statuses['lhalf']
return statuses['dead']
def get_binary_from_hsv(card):
# convert from BGR colorspace to HSV colorspace
hsv = cv2.cvtColor(card, cv2.COLOR_BGR2HSV)
# separate hue, saturation, and value into three images
hue, sat, val = [np.array([[col[i] for col in row] for row in hsv]) for i in xrange(3)]
# get binary representation of saturation image
# higher threshold = less white
_, bin_sat = cv2.threshold(np.array(sat), thresh=55, maxval=255, type=cv2.THRESH_BINARY)
# bin_sat = cv2.GaussianBlur(bin_sat, ksize=(5, 5), sigmaX=0)
# get binary representation of value image
# higher threshold = more white
_, bin_val = cv2.threshold(np.array(val), thresh=140, maxval=255, type=cv2.THRESH_BINARY_INV)
bin_sat_val = cv2.bitwise_or(bin_sat, bin_val)
# erosion followed by morphological opening to erase noise and fill gaps
# in shapes
kernel_e = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2))
kernel_d = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 8))
bin_sat_val = cv2.erode(bin_sat_val, kernel_e)
bin_sat_val = cv2.morphologyEx(bin_sat_val, cv2.MORPH_CLOSE, kernel_d)
return bin_sat_val, hue, sat, val
def create_merged_map():
# copy sat images
for data_type in ["train", "test", "valid"]:
out_dir = "data/mass_merged/%s/sat" % data_type
if not os.path.exists(out_dir):
os.makedirs(out_dir)
for fn in glob.glob("data/mass_buildings/%s/sat/*.tiff" % data_type):
shutil.copy(fn, "%s/%s" % (out_dir, os.path.basename(fn)))
road_maps = dict([(os.path.basename(fn).split(".")[0], fn) for fn in glob.glob("data/mass_roads/*/map/*.tif")])
# combine map images
for data_type in ["train", "test", "valid"]:
out_dir = "data/mass_merged/%s/map" % data_type
if not os.path.exists(out_dir):
os.makedirs(out_dir)
for fn in glob.glob("data/mass_buildings/%s/map/*.tif" % data_type):
base = os.path.basename(fn).split(".")[0]
building_map = cv.imread(fn, cv.IMREAD_GRAYSCALE)
road_map = cv.imread(road_maps[base], cv.IMREAD_GRAYSCALE)
_, building_map = cv.threshold(building_map, 0, 1, cv.THRESH_BINARY)
_, road_map = cv.threshold(road_map, 0, 1, cv.THRESH_BINARY)
h, w = road_map.shape
merged_map = np.zeros((h, w))
merged_map += building_map
merged_map += road_map * 2
merged_map = np.where(merged_map > 2, 2, merged_map)
cv.imwrite("data/mass_merged/%s/map/%s.tif" % (data_type, base), merged_map)
print(merged_map.shape, fn)
merged_map = np.array(
[np.where(merged_map == 0, 1, 0), np.where(merged_map == 1, 1, 0), np.where(merged_map == 2, 1, 0)]
)
merged_map = merged_map.swapaxes(0, 2).swapaxes(0, 1)
cv.imwrite("data/mass_merged/%s/map/%s.png" % (data_type, base), merged_map * 255)
def _draw_board(self, dst):
hsvdst = cv2.cvtColor(dst,cv2.COLOR_BGR2HSV)
step = CHESS_SIZE/8
black_val = cv2.getTrackbarPos('black', 'warp')
white_val = cv2.getTrackbarPos('white', 'warp')
yc = cv2.getTrackbarPos('ycutoff', 'warp')
font = cv2.FONT_HERSHEY_SIMPLEX
#dst = hsvdst[...,2]
col = dst
dst = cv2.cvtColor(dst,cv2.COLOR_BGR2GRAY)
rv, bl_dst = cv2.threshold(dst, black_val, 255, cv2.THRESH_BINARY)
rv, wt_dst = cv2.threshold(dst, white_val, 255, cv2.THRESH_BINARY)
for i in range(8):
for j in range(8):
y = self.BORDER + step*i
x = self.BORDER + step*j
bl_cnt = np.sum(bl_dst[y-yc:y-yc + step,x:x+step] == 0)
wt_cnt = np.sum(wt_dst[y-yc:y+step-yc,x:x+step] == 255)
col[y-yc:y-yc+step,x:x+step,2] = np.zeros((step, step))
s = str(bl_cnt) + "/" + str(wt_cnt)
if bl_cnt > 500:
cv2.circle(col, (x+step/2, y+step/2), 25, BLACK, -1)
elif wt_cnt >= 75:
cv2.circle(col, (x+step/2, y+step/2), 25, WHITE, -1)
cv2.putText(col, s ,(x, y+step/2), font, 0.5,BLUEGREEN,2,cv2.LINE_AA)
def investigate_threshold(frame):
"""Try different threshold types a visualize the results.
otsu:
Args:
frame (np.array): Input frame.
"""
ret1, th1 = cv2.threshold(frame, 127, 255, cv2.THRESH_BINARY)
ret2, th2 = cv2.threshold(frame, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(frame, (3, 3), 0)
ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
#print th3
# plot all the images and their histograms
images = [frame, 0, th1,
frame, 0, th2,
blur, 0, th3]
titles = ['Original Noisy Image', 'Histogram', 'Global Thresholding (v=127)',
'Original Noisy Image', 'Histogram', "Otsu's Thresholding",
'Gaussian filtered Image', 'Histogram', "Otsu's Thresholding"]
for i in xrange(3):
plt.subplot(3, 3, i * 3 + 1), plt.imshow(images[i * 3], 'gray')
plt.title(titles[i * 3]), plt.xticks([]), plt.yticks([])
plt.subplot(3, 3, i * 3 + 2), plt.hist(images[i * 3].ravel(), 256)
plt.title(titles[i * 3 + 1]), plt.xticks([]), plt.yticks([])
plt.subplot(3, 3, i * 3 + 3), plt.imshow(images[i * 3 + 2], 'gray')
plt.title(titles[i * 3 + 2]), plt.xticks([]), plt.yticks([])
plt.show()
def detect(capture, prev_images):
# Capture a new frame
new_frame = capture.grab_frame()
# Not enough frames: no detection, just store this one
if len(prev_images) < 2:
return None, None, new_frame, None
# Everything to grayscale
prev_images = [prev_images[1], prev_images[0], new_frame]
prev_images = [cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
for prev_frame in prev_images]
prev_frame, current_frame, next_frame = prev_images
# Diff
d1 = cv2.absdiff(prev_frame, next_frame)
d2 = cv2.absdiff(next_frame, current_frame)
motion = cv2.bitwise_and(d1, d2)
# Threshold & erode
cv2.threshold(motion, config.DIFF_THRESHOLD, 255, cv2.THRESH_BINARY,
dst=motion)
cv2.erode(motion, kernel_ero, dst=motion)
# Find and count changes
number_of_changes, location, std_dev = detect_motion(motion)
return number_of_changes, std_dev, new_frame, location
def ComputeDescriptors(self,RGB,Depth,dep_mask,h):
dep = np.float32(Depth)
dep_mask =cv2.bitwise_not(dep_mask)
ret, mask = cv2.threshold(dep, 1.7, 1, cv2.THRESH_BINARY_INV)
mask = np.uint8(mask)
ret, mask2 = cv2.threshold(dep, 0.01, 1, cv2.THRESH_BINARY)
mask2 = np.uint8(mask2)
mask = cv2.bitwise_and(mask,mask2)
mask = cv2.bitwise_and(mask,dep_mask)
if h:
masked_data = cv2.bitwise_and(RGB, RGB, mask=mask)
masked_data = cv2.bitwise_and(masked_data, masked_data, mask=mask2)
sp = cv2.cvtColor(masked_data, cv2.COLOR_RGB2GRAY)
sp = cv2.GaussianBlur(sp, (5, 5),10)
fd, imn = hog(dep, self.orientations, self.pixels_per_cell, self.cells_per_block,
self.visualize, self.normalize)
if self.HogDepth:
fdn,im = hog(sp, self.orientations, self.pixels_per_cell, self.cells_per_block,
self.visualize, self.normalize)
fd = np.concatenate((fd, fdn))
else:
fd = []
fgrid = np.array([])
for i in xrange(4):
for j in xrange(4):
sub = RGB[25*i:25*(i+1),25*j:25*(j+1)]
sub_mask = mask[25*i:25*(i+1),25*j:25*(j+1)]
fsub = self.ComputeHC(sub,sub_mask)
fgrid = np.concatenate((fgrid,fsub))
fd2 = fgrid.copy()
return fd,fd2,masked_data
def ReadImage(self, fileIndex, show):
# read in image
# print "Read in image: ", fileIndex
colorFileName = 'trainImageDistorted\\color_'+str(fileIndex)+'.jpg'
depthFileName = 'trainImageDistorted\\depth_'+str(fileIndex)+'.jpg'
maskFileName = 'trainImageDistorted\\mask_'+str(fileIndex)+'.jpg'
labelFileName = 'trainImageDistorted\\label_'+str(fileIndex)+'.jpg'
imgBGR = cv2.imread(colorFileName, 1)
imgGray = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2GRAY)
imgDepth = cv2.imread(depthFileName, 0)
imgMaskGray = cv2.imread(maskFileName, 0)
# erod the mask so that the boundary won't bother us
# imgMaskGray = cv2.erode(imgMaskGray, np.ones((5,5),np.uint8))
_, imgMask = cv2.threshold(imgMaskGray,127,255,cv2.THRESH_BINARY)
# the label
imgLabel = cv2.imread(labelFileName, 1)
imgLabelGray = cv2.cvtColor(imgLabel, cv2.COLOR_BGR2GRAY)
_, imgLabelBW = cv2.threshold(imgLabelGray,10,255,cv2.THRESH_BINARY)
# rescale depth and mask
imgDepth, imgMask = self.RescaleDepth(imgBGR, imgDepth, imgMask)
# histogram equalization on the gray image
imgGrayHistEqu = self.IlluminationNorm(imgGray, show = False)
if show == True:
cv2.imshow("imgBGR", imgBGR)
cv2.imshow("imgGray", imgGray)
cv2.imshow('imgDepth', imgDepth)
cv2.imshow('imgMask', imgMask)
cv2.imshow('imgLabelBW', imgLabelBW)
WaitKey(30)
return imgBGR, imgGray, imgGrayHistEqu, imgDepth, imgMask, imgLabelBW
def draw_circle(event,x,y,flags,param):
global i
global punkte
if event == cv2.EVENT_LBUTTONDBLCLK:
draw_overlay[:] = 0
cv2.circle(draw_overlay,(x,y),1,(0,0,255),2)
ret, mask = cv2.threshold(draw_overlay, 10, 255, cv2.THRESH_BINARY)
img2gray = cv2.cvtColor(draw_overlay,cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)
rows,cols,channels = img.shape
roi = img[0:rows, 0:cols ]
img_bg = cv2.bitwise_and(roi,roi,mask = mask_inv)
dst = cv2.add(img_bg,draw_overlay)
font = cv2.FONT_HERSHEY_SIMPLEX
text = 'X: ' + str(x) + ' Y: ' + str(y) + ' Press Escape to close the Window!'
cv2.putText(dst, text, (10, 20), font, 0.5,
(0,0,0), 1, cv2.LINE_AA)
cv2.imshow('image',dst)
punkte[i,0] = x
punkte[i,1] = y
i = i + 1
print
print punkte
if i>3:
i = 0
deform()
def scan2hdf5 (out_path, image_dir, label_dir, folds=0, resize=255, gray=False, useall=False):
images = find_images(image_dir)
labels = find_images(label_dir)
all = []
chs = 1 if gray else 3
for key, ipath in images.iteritems():
image = cv2.imread(ipath, cv2.IMREAD_GRAYSCALE if gray else cv2.IMREAD_COLOR)
image = cv2.resize(image, (resize, resize)).reshape(1, chs, resize, resize)
lpath = labels.get(key, None)
if lpath:
label = cv2.imread(lpath, cv2.IMREAD_GRAYSCALE)
#_,label = cv2.threshold(label, 1,255,cv2.THRESH_BINARY_INV)
contour_img = lpath
if 'c_' in lpath:
_,label = cv2.threshold(label, 1,255,cv2.THRESH_BINARY_INV)
else:
_,label = cv2.threshold(label, 127,255,cv2.THRESH_BINARY_INV)
label = cv2.resize(label, (resize, resize)).reshape(1, 1, resize, resize)
elif useall: # no label, set all negative
label = np.zeros((1, 1, resize, resize), dtype=np.uint8)
else:
logging.warning('no label found for {}'.format(ipath))
continue
all.append((image, label))
pass
logging.info("found {} images".format(len(all)))
save_hd5py(out_path, all, folds)
pass
def findTarget(img):
template_tg = cv2.imread('template_target2.png', 0)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray,253,255,cv2.THRESH_TOZERO_INV)
ret,th3 = cv2.threshold(th1,251,255,cv2.THRESH_BINARY)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 15))
closed = cv2.morphologyEx(th3, cv2.MORPH_CLOSE, kernel)
closed = cv2.erode(closed, None, iterations = 3)
closed = cv2.dilate(closed, None, iterations = 2)
(cnts, hierarchy) = cv2.findContours(closed,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
approxes = []
hulls = []
for cnt in cnts:
approxes.append(cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True))
hulls.append(cv2.convexHull(cnt))
left = list(cnt[cnt[:,:,0].argmin()][0])
right = list(cnt[cnt[:,:,0].argmax()][0])
print 'left x' + str(left[0])+ 'y '+ str(left[1])
print 'right x' + str(right[0])+ 'y '+ str(right[1])
center = round((right[0]+left[0])/2)
center = int(center)
moveMouse(center-10,left[1]+70)
if (findFromTargeted(template_tg, left, right)):
autoit.mouse_click('left', center-10, left[1]+70)
return True
pyautogui.moveTo(center,left[1]+70)
moveMouse(center,left[1]+70)
if (findFromTargeted(template_tg, left, right)):
autoit.mouse_click('left', center+10, left[1]+70)
return True
def getDetBoxes_core(textmap, linkmap, text_threshold, link_threshold, low_text):
# prepare data
linkmap = linkmap.copy()
textmap = textmap.copy()
img_h, img_w = textmap.shape
""" labeling method """
ret, text_score = cv2.threshold(textmap, low_text, 1, 0)
ret, link_score = cv2.threshold(linkmap, link_threshold, 1, 0)
text_score_comb = np.clip(text_score + link_score, 0, 1)
nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats(
text_score_comb.astype(np.uint8), connectivity=4
)
det = []
mapper = []
for k in range(1, nLabels):
# size filtering
size = stats[k, cv2.CC_STAT_AREA]
if size < 10:
continue
# thresholding
if np.max(textmap[labels == k]) < text_threshold:
continue
# make segmentation map
segmap = np.zeros(textmap.shape, dtype=np.uint8)
segmap[labels == k] = 255
# remove link area
segmap[np.logical_and(link_score == 1, text_score == 0)] = 0
x, y = stats[k, cv2.CC_STAT_LEFT], stats[k, cv2.CC_STAT_TOP]
w, h = stats[k, cv2.CC_STAT_WIDTH], stats[k, cv2.CC_STAT_HEIGHT]
niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2)
sx, ex, sy, ey = (x - niter, x + w + niter + 1, y - niter, y + h + niter + 1)
# boundary check
if sx < 0:
sx = 0
if sy < 0:
sy = 0
if ex >= img_w:
ex = img_w
if ey >= img_h:
ey = img_h
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1 + niter, 1 + niter))
segmap[sy:ey, sx:ex] = cv2.dilate(segmap[sy:ey, sx:ex], kernel)
# make box
np_temp = np.roll(np.array(np.where(segmap != 0)), 1, axis=0)
np_contours = np_temp.transpose().reshape(-1, 2)
rectangle = cv2.minAreaRect(np_contours)
box = cv2.boxPoints(rectangle)
# boundary check due to minAreaRect may have out of range values
# (see https://docs.opencv.org/3.4/d3/dc0/group__imgproc__shape.html#ga3d476a3417130ae5154aea421ca7ead9)
for p in box:
if p[0] < 0:
p[0] = 0
if p[1] < 0:
p[1] = 0
if p[0] >= img_w:
p[0] = img_w
if p[1] >= img_h:
p[1] = img_h
# align diamond-shape
w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])
box_ratio = max(w, h) / (min(w, h) + 1e-5)
if abs(1 - box_ratio) <= 0.1:
l, r = min(np_contours[:, 0]), max(np_contours[:, 0])
t, b = min(np_contours[:, 1]), max(np_contours[:, 1])
box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32)
# make clock-wise order
startidx = box.sum(axis=1).argmin()
box = np.roll(box, 4 - startidx, 0)
box = np.array(box)
det.append(box)
mapper.append(k)
return det, labels, mapper
def splitImg(self):
# 找出各輪廓的距離
def find_if_close(cnt1, cnt2, distance):
row1, row2 = cnt1.shape[0], cnt2.shape[0]
for i in xrange(row1):
for j in xrange(row2):
dist = np.linalg.norm(cnt1[i] - cnt2[j])
if abs(dist) < distance:
return True
elif i == row1 - 1 and j == row2 - 1:
return False
# 傳入輪廓陣列 回傳各陣列的距離分級
def getStatus(contours, distance):
LENGTH = len(contours)
status = np.zeros((LENGTH, 1)) # 用來儲存每個輪廓的等級 等級一樣的會合併為同一個輪廓
for i, cnt1 in enumerate(contours):
x = i
if i != LENGTH - 1:
for j, cnt2 in enumerate(contours[i + 1:]):
x = x + 1
dist = find_if_close(cnt1, cnt2, distance)
if dist == True:
val = min(status[i], status[x])
status[x] = status[i] = val
else:
if status[x] == status[i]:
status[x] = i + 1
return status
# 合併各輪廓
def MergeEachCnts(contours, distance, unified=[], excuteTimes=0):
'''
:param contours: 要判斷距離的輪廓
:param unified: 已經判斷完 合併後的輪廓
:return:
'''
# print('============\n執行次數:'+ str(excuteTimes))
unsucess = [] # 面積過大的輪廓放進來重新判斷
# 取得各輪廓距離的分類
status = getStatus(contours, distance)
# print('status:\n')
# print(status)
# print('areas and width:\n')
maximum = int(status.max()) + 1
for i in xrange(maximum):
pos = np.where(status == i)[0]
if pos.size != 0:
cont = np.vstack(
contours[i]
for i in pos) # 把輪廓陣列裡的輪廓合併 pos的index對應到輪廓陣列的index
# hull = cv2.convexHull(cont) # 將合併後的輪廓轉凸包
# 如果面積大於200 就是錯誤合併兩個數字了
area = cv2.contourArea(cont)
(x, y, w, h) = cv2.boundingRect(cont)
# print(area)
# print(x, y, w, h)
# 當面積大於200或寬度大於20或高度大於20且distance大於0 才會加到錯誤判斷輪廓的陣列
if (area > 200 or w > 20 or h > 20) and distance > 0:
for i in pos:
unsucess.append(contours[i])
# 如果distance已經小於0 就把未經合併的原始輪廓加到unified
elif area > 200 and distance <= 0:
for i in pos:
unified.append(contours[i])
# 如果面積<26且寬高皆小於9 判斷為雜點
elif (area < 26 and w < 9 and h < 9):
pass
else:
unified.append(cont)
if len(unsucess) > 0:
return MergeEachCnts(unsucess, distance - 3, unified,
excuteTimes + 1)
else:
return unified
# 將圖片二值化 以便做邊緣檢測
colorIm = self.im
self.im = cv2.cvtColor(self.im, cv2.COLOR_BGR2GRAY)
self.retval, self.im = cv2.threshold(self.im, 200, 255,
cv2.THRESH_BINARY_INV)
# 找出輪廓
_, contours, hierarchy = cv2.findContours(self.im.copy(),
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# 取出輪廓的範圍、區域大小 且過濾面積太小的輪廓
contours = [c for c in contours if 4 < cv2.contourArea(c) < 1000]
# 將鄰近的輪廓合併
unified = MergeEachCnts(contours, 7)
a = colorIm.copy()
cv2.drawContours(a, contours, -1, (255, 0, 0), 1)
self.dicImg.update({"找出輪廓(合併前)": a})
cv2.drawContours(colorIm, unified, -1, (255, 0, 0), 1)
self.dicImg.update({"找出輪廓(合併後)": colorIm.copy()})
# 依照X軸排序輪廓
unified = sorted([(c, cv2.boundingRect(c)[0], cv2.contourArea(c))
for c in unified],
key=lambda x: x[1])
# 再將太小的輪廓移除
unified = [c for c, v, a in unified if 15 < a < 200]
for index, c in enumerate(unified):
(x, y, w, h) = cv2.boundingRect(c)
# print('目前輪廓rect :'+str(x)+' '+ str(y)+' '+ str(w)+' '+ str(h))
try:
# 只將寬高大於 7 視為數字留存
if w > 7 and h > 7:
add = True
for i, img in enumerate(self.arr):
# 計算此輪廓與其他輪廓重心的距離 如果小於10 代表這兩輪廓是重疊的 例如6 0 9 會造成此誤判
dist = math.sqrt((x - img[0])**2 + (y - img[1])**2)
# print('距離:'+str(dist))
if dist <= 10:
add = False
break
if add:
self.arr.append((x, y, w, h))
except IndexError:
pass
Imgarr = [self.im[y:y + h, x:x + w] for x, y, w, h in self.arr]
self.dicImg.update({"圖片切割": Imgarr})
return Imgarr
def newCircles(imgOrg, org, circles,accept):
newCircles = []
cir = []
accept = set(accept)
imgRes = copy.deepcopy(imgOrg)
for i in range(len(circles)):
x0 = circles[i][1][0]
y0 = circles[i][1][1]
radius = circles[i][1][2]
name = (str(x0) + str(y0))
acc = False
if (name in accept):
acc = True
nimg = np.ones_like(imgOrg)*255
nimg = np.where(imgOrg == 0, 0, nimg)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
nimg = cv2.morphologyEx(nimg, cv2.MORPH_ERODE, kernel)
lin = 1
if(nimg[x0][y0] == 0):
lin +=10
while True:
if(lin > radius):
lin -= 1
else:
break
while True:
if(nimg[x0+lin][y0] == 255):
break
else:
lin -= 1
else:
per = radius*50//100
while True:
lin +=1
r = x0 + lin
if(nimg[r][y0] == 0):
break
if(r >= (x0+radius-per)):
lin = 0
break
while True and lin >0:
lin +=1
r = x0 + lin
if(nimg[r][y0] == 255):
break
if(r >= (x0+radius-per)):
lin = 0
break
point = (x0+lin,y0)
label = 100
nimg, counter = us.bfs4neig(nimg,point,label)
imgcir = np.ones_like(imgOrg)*255
img = np.ones_like(imgOrg)*0
img = np.where(nimg==label,255,img)
minX = np.min(np.where(nimg == label)[0])
maxX = np.max(np.where(nimg == label)[0])
minY = np.min(np.where(nimg == label)[1])
maxY = np.max(np.where(nimg == label)[1])
radX = (maxX - minX) // 2
radY = (maxY - minY) // 2
medX = radX + minX
medY = radY + minY
if (radX > radY):
rad = radX
else:
rad = radY
orgAr = radius**2 * 3.14
newAr = rad**2 * 3.14
imgRealSize = np.ones_like(imgOrg)*255
imgRealSize = drawCircle(imgRealSize,medX,medY,rad)
imgRealSize, counter = us.bfs4neig(imgRealSize,(medX,medY),label)
sizeEll = us.sizeEllipse(newAr)
sizeImg = imgOrg.shape[0]* imgOrg.shape[1]
loop = False
if (sizeImg < newAr):
loop = True
if (not loop):
imgcir = np.where(nimg == label, 0, imgcir)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(sizeEll,sizeEll))
imgcir = cv2.morphologyEx(imgcir, cv2.MORPH_OPEN, kernel)
imglabel = np.ones_like(imgOrg)*255
imglabel = np.where(imgcir==0,org,imglabel)
imglabel = np.where(imglabel >= 245, 255, imglabel)
imglabel = cv2.threshold(imglabel,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
text = ocr.labelToText(imglabel)
sizeEdge,acc = discoverEdge(imgOrg,acc,minX,maxX,minY,maxY,medX,medY)
rad = (sizeEdge//3) + rad
imgcir = cv2.threshold(imgcir,0,255,cv2.THRESH_BINARY_INV)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(sizeEdge,sizeEdge))
imgcir = cv2.morphologyEx(imgcir, cv2.MORPH_DILATE, kernel)
imgRes = imgRes + imgcir
imgcir = cv2.Canny(imgcir,100,200)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(sizeEdge//3,sizeEdge//3))
imgcir = cv2.morphologyEx(imgcir, cv2.MORPH_DILATE, kernel)
label = 255
minX = np.min(np.where(imgcir == label)[0])
maxX = np.max(np.where(imgcir == label)[0])
minY = np.min(np.where(imgcir == label)[1])
maxY = np.max(np.where(imgcir == label)[1])
point = (minX,minY)
cir = set(cir)
if(str(medX)+str(medY) not in cir):
newCircles.append((circles[0],(medX,medY,rad),loop,acc,text,point,imgcir[minX:maxX,minY:maxY]))
cir = list(cir)
cir.append(str(medX)+str(medY))
name = 'img'+str(x0)+str(y0)
else:
newCircles.append((circles[0],(x0,y0,radius),loop,acc,'',(0,0),[]))
return newCircles,imgRes
# # In the real world, you'd replace this section with code to grab a real
# # CAPTCHA image from a live website.
# captcha_image_files = list(paths.list_images(CAPTCHA_IMAGE_FOLDER))
# captcha_image_files = np.random.choice(captcha_image_files, size=(10,), replace=False)
#
# # loop over the image paths
# for image_file in captcha_image_files:
# Load the image and convert it to grayscale
image = cv2.imread("C:\\Users\\Administrator\\Desktop\\pic.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Add some extra padding around the image
image = cv2.copyMakeBorder(image, 20, 20, 20, 20, cv2.BORDER_REPLICATE)
# threshold the image (convert it to pure black and white)
thresh = cv2.threshold(image, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# find the contours (continuous blobs of pixels) the image
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# Hack for compatibility with different OpenCV versions
contours = contours[0] if imutils.is_cv2() else contours[1]
letter_image_regions = []
# Now we can loop through each of the four contours and extract the letter
# inside of each one
for contour in contours:
# Get the rectangle that contains the contour
(x, y, w, h) = cv2.boundingRect(contour)
cv2.rectangle(frame, (int(cap_region_x_begin * frame.shape[1]), 0),
(frame.shape[1], int(cap_region_y_end * frame.shape[0])), (255, 0, 0), 2)
cv2.imshow('original', frame)
# Main operation
if isBgCaptured == 1: # this part wont run until background captured
img = removeBG(frame)
img = img[0:int(cap_region_y_end * frame.shape[0]),
int(cap_region_x_begin * frame.shape[1]):frame.shape[1]] # clip the ROI
cv2.imshow('mask', img)
# convert the image into binary image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (blurValue, blurValue), 0)
cv2.imshow('blur', blur)
ret, thresh = cv2.threshold(blur, threshold, 255, cv2.THRESH_BINARY)
cv2.imshow('ori', thresh)
# get the coutours
thresh1 = copy.deepcopy(thresh)
_,contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
length = len(contours)
maxArea = -1
if length > 0:
for i in range(length): # find the biggest contour (according to area)
temp = contours[i]
area = cv2.contourArea(temp)
if area > maxArea:
maxArea = area
ci = i
cnt += 1
continue
next = frame.copy()
next_gray = cv2.cvtColor(next, cv2.COLOR_BGR2GRAY)
next_gray = cv2.GaussianBlur(next_gray, (5, 5), 0)
next_dif = cv2.absdiff(gray, next_gray)
prev_next_dif = cv2.absdiff(prev_gray, next_gray)
cv2.imshow('prev', prev)
cv2.imshow('curr', curr)
cv2.imshow('next', next)
# dif=cv2.addWeighted(next_dif,0.5,prev_dif,0.5,0)
_, next_thr = cv2.threshold(next_dif, 5, 255, cv2.THRESH_BINARY)
_, prev_thr = cv2.threshold(prev_dif, 5, 255, cv2.THRESH_BINARY)
_, prev_next_thr = cv2.threshold(prev_next_dif, 5, 255,
cv2.THRESH_BINARY)
# thr=cv2.bitwise_or(prev_thr,next_thr)
# _, thr = cv2.threshold(dif, 30, 255, cv2.THRESH_BINARY)
# print(np.max(np.mean([next_dif,prev_dif])))
prev_contours, hierachy = cv2.findContours(prev_thr, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
prev_action_mask = np.zeros_like(gray)
for cntr in prev_contours:
# if cv2.arcLength(cntr, True) >= 1000:
# if cv2.contourArea(cntr)<3000:
cv2.drawContours(image=visual,
import cv2
import numpy as np
def nothing(x):
pass
img = cv2.imread('../debug/circle_ring.jpg',1)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# # 1 官方解决方案
_,thresh = cv2.threshold(gray,100,255,cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
# 默认
image,contours,hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 清晰
# image,contours,hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
image_ = np.copy(img)
# 填充，第一步完成
# cv2.drawContours(image_,contours,2,(215, 215, 180),-1)
# cv2.drawContours(image_,contours,4,(215, 215, 180),-1)
# cv2.drawContours(image_,contours,6,(215, 215, 180),-1)
# 勾勒轮廓，为了接下来处理
cv2.drawContours(image_,contours,2,(0, 0, 255),2)
cv2.drawContours(image_,contours,4,(0, 0, 255),2)
cv2.drawContours(image_,contours,6,(0, 0, 255),2)
cv2.imshow("demo",image_)
cv2.waitKey(0)
import numpy as np
import cv2
img = cv2.imread('faces.jpeg',1)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h = hsv[:,:,0]
s = hsv[:,:,1]
v = hsv[:,:,2]
hsv_split = np.concatenate((h,s,v), axis=1)
cv2.imshow("Split HSV",hsv_split)
ret, min_sat = cv2.threshold(s,40,255, cv2.THRESH_BINARY)
cv2.imshow("Sat Filter",min_sat)
ret, max_hue = cv2.threshold(h,15, 255, cv2.THRESH_BINARY_INV)
cv2.imshow("Hue Filter",max_hue)
final = cv2.bitwise_and(min_sat,max_hue)
cv2.imshow("Final",final)
cv2.imshow("Original",img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def warp_process_image(img):
global nwindows
global margin
global minpix
global lane_bin_th
blur = cv2.GaussianBlur(img, (5, 5), 0)
_, L, _ = cv2.split(cv2.cvtColor(blur, cv2.COLOR_BGR2HLS))
_, lane = cv2.threshold(L, lane_bin_th, 255, cv2.THRESH_BINARY)
histogram = np.sum(lane[lane.shape[0] // 2:, :], axis=0)
midpoint = np.int(histogram.shape[0] / 2)
leftx_current = np.argmax(histogram[:midpoint])
rightx_current = np.argmax(histogram[midpoint:]) + midpoint
window_height = np.int(lane.shape[0] / nwindows)
nz = lane.nonzero()
left_lane_inds = []
right_lane_inds = []
lx, ly, rx, ry = [], [], [], []
out_img = np.dstack((lane, lane, lane)) * 255
for window in range(nwindows):
win_yl = lane.shape[0] - (window + 1) * window_height
win_yh = lane.shape[0] - window * window_height
win_xll = leftx_current - margin
win_xlh = leftx_current + margin
win_xrl = rightx_current - margin
win_xrh = rightx_current + margin
cv2.rectangle(out_img, (win_xll, win_yl), (win_xlh, win_yh),
(0, 255, 0), 2)
cv2.rectangle(out_img, (win_xrl, win_yl), (win_xrh, win_yh),
(0, 255, 0), 2)
good_left_inds = ((nz[0] >= win_yl) & (nz[0] < win_yh) &
(nz[1] >= win_xll) & (nz[1] < win_xlh)).nonzero()[0]
good_right_inds = ((nz[0] >= win_yl) & (nz[0] < win_yh) &
(nz[1] >= win_xrl) & (nz[1] < win_xrh)).nonzero()[0]
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nz[1][good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nz[1][good_right_inds]))
lx.append(leftx_current)
ly.append((win_yl + win_yh) / 2)
rx.append(rightx_current)
ry.append((win_yl + win_yh) / 2)
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
#left_fit = np.polyfit(nz[0][left_lane_inds], nz[1][left_lane_inds], 2)
#right_fit = np.polyfit(nz[0][right_lane_inds] , nz[1][right_lane_inds], 2)
lfit = np.polyfit(np.array(ly), np.array(lx), 2)
rfit = np.polyfit(np.array(ry), np.array(rx), 2)
out_img[nz[0][left_lane_inds], nz[1][left_lane_inds]] = [255, 0, 0]
out_img[nz[0][right_lane_inds], nz[1][right_lane_inds]] = [0, 0, 255]
cv2.imshow("viewer", out_img)
#return left_fit, right_fit
return lfit, rfit
for ind in indices:
for subset in itertools.combinations(ind, 2):
a_list.append(tuple(subset))
a = list(set(a_list))
Graph.add_edges_from(a)
return Graph
# Read image
image = cv2.imread("/home/mark/Documents/CorGraphProjectGit/CorticalVision/TCS10.PNG",0)
# Create binary image
#gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(image, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
# Find contours
(_, contours, _) = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print("Found %d components." % len(contours))
centroids = []
G = nx.Graph()
tempInt = 0
for c in contours:
M = cv2.moments(c)
temp = []
temp.append(int(M["m10"] / M["m00"]))
temp.append(int(M["m01"] / M["m00"]))
print(M["m00"])
def execute(self):
img=self.img
#gray is used for classification and search, gray_c only for search
gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4,8))
gray_c = clahe.apply(gray)
#guess 'white' color
epsilon=0.0001
white=np.median(gray)
white=np.mean(gray[gray>white-epsilon])
white_c=np.median(gray_c)
white_c=np.mean(gray_c[gray_c>white_c-epsilon])
window=21
#adaptive mean
th2 = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, window, 2)
#adaptive gaussian
th3 = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, window, 2)
#Otsu's
_,th4 = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#adaptive mean
th_c2 = cv2.adaptiveThreshold(gray_c, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, window, 2)
#adaptive gaussian
th_c3 = cv2.adaptiveThreshold(gray_c, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, window, 2)
#Otsu's
_,th_c4 = cv2.threshold(gray_c,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#extend borders(probably only needed for detectMultiscale)
img=cv2.copyMakeBorder(img, 4, 4, 2, 2, cv2.BORDER_CONSTANT, value=(int(white), int(white), int(white)))
gray=cv2.copyMakeBorder(gray, 4, 4, 2, 2, cv2.BORDER_CONSTANT, value=int(white))
gray_c=cv2.copyMakeBorder(gray_c, 4, 4, 2, 2, cv2.BORDER_CONSTANT, value=int(white_c))
ths=[th2, th3, th4, th_c2, th_c3, th_c4]
for i in xrange(len(ths)):
ths[i]=cv2.copyMakeBorder(ths[i], 4, 4, 2, 2, cv2.BORDER_CONSTANT, value=255)
th2, th3, th4, th_c2, th_c3, th_c4 = ths
self.debug(img, "svm_img")
self.debug(gray, "svm_gr")
self.debug(gray_c, "svm_gr_c")
self.debug(th2, "svm_th2")
self.debug(th3, "svm_th3")
self.debug(th4, "svm_th4")
self.debug(th_c2, "svm_th_c2")
self.debug(th_c3, "svm_th_c3")
self.debug(th_c4, "svm_th_c4")
plate=[]
min_height=10
min_width=5
min_area=70
epsilon=0.00001
@memoize
def max_score_hsplit(box, n=3):
x,y,w,h=box
l,s=max_score(box)
if s<0.0:
l_s=[(s, [(l,s,box)])]
else:
l_s=[(epsilon, [(l,epsilon,box)])]
if n>1:
for w0 in xrange(1, w):
if w0*h<min_area or w0<min_width or h<min_height:
l0, s0=(None, epsilon)
else:
l0, s0=max_score((x,y,w0,h))
if (w-w0)*h<min_area or (w-w0)<min_width or h<min_height:
s1, ls1=(epsilon, [(None, epsilon, (x+w0,y,w-w0,h))])
else:
s1, ls1=max_score_hsplit((x+w0,y,w-w0,h),n-1)
if s0>epsilon:
s0=epsilon
score=(s0*w0+s1*(w-w0)+0.0)/w
l_s+=[(score, [(l0, s0, (x,y,w0,h))]+ls1)]
return min(l_s)
#functions defined as closures to avoid passing multiple and/or complex arguments
#which allows memoize_simple use and autoresets after execute comletion
def compute_hog(box):
X,Y,W,H=box
gray_=gray[Y-1:Y+H+1, X-1:X+W+1] #FIXME should check area bounds
winSize = (20, 30)
blockSize = (4,6)
blockStride = (2,3)
cellSize = (2,3)
nbins=9
winStride = (20,30)
padding = (0,0)
gray_=cv2.resize(gray_, winSize, interpolation = cv2.INTER_CUBIC)
hog=cv2.HOGDescriptor(winSize, blockSize,blockStride,cellSize, nbins)
desc = hog.compute(gray_, winStride, padding, ((0, 0),))
return desc
letters=['1','2','3','4','5','6','7','8','9','0O','A','B','C','E','H','K','M','P','T','X','Y']
@memoize_simple
def max_score(box):
x,y,w,h=box
if w*h<min_area or w<min_width or h<min_height:
return (None, 1.0)
desc=compute_hog(box)
l_s=[(l, -self.svm_letters[l].predict(desc, returnDFVal=True)) for l in letters]
return min(l_s, key=lambda x: x[1])
letter_ligatures=['8dot', 'dotO', 'dotM', 'dotB', 'dotC', 'dotH', 'dotE', 'dotP']
@memoize_simple
def max_score_ligatures(box):
x,y,w,h=box
if w*h<min_area or w<min_width or h<min_height:
return (None, 1.0)
desc=compute_hog(box)
l_s=[(l, -self.svm_letters[l].predict(desc, returnDFVal=True)) for l in letter_ligatures]
return min(l_s, key=lambda x: x[1])
h1_candidates=[10,5]
h2_candidates=[16,22]
@memoize_simple
def max_score_vsplit(box):
x,y,w,h=box
l_s=[]
min_score=1.0
min_letter=None
min_box=(x,y,w,h)
for h1 in h1_candidates:
for h2 in h2_candidates:
l,s=max_score((x,h1,w,h2))
s=s*h2/(h+0.0)
if s<min_score:
min_score=s
min_letter=l
min_box=(x,h1,w,h2)
return min_letter, min_score, min_box
def max_score_hsplit3(box):
x,y,w,h=box
min_score=1.0
min_letter=None
min_box=(x,y,w,h)
for w1 in xrange(0,min(w-min_width,10)):
for w2 in xrange(min_width, min(w-w1,16)):
b_=(x+w1,y,w2,h)
l,s,b=max_score_vsplit(b_)
s=s/(w+0)*w2
if s<min_score:
min_score=s
min_letter=l
min_box=b
return min_score, min_letter, min_box
#replacing original compute_hog with memoized version
#will be restored after ligatures detection
compute_hog_raw=compute_hog
compute_hog=memoize_simple(compute_hog)
boxes=[]
for th in ths:
boxes+=self.get_boxes_from_contour(th, gray)
boxes=list(set(boxes)) #get uniq boxes
#annotate each box with name for debug, letter, score, cropped image
boxes=[box+(str(box), None, 1.0, None) for box in boxes]
#search all boxes for letters
boxes_left=[]
while boxes:
X,Y,W,H,m,min_letter,min_score,b_img = boxes.pop()
b_img=gray[Y-1:Y+H+1, X-1:X+W+1]
self.debug(b_img, "svm1_t_"+str(m))
min_letter, min_score=max_score((X,Y,W,H))
if min_score<0:
self.debug(b_img, "svm1_f_"+min_letter+"_"+str(m))
plate+=[(min_letter, (X,Y,W,H), -min_score)]
else:
boxes_left+=[(X,Y,W,H,m,min_letter,min_score, b_img)]
#prune plate, distructive to origianl
plate=prune_plate(plate, threshold=0.799)
#are we done?
#RUSSIAN PLATE TYPE1 SPECIFIC
alphas, nums, alphanums=get_stats_symbols(plate)
if alphanums>=9:
return TaskResultSVMLetterDetector(plate)
#prune boxes by content
hranges=get_free_hranges(gray, plate, 2)
hranges=range_diff_many([(0,gray.shape[1])], [(r[0], r[1]-r[0]+1) for r in hranges])
hranges=[r for r in hranges if r[1]>0]
boxes=boxes_left
boxes_left=[]
while boxes:
X,Y,W,H,m,min_letter,min_score,b_img = boxes.pop()
fr=range_diff_many([(X,W)], hranges)
for r in fr:
X, W=r
if W<min_width:
continue
b_img=gray[Y-1:Y+H+1, X-1:X+W+1]
min_letter, min_score=max_score((X,Y,W,H))
m_r=str(m)+"_"+str(r)
boxes_left+=[(X,Y,W,H,m_r,min_letter,min_score, b_img)]
self.debug(b_img, "svm1_t2_"+str(m_r))
#search known 'ligatures'
boxes=boxes_left
boxes_left=[]
while boxes:
X,Y,W,H,m,min_letter,min_score,b_img = boxes.pop()
min_letter_new, min_score_new=max_score_ligatures((X,Y,W,H))
if min_score_new<0:
min_letter=min_letter_new.replace('dot','').replace('O','0O')
min_score=min_score_new
self.debug(b_img, "svm1_fl_"+min_letter+"_"+str(m))
plate+=[(min_letter, (X,Y,W,H), -min_score)]
else:
boxes_left+=[(X,Y,W,H,m,min_letter,min_score,b_img)]
#prune plate, distructive to origianl
plate=prune_plate(plate, threshold=0.799)
#replace score if ligaturazed version if better
#FIXME maybe just recrop?
for i in xrange(len(plate)):
letter, box, score = plate[i]
X,Y,W,H = box
if letter in ['8', 'O0', 'M', 'B', 'C', 'H', 'E', 'P']:
if letter=='8':
ligature='8dot'
elif letter=='0O':
ligature='dotO'
else:
ligature='dot'+letter
desc=compute_hog(box)
score=max(score, self.svm_letters[ligature].predict(desc, returnDFVal=True))
plate[i]=(letter, (X,Y,W,H), score)
#are we done?
#RUSSIAN PLATE TYPE1 SPECIFIC
alphas, nums, alphanums=get_stats_symbols(plate)
if alphanums>=9:
return TaskResultSVMLetterDetector(plate)
#search by splitting
boxes=boxes_left
boxes_left=[]
while boxes:
X,Y,W,H,m,min_letter,min_score,b_img = boxes.pop()
s, splt=max_score_hsplit((X,Y,W,H), n=3)
for k in xrange(len(splt)):
letter_s, score_s, box_s=splt[k]
if score_s<0:
b_img_s=gray[box_s[1]-1:box_s[1]+box_s[3]+1, box_s[0]-1:box_s[0]+box_s[2]+1]
self.debug(b_img_s, "svm1_fspl_"+letter_s+"_"+str(m)+"_"+str(k))
plate+=[(letter_s, box_s, -score_s)]
if s>0:
self.debug(b_img, "svm1_nf_"+str(m))
#restore original compute_hog
compute_hog_raw=compute_hog_raw
#prune plate, distructive to original
plate=prune_plate(plate, threshold=0.799) #distructive
#plate=sorted(plate, key=lambda x: x[1][0]+x[1][2]/2.0)
#'bruteforce' search
hranges=[(r[0], r[1]-r[0]+1) for r in get_free_hranges(gray, plate, 2)]
h1_cnds=list(set([l[1][1] for l in plate]))
h2_cnds=list(set([l[1][3] for l in plate]))
if len(h1_cnds)>2:
h1_candidates=h1_cnds
if len(h2_cnds)>2:
h2_candidates=h2_cnds
ws=[l[1][2] for l in plate]
if ws:
min_width=max(min_width, int(np.floor(min(ws)*0.75)))
max_width=20
for r in hranges:
x,w=r
if w<min_width:
continue
if x==0:
x+=1
if x+w==gray.shape[1]:
w-=1
scores=[max_score_hsplit3((x+i, 0, min(w-i,max_width), gray.shape[0])) for i in xrange(0,w-min_width,3)]
for s in [s for s in scores if s[0]<0.0]:
min_letter, min_score=max_score(s[2])
b_img=gray[s[2][1]-1:s[2][1]+s[2][3]+1, s[2][0]-1:s[2][0]+s[2][2]+1]
self.debug(b_img, "svm1_fbf_"+min_letter+"_"+str(s[2]))
plate+=[(min_letter, s[2], -min_score)]
plate=prune_plate(plate, threshold=0.799) #distructive
return TaskResultSVMLetterDetector(plate)
import cv2
import pytesseract
l = 0
a = []
pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract'
img = cv2.imread("C:\\Users\\admin\\Desktop\\1.jpg")
g = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
g = cv2.bilateralFilter(g, 250, 90, 190)
ret, thresh = cv2.threshold(g, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
t = pytesseract.image_to_string(thresh, lang="eng")
cv2.imshow("1", thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
print("THE NUMBER IS:", t)
for i in range(0, len(t)):
if t[i] == 'T':
l = i
break
for j in range(l, len(t)):
if t[j].isupper():
a.append(t[j])
elif t[j].isnumeric():
a.append(t[j])
print(*a)
import cv2
import numpy as np
import time
img = cv2.imread('lena.jpg')
# -----------------------------------------------------
cv2.setUseOptimized(True) # open AVX
cv2.useOptimized()
# -----------------------------------------------------
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,128,255,cv2.THRESH_BINARY)
tStart = time.time()
# -----------------------------------------------------
for i in range (1,100,1) :
kernel = np.ones((5,5),np.uint8)
frame_mor = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
frame_mor = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=1)
# -----------------------------------------------------
tEnd = time.time()
t_total = tEnd - tStart
print("With AVX : %f s" % t_total)
# -----------------------------------------------------
cv2.namedWindow("With_AVX",0)
cv2.imshow('With_AVX', frame_mor)
# ---------------------------------------------------------------------------------------------------------------------
img = cv2.imread('lena.jpg')
# -----------------------------------------------------
cv2.setUseOptimized(False) # close AVX
cv2.useOptimized()
# -----------------------------------------------------
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
camera = cv2.VideoCapture(0)
background = None
es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 4))
while True:
grabbed, frame = camera.read()
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray_frame = cv2.GaussianBlur(gray_frame, (25, 25), 3)
if background is None:
background = gray_frame
continue
diff = cv2.absdiff(background, gray_frame)
diff = cv2.threshold(diff, 50, 255, cv2.THRESH_BINARY)[1]
diff = cv2.dilate(diff, es, iterations=3)
cv2.putText(frame, "Motion: Undetected", (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.putText(frame,
datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 255, 0), 1)
cv2.imshow('video', frame)
cv2.imshow('diff', diff)
key = cv2.waitKey(1) & 0xFFf
if key == ord('q'):
break
def padding_position(x, y, w, h, p):
return x - p, y - p, w + p * 2, h + p * 2
###以降メイン処理###
img = cv2.imread("concat.jpg", 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imwrite("img_gray.jpg", gray)
gray2 = cv2.bitwise_not(gray)
cv2.imwrite("img_gray2.jpg", gray2)
# 閾値の設定
threshold = 80
# 二値化(閾値100を超えた画素を255にする。)
ret, gray3 = cv2.threshold(gray2, threshold, 255, cv2.THRESH_BINARY)
gray3 = cv2.bitwise_not(gray3)
cv2.imwrite("img_gray3.jpg", gray3)
img_out = cv2.imread("white.jpg", 3)
min_size = 30
# 輪郭検出
image, contours = cv2.findContours(gray3, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
img2 = detect_contour(gray3, img_out, 500)
cv2.imwrite("img_contour.jpg", img2)
# coding=utf-8
# from __future__ import division
import cv2
import numpy as np
#这是遮罩效果 可以做mask 好屌啊 但是可惜有bug！
img1 = cv2.imread('abc.jpg')
img2 = cv2.imread('bbc.png')
rows, cols, channels = img2.shape
roi = img1[0:rows, 0:cols]
img2gray = cv2.cvtColor(img2, cv2.COLOR_BAYER_BG2GRAY)
ret, mask = cv2.threshold(img2gray, 175, 255, cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)
img1_bg = cv2.bitwise_and(roi, roi, mask=mask)
img2_fg = cv2.bitwise_and(img2, img2, mask=mask_inv)
dst = cv2.add(img1_bg, img2_fg)
img1[0:rows, 0:cols] = dst
cv2.imshow('img', img1)
def compare_screen_without_areas(
self, path1, *args, save_folder=save_folder_path, ssim=starts_ssim, image_format=starts_format_image
):
"""
Compares two pictures, which have parts to be ignored
x1 and y1 = x and y coordinates for the upper left corner of the ignored area square
x2 and y2 = x and y coordinates for the lower right corner of the square of the ignored part
Attention! It is always necessary to enter in order x1 y1 x2 y2 x1 y1 x2 y2 etc ...
Compare screen without areas ../Image1.png 0 0 30 40 50 50 100 100
Creates 2 ignored parts at 0,0, 30,40 and 50, 50, 100, 100
"""
self._check_dir(save_folder)
self._check_ssim(ssim)
self._check_image_format(image_format)
save_folder = self.save_folder
self.seleniumlib.capture_page_screenshot(save_folder + "/test1.png")
path2 = save_folder + "/test1.png"
if os.path.exists(path1) and os.path.exists(path2):
lt = len(args)
img1 = cv.imread(path1, 1)
img2 = cv.imread(path2, 1)
if lt % 4 == 0:
x = lt / 4
self.robotlib.log_to_console(x)
i = 0
a = 0
while i < x:
color = (0, 0, 0)
x1 = int(args[0 + a])
y1 = int(args[1 + a])
x2 = int(args[2 + a])
y2 = int(args[3 + a])
cv.rectangle(img1, (x1, y1), (x2, y2), color, -1)
cv.rectangle(img2, (x1, y1), (x2, y2), color, -1)
a += 4
i += 1
cv.namedWindow("image", cv.WINDOW_NORMAL)
# convert to grey
gray_img1 = cv.cvtColor(img1, cv.COLOR_BGR2GRAY)
gray_img2 = cv.cvtColor(img2, cv.COLOR_BGR2GRAY)
# SSIM diff Img
(self.score, diff) = structural_similarity(
gray_img1, gray_img2, full=True
)
diff = (diff * 255).astype("uint8")
# Threshold diff Img
thresh = cv.threshold(
diff, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU
)[1]
cnts = cv.findContours(
thresh.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE
)
cnts = imutils.grab_contours(cnts)
# Create frame in diff area
for c in cnts:
(x, y, w, h) = cv.boundingRect(c)
cv.rectangle(img1, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv.rectangle(img2, (x, y), (x + w, y + h), (0, 0, 255), 2)
# Show image
if float(self.score) < self.ssim:
img_diff = cv.hconcat([img1, img2])
time_ = str(time.time())
self.seleniumlib.capture_page_screenshot(
save_folder + "/Img" + time_ + self.format
)
cv.imwrite(save_folder + "/Img" + time_ + self.format, img_diff)
self.robotlib.fail("Image has diff: {} ".format(self.score))
else:
img_diff = cv.hconcat([img1, img2])
time_ = str(time.time())
self.seleniumlib.capture_page_screenshot(
save_folder + "/Img" + time_ + self.format
)
cv.imwrite(save_folder + "/Img" + time_ + self.format, img_diff)
self.robotlib.log_to_console(
"Image has diff: {} ".format(self.score)
)
else:
raise AssertionError("The path to the image does not exist")
Third argument is the
ADAPTIVE_THRESH_MEAN_C − threshold value is the mean of neighborhood area. (OR)
ADAPTIVE_THRESH_GAUSSIAN_C − threshold value is the weighted sum of neighborhood values where weights are a Gaussian window.
Fourth Argument is the threshold type -> variable of integer type representing the type of threshold to be used.
Fifth Argument is the blockSize − A variable of the integer type representing size of the pixelneighborhood used to calculate the threshold value.
Sixth Argument is C (Constant) − A variable of double type representing the constant used in the both methods (subtracted from the mean or weighted mean).
"""
import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('b.jpg', 0)
ret,thresh1 = cv2.threshold(img, 130, 255, cv2.THRESH_BINARY)
ret,thresh2 = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY_INV)
ret,thresh3 = cv2.threshold(img, 140, 255, cv2.THRESH_TRUNC)
ret,thresh4 = cv2.threshold(img, 120, 255, cv2.THRESH_TOZERO)
ret,thresh5 = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO_INV)
th2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2) # ADAPTIVE THRESHOLDING
th3 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
ret2, th4 = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) # OTSU'S BINARIZATION
blur = cv2.GaussianBlur(img, (5, 5), 0) # Removal of noise with gaussian for OTSU'S Binarization.
ret3, th5 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV', 'Adaptive Mean', 'Adaptive Gaussian', 'Otsu', 'Gaussian Otsu']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5, th2, th3, th4, th5]
for i in range(10):
def warp_example(dm):
dm.example_image_warped = np.zeros_like(dm.subject_image)
def find_index_of_point(point, point_array):
for i, current_point in enumerate(point_array):
if np.all(current_point == point):
return i
return -1
# Search index
for subject_triangle in dm.subject_triangles:
subject_point1 = subject_triangle[:2]
subject_point2 = subject_triangle[2:4]
subject_point3 = subject_triangle[4:6]
index1 = find_index_of_point(subject_point1, dm.subject_face_landmarks)
index2 = find_index_of_point(subject_point2, dm.subject_face_landmarks)
index3 = find_index_of_point(subject_point3, dm.subject_face_landmarks)
example_point1 = dm.example_face_landmarks[index1]
example_point2 = dm.example_face_landmarks[index2]
example_point3 = dm.example_face_landmarks[index3]
subject_cropped_triangle_coord = np.array(
[subject_point1, subject_point2, subject_point3], np.int32)
example_cropped_triangle_coord = np.array(
[example_point1, example_point2, example_point3], np.int32)
# Triangulate example
example_crop_bounding_rect = cv.boundingRect(
example_cropped_triangle_coord)
x, y, w, h = example_crop_bounding_rect
cropped_example_triangle = dm.example_image[y:y + h, x:x + w]
# cropped_example_triangle_mask = np.zeros((h, w), np.uint8)
cropped_example_triangle_coord_relative = example_cropped_triangle_coord - np.tile(
np.array([x, y]), (3, 1))
# cv.fillConvexPoly(cropped_example_triangle_mask, cropped_example_triangle_coord_relative, 255)
# Triangulate subject
subject_crop_bounding_rect = cv.boundingRect(
subject_cropped_triangle_coord)
x, y, w, h = subject_crop_bounding_rect
# cropped_subject_triangle = dm.subject_image[y: y+h, x:x+w]
cropped_subject_triangle_mask = np.zeros((h, w), np.uint8)
cropped_subject_triangle_coord_relative = subject_cropped_triangle_coord - np.tile(
np.array([x, y]), (3, 1))
cv.fillConvexPoly(cropped_subject_triangle_mask,
cropped_subject_triangle_coord_relative, 255)
# Transform
M = cv.getAffineTransform(
cropped_example_triangle_coord_relative.astype(np.float32),
cropped_subject_triangle_coord_relative.astype(np.float32))
warped_example_triangle = cv.warpAffine(cropped_example_triangle, M,
(w, h))
warped_example_triangle = cv.bitwise_and(
warped_example_triangle,
warped_example_triangle,
mask=cropped_subject_triangle_mask)
# Reconstruct
# Fix to remove white lines present when adding triangles to places
result_face_area = dm.example_image_warped[y:y + h, x:x + w]
result_face_area_gray = cv.cvtColor(result_face_area,
cv.COLOR_BGR2GRAY)
_, triangle_fix_mask = cv.threshold(result_face_area_gray, 1, 255,
cv.THRESH_BINARY_INV)
warped_example_triangle = cv.bitwise_and(warped_example_triangle,
warped_example_triangle,
mask=triangle_fix_mask)
result_face_area = cv.add(result_face_area, warped_example_triangle)
dm.example_image_warped[y:y + h, x:x + w] = result_face_area
# cropped_subject_triangle_mask_3_channel = np.zeros((cropped_subject_triangle_mask.shape[0], cropped_subject_triangle_mask.shape[1], 3), np.uint8)
# cropped_subject_triangle_mask_3_channel[:,:,0] = np.array([cropped_subject_triangle_mask])
# cropped_subject_triangle_mask_3_channel[:,:,1] = np.array([cropped_subject_triangle_mask])
# cropped_subject_triangle_mask_3_channel[:,:,2] = np.array([cropped_subject_triangle_mask])
# a = cv.bitwise_and(cropped_subject_triangle_mask_3_channel, warped_example_triangle)
# dm.example_image_warped[y: y+h, x: x+w] = cv.bitwise_or(dm.example_image_warped[y: y+h, x: x+w], a)
# e = dm.example_image_warped
# cv.imshow("e", e)
# cv.waitKey(100)
if dm.show_intermediary:
plt.subplot(1, 2, 1)
plt.imshow(opencv2matplotlib(dm.subject_image))
plt.subplot(1, 2, 2)
plt.imshow(opencv2matplotlib(dm.example_image_warped))
plt.show()
cv2.imshow("Card", image)
key = cv2.waitKey(1) & 0xFF
if key == ord("p"):
break
raw_capture.truncate(0)
gray = \
cv2.cvtColor(
image,
cv2.COLOR_BGR2GRAY
)
blur = cv2.GaussianBlur(gray,(5,5),0)
_, threshold = \
cv2.threshold(
blur,
100,
255,
cv2.THRESH_BINARY
)
contours, hierarchy = \
cv2.findContours(
threshold,
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE
)
contours = \
sorted(
contours,
key=cv2.contourArea,
reverse=True
cap = cv2.VideoCapture(0)
scaling_factor = 0.5
while True:
ret, frame = cap.read()
frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
mouth_rects = mouth_cascade.detectMultiScale(gray, 1.3, 5)
if len(mouth_rects) > 0:
(x,y,w,h) = mouth_rects[0]
h, w = int(0.6*h), int(1.2*w)
x -= int(0.05*w)
y -= int(0.55*h)
frame_roi = frame[y:y+h, x:x+w]
moustache_mask_small = cv2.resize(moustache_mask, (w, h), interpolation=cv2.INTER_AREA)
gray_mask = cv2.cvtColor(moustache_mask_small, cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(gray_mask, 50, 255, cv2.THRESH_BINARY_INV)
mask_inv = cv2.bitwise_not(mask)
masked_mouth = cv2.bitwise_and(moustache_mask_small, moustache_mask_small, mask=mask)
masked_frame = cv2.bitwise_and(frame_roi, frame_roi, mask=mask_inv)
frame[y:y+h, x:x+w] = cv2.add(masked_mouth, masked_frame)
cv2.imshow('Moustache', frame)
c = cv2.waitKey(1)
if c == 27:
break
cap.release()
cv2.destroyAllWindows()
def compare_screen_areas(
self, x1, y1, x2, y2, path1, save_folder=save_folder_path, ssim=starts_ssim,
image_format=starts_format_image
):
"""Creates a cut-out from the screen
Creates a cut-out from the screen that is on the screen and compares it to a previously created
x1 and y1 = x and y coordinates for the upper left corner of the square
x2 and y2 = x and y coordinates for the bottom right corner of the square
path1 = Path to an already created viewport with which we want to compare the viewport created by us
Example: Compare screen area 0 0 25 25 ../Crop_Image1.png Creates Crop_Image1.png from 0, 0, 25, 25
"""
self._check_dir(save_folder)
self._check_ssim(ssim)
self._check_image_format(image_format)
save_folder = self.save_folder
self.seleniumlib.capture_page_screenshot(save_folder + '/test1.png')
path2 = save_folder + '/test1.png'
if os.path.exists(path1):
if os.path.exists(path2):
# load img
img1 = cv.imread(path1, 1) # img from docu
img2 = cv.imread(path2, 1) # img from screenshot
# convert to grey
gray_img1 = cv.cvtColor(img1, cv.COLOR_BGR2GRAY)
gray_img2 = cv.cvtColor(img2, cv.COLOR_BGR2GRAY)
# spliting area
crop_img = gray_img2[
int(x1): int(y2), int(y1): int(x2)
] # Crop from {x, y, w, h } => {0, 0, 300, 400}
# SSIM diff img
(self.score, diff) = structural_similarity(
gray_img1, crop_img, full=True
)
diff = (diff * 255).astype('uint8')
# Threshold diff img
thresh = cv.threshold(
diff, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU
)[1]
cnts = cv.findContours(
thresh.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE
)
cnts = imutils.grab_contours(cnts)
crop_img_color = img2[int(x1): int(y2), int(y1): int(x2)]
# Create frame in diff area
for c in cnts:
(x, y, w, h) = cv.boundingRect(c)
cv.rectangle(img1, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv.rectangle(crop_img_color, (x, y), (x + w, y + h), (0, 0, 255), 2)
# Show image
if float(self.score) < self.ssim:
self.robotlib = BuiltIn().get_library_instance('BuiltIn')
img_diff = cv.hconcat([img1, crop_img_color])
time_ = str(time.time())
self.seleniumlib.capture_page_screenshot(
save_folder + '/img' + time_ + '.png'
)
cv.imwrite(save_folder + '/img' + time_ + self.format, img_diff)
self.robotlib.fail('Image has diff: {} '.format(self.score))
score_percen = float(self.score) * +100
self.robotlib.fail('Image has diff: {} %'.format(score_percen))
else:
img_diff = cv.hconcat([self.img1, self.img2])
time_ = str(time.time())
self.seleniumlib.capture_page_screenshot(
save_folder + "/Img" + time_ + self.format
)
cv.imwrite(save_folder + "/Img" + time_ + self.format, img_diff)
self.robotlib.log_to_console(
"Image has diff: {} ".format(self.score)
)
else:
raise AssertionError("New screen doesnt exist anymore")
else:
raise AssertionError("The path1 to the image does not exist. Try a other path, than:" + path1)
if os.path.exists(save_folder + '/test1.png'):
os.remove(save_folder + '/test1.png')
def predict(self,
image,
background,
beard_file=None,
glasses_file=None,
hat_file=None,
visualization=False,
threshold=0.5):
# instance segmention
solov2_output = self.solov2.predict(image=image,
threshold=threshold,
visualization=visualization)
# Set background pixel to 0
im_segm, x0, x1, y0, y1, _, _, _, _, flag_seg = P.visualize_box_mask(
image, solov2_output, threshold=threshold)
if flag_seg == 0:
return im_segm
h, w = y1 - y0, x1 - x0
back_json = background[:-3] + 'json'
stand_box = json.load(open(back_json))
stand_box = stand_box['outputs']['object'][0]['bndbox']
stand_xmin, stand_xmax, stand_ymin, stand_ymax = stand_box[
'xmin'], stand_box['xmax'], stand_box['ymin'], stand_box['ymax']
im_path = np.asarray(im_segm)
# face detection
blaceface_output = self.blaceface.predict(image=im_path,
threshold=threshold,
visualization=visualization)
im_face_kp, p_left, p_right, p_up, p_bottom, h_xmin, h_ymin, h_xmax, h_ymax, flag_face = P.visualize_box_mask(
im_path,
blaceface_output,
threshold=threshold,
beard_file=beard_file,
glasses_file=glasses_file,
hat_file=hat_file)
if flag_face == 1:
if x0 > h_xmin:
shift_x_ = x0 - h_xmin
else:
shift_x_ = 0
if y0 > h_ymin:
shift_y_ = y0 - h_ymin
else:
shift_y_ = 0
h += p_up + p_bottom + shift_y_
w += p_left + p_right + shift_x_
x0 = min(x0, h_xmin)
y0 = min(y0, h_ymin)
x1 = max(x1, h_xmax) + shift_x_ + p_left + p_right
y1 = max(y1, h_ymax) + shift_y_ + p_up + p_bottom
# Fill the background image
cropped = im_face_kp.crop((x0, y0, x1, y1))
resize_scale = min((stand_xmax - stand_xmin) / (x1 - x0),
(stand_ymax - stand_ymin) / (y1 - y0))
h, w = int(h * resize_scale), int(w * resize_scale)
cropped = cropped.resize((w, h), cv2.INTER_LINEAR)
cropped = cv2.cvtColor(np.asarray(cropped), cv2.COLOR_RGB2BGR)
shift_x = int((stand_xmax - stand_xmin - cropped.shape[1]) / 2)
shift_y = int((stand_ymax - stand_ymin - cropped.shape[0]) / 2)
out_image = cv2.imread(background)
e2gray = cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(e2gray, 1, 255, cv2.THRESH_BINARY_INV)
mask_inv = cv2.bitwise_not(mask)
roi = out_image[stand_ymin + shift_y:stand_ymin + cropped.shape[0] +
shift_y, stand_xmin + shift_x:stand_xmin +
cropped.shape[1] + shift_x]
person_bg = cv2.bitwise_and(roi, roi, mask=mask)
element_fg = cv2.bitwise_and(cropped, cropped, mask=mask_inv)
dst = cv2.add(person_bg, element_fg)
out_image[stand_ymin + shift_y:stand_ymin + cropped.shape[0] + shift_y,
stand_xmin + shift_x:stand_xmin + cropped.shape[1] +
shift_x] = dst
return out_image
training_label_loc = data_location + 'Luna/train/label/'
testing_images_loc = data_location + 'Luna/test/image/'
testing_label_loc = data_location + 'Luna/test/label/'
train_files = os.listdir(training_images_loc)
train_data = []
train_label = []
for i in train_files:
train_data.append(
cv2.resize((mc.imread(training_images_loc + i)), (512, 512)))
temp = cv2.resize(
mc.imread(training_label_loc + i.split('.')[0] + '_mask.tif',
mode="L"), (512, 512))
_, temp = cv2.threshold(temp, 127, 255, cv2.THRESH_BINARY)
train_label.append(temp)
train_data = np.array(train_data)
train_label = np.array(train_label)
test_files = os.listdir(testing_images_loc)
test_data = []
test_label = []
for i in test_files:
test_data.append(
cv2.resize((mc.imread(testing_images_loc + i)), (512, 512)))
# Change '_manual1.tiff' to the label name
temp = cv2.resize(
mc.imread(testing_label_loc + i.split('.')[0] + '_mask.tif'),
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 23 16:33:15 2020
@author: Javier
"""
import time
import cv2
import matplotlib.pyplot as plt
im1 = cv2.imread("F:\\TFM_datasets\\extracted_frames\\001084\\32.jpg")
im2 = cv2.imread("F:\\TFM_datasets\\extracted_frames\\001084\\38.jpg")
def read_gray(image):
return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
im1_gray = read_gray(im1)
im2_gray = read_gray(im2)
#plt.imshow(im2_gray,cmap='gray')
start_time = time.time()
diff_image = cv2.absdiff(im1_gray, im2_gray)
ret, thresh = cv2.threshold(diff_image, 30, 255, cv2.THRESH_BINARY)
#plt.imshow(thresh,cmap='gray')
print("--- %s seconds ---" % (time.time() - start_time))
while True:
ret, frame = cap.read()
grayscale = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.line(grayscale, (50, 50), (150, 150), (0, 0, 0), 15)
cv2.rectangle(grayscale, (50, 50), (200, 200), (0, 0, 0), 10)
cv2.circle(grayscale, (100, 50), 55, (0, 0, 0), -1)
cv2.putText(grayscale, 'rajat', (400, 400), cv2.FONT_HERSHEY_SIMPLEX, 1,
(255, 0, 0), 5)
rows, cols, channels = img.shape
roi = frame[0:rows, 0:cols]
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(img_gray, 220, 255, cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)
frame_bg = cv2.bitwise_and(roi, roi, mask=mask)
img_fg = cv2.bitwise_and(img, img, mask=mask_inv)
add = frame_bg + img_fg
frame[0:rows, 0:cols] = add
out.write(frame)
cv2.imshow('webcam', frame)
cv2.imshow('grayscale', grayscale)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def Basket_Detection(image):
# Import the necessary packages
import time
import cv2
import imutils
import numpy as np
# User variables
area_max = 35000
area_min = 1500
apx_min = 1
apx_max = 5
threshold_value = 5
s4_thresh = 150
s3_thresh = 280
s2_thresh = 360
s1_thresh = 440
s0_thresh = 520
s_left = 220
s_right = 290
# Define range of blue color in HSV
lower_green = np.array([35, 50, 50])
upper_green = np.array([75, 255, 255])
# Convert BGR to HSV
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, lower_green, upper_green)
# Bitwise-AND mask and original image
residual = cv2.bitwise_and(image, image, mask=mask)
# Contour Detection
# Convert the residual image to gray scale
gray = cv2.cvtColor(residual, cv2.COLOR_BGR2GRAY)
# Blur the image to eliminate high frequency noise
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# Threshold the image to map blue values to white
threshold = cv2.threshold(blurred, threshold_value, 255,
cv2.THRESH_BINARY)[1]
# Find the contours
contours = cv2.findContours(threshold, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# Use the first or second element depending on opencv version
contours = contours[0] if imutils.is_cv2() else contours[1]
# Define parameters to track the closest ball to the reference point
prev_distance = 600000
closest_cX = 0
closest_cY = 0
# Loop over the contours
for c in contours:
approximate = cv2.approxPolyDP(c, 0.05 * cv2.arcLength(c, True), True)
area = cv2.contourArea(c)
if (area > area_min) & (area < area_max):
# Compute the center of the contour
M = cv2.moments(c)
# The if there to avoid dividing by 0 errors.
if (M["m00"] == 0):
M["m00"] = 1
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
distance = (abs(cX - 230)**2) + (abs(544 - cY)**2)
# Update the values if the next contour is closer
if (distance < prev_distance):
prev_distance = distance
closest_cX = cX
closest_cY = cY
if (closest_cX == 230):
closest_cX = 231
# Mark the closest ball with black center
Arct = np.arctan((544 - closest_cY) / (closest_cX - 230)) / np.pi
# Decide to turn or go straight
if (closest_cX == 0):
operation = "nbx"
elif ((closest_cX > s_left) and (closest_cX < s_right)):
if (closest_cY < s4_thresh):
operation = "s4x"
elif (closest_cY < s3_thresh):
operation = "s3x"
elif (closest_cY < s2_thresh):
operation = "s2x"
elif (closest_cY < s1_thresh):
operation = "s1x"
elif (closest_cY < s0_thresh):
operation = "rcx"
else:
operation = "nbx"
elif (Arct < 0):
if (Arct > -0.5 / 8):
operation = "l2x"
else:
operation = "l1x"
else:
if (Arct < 0.5 / 8):
operation = "r2x"
else:
operation = "r1x"
return operation
import cv2
src = cv2.imread('coins.png', cv2.IMREAD_GRAYSCALE)
if src is None:
print('Image load failed!')
sys.exit()
h, w = src.shape[:2]
dst1 = np.zeros((h, w, 3), np.uint8)
dst2 = np.zeros((h, w, 3), np.uint8)
# 전처리
src = cv2.blur(src, (3, 3))
_, src_bin = cv2.threshold(src, 0, 255, cv2.THRESH_OTSU)
# 레이블링
cnt, labels, stats, centroids = cv2.connectedComponentsWithStats(src_bin)
for i in range(1, cnt):
c = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
dst1[labels == i] = c
# 외곽선 검출
contours, _ = cv2.findContours(src_bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
for i in range(len(contours)):
c = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
cv2.drawContours(dst2, contours, i, c, 1)
cv2.imshow('src', src)
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) > 2:
candidates.append(approx)
mask = np.zeros_like(gray_image)
cv2.drawContours(mask, candidates, 0, (255, 255, 255), -1)
#cv2.imshow("mask", mask)
out = np.zeros_like(gray_image)
out[mask == 255] = gray_image[mask == 255]
#cv2.imshow("woo", out)
ret, filter_image = cv2.threshold(gray_image, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
ret, filter_maskimage = cv2.threshold(out, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
dots = cv2.bilateralFilter(filter_maskimage, 11, 17, 17)
_, cnts, _ = cv2.findContours(dots, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
print(len(cnts) - 1) # -1 because the die itself counts
try:
cv2.imshow("mask", mask)
cv2.imshow("woo", out)
cv2.imshow("Dice", image)
cv2.imshow("Dice - gray", gray_image)
cv2.imshow("Dice - filtered", filter_image)
cv2.imshow("Dice - masked - filtered", filter_maskimage)
cv2.waitKey(0)
def create_annotation_info(annotation_id,
image_id,
category_info,
binary_mask,
image_size=None,
tolerance=2,
bounding_box=None):
if bounding_box is None:
bounding_box = []
ret, thresh = cv2.threshold(binary_mask, 127, 255, 1)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
xmin = np.min(x)
xmax = np.max(x + w)
ymin = np.min(y)
ymax = np.max(y + h)
bounding_box.append([xmin, ymin, xmax, ymax])
# if bounding_box is None:
# obj_ids = np.unique(binary_mask)
# obj = []
# bounding_box = []
# for j in obj_ids:
# if j < 255:
# obj.append(j)
# num_obj = len(obj)
# if num_obj > 1:
# for i in range(num_obj):
# masks = binary_mask == obj[i]
# pos = np.where(masks)
# xmin = np.min(pos[1])
# xmax = np.max(pos[1])
# ymin = np.min(pos[0])
# ymax = np.max(pos[0])
# bounding_box.append([xmin, ymin, xmax, ymax])
# else:
# masks = binary_mask == obj
# pos = np.where(masks)
# xmin = np.min(pos[1])
# xmax = np.max(pos[1])
# ymin = np.min(pos[0])
# ymax = np.max(pos[0])
# bounding_box.append([xmin, ymin, xmax, ymax])
if category_info["is_crowd"]:
is_crowd = 1
segmentation = binary_mask_to_rle(binary_mask)
else:
is_crowd = 0
segmentation = binary_mask_to_polygon(binary_mask, tolerance)
if not segmentation:
return None
if image_size is not None:
binary_mask = resize_binary_mask(binary_mask, image_size)
binary_mask_encoded = mask.encode(
np.asfortranarray(binary_mask.astype(np.uint8)))
area = mask.area(binary_mask_encoded)
if area < 1:
return None
annotation_info = {
"id": annotation_id,
"image_id": image_id,
"category_id": category_info["id"],
"iscrowd": is_crowd,
"area": area.tolist(),
"bbox": bounding_box,
"segmentation": segmentation,
"width": binary_mask.shape[1],
"height": binary_mask.shape[0],
}
return annotation_info
