def _get_button_images(im):
im_mono = extract_color(im, (0, 180), (0, 120), (0, 120))
contours, hierarchy = cv2.findContours(im_mono, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = [cv2.approxPolyDP(c, 6, True) for c in contours]
midpoint_y = im.shape[1] / 2
contours = [c for c in contours if len(c) == 4 and cv2.boundingRect(c)[1] > midpoint_y and cv2.isContourConvex(c)]
button_box = sorted(contours, key=cv2.contourArea)[-1]
button_box = button_box.reshape((4, 2))
button_im = four_point_transform(im, button_box)
button_im_mono = extract_color(button_im, 18, (50, 100), (175, 255))
contours, hierarchy = cv2.findContours(button_im_mono, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# There's 4 buttons, which will be the largest 4 contours
contours = sorted(contours, key=cv2.contourArea)[-4:]
contour_rects = [cv2.boundingRect(c) for c in contours]
# Sort from left to right
contour_rects = sorted(contour_rects, key=lambda rect: rect[0])
buttons = []
for x, y, w, h in contour_rects:
button = four_point_transform(button_im, np.array(((x, y), (x + w, y), (x, y + h), (x + w, y + h))), -6)
button = extract_color(button, 18, (50, 100), (175, 255))
buttons.append(button)
return buttons
def _get_screen_image(im):
im_mono = extract_color(im, 76, (50, 150), (50, 150))
# show(im_mono)
contours, hierarchy = cv2.findContours(im_mono, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
midpoint_y = im.shape[1] * 2 / 3
contours_filtered = []
for contour in contours:
# contour = cv2.approxPolyDP(contour, 2, True)
x, y, w, h = cv2.boundingRect(contour)
# if y + h < midpoint_y and cv2.isContourConvex(contour):
if y + h < midpoint_y:
contours_filtered.append(contour)
contours = sorted(contours_filtered, key=cv2.contourArea)[-4:]
boxes = [cv2.boundingRect(c) for c in contours]
x1 = min(x for x, y, w, h in boxes)
y1 = min(y for x, y, w, h in boxes)
x2 = max(x + w for x, y, w, h in boxes)
y2 = max(y + h for x, y, w, h in boxes)
points = np.array(((x1, y1), (x1, y2), (x2, y1), (x2, y2)))
screen = four_point_transform(im, points)
screen_mono = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
_, screen_mono = cv2.threshold(screen_mono, 245, 255, cv2.THRESH_BINARY)
return screen_mono
def contourparser(contours):
if contours:
contour1,contour2,contour3 = contours[0:3]
cnt1x,cnt1y, h,w = cv2.boundingRect(contour1)
cnt2x,cnt2y, h,w = cv2.boundingRect(contour2)
cnt3x,cnt3y, h,w = cv2.boundingRect(contour3)
dist12 = np.sqrt((cnt1x-cnt2x)**2 + (cnt1y-cnt2y)**2)
dist13 = np.sqrt((cnt1x-cnt3x)**2 + (cnt1y-cnt3y)**2)
dist23 = np.sqrt((cnt2x-cnt3x)**2 + (cnt2y-cnt3y)**2)
mindist = np.argmin([dist12,dist13,dist23])
if mindist == 0 and dist12 < 26 and dist13 < 100:
eye1 = [contour1,cnt1x,cnt1y]
eye2 = [contour2,cnt2x,cnt2y]
swimb = [contour3,cnt3x,cnt3y]
elif mindist == 1 and dist13 < 26 and dist12 < 100:
eye1 = [contour1,cnt1x,cnt1y]
eye2 = [contour3,cnt3x,cnt3y]
swimb = [contour2,cnt2x,cnt2y]
elif mindist == 2 and dist23 < 26 and dist13 < 100:
eye1 = [contour2,cnt2x,cnt2y]
eye2 = [contour3,cnt3x,cnt3y]
swimb = [contour1,cnt1x,cnt1y]
else:
eye1 = []
eye2 = []
swimb = []
return eye1,eye2,swimb
else:
return [],[],[]
def warpTriangle(img1, img2, t1, t2) :
# Find bounding rectangle for each triangle
r1 = cv2.boundingRect(np.float32([t1]))
r2 = cv2.boundingRect(np.float32([t2]))
# Offset points by left top corner of the respective rectangles
t1Rect = []
t2Rect = []
t2RectInt = []
for i in xrange(0, 3):
t1Rect.append(((t1[i][0] - r1[0]),(t1[i][1] - r1[1])))
t2Rect.append(((t2[i][0] - r2[0]),(t2[i][1] - r2[1])))
t2RectInt.append(((t2[i][0] - r2[0]),(t2[i][1] - r2[1])))
# Get mask by filling triangle
mask = np.zeros((r2[3], r2[2], 3), dtype = np.float32)
cv2.fillConvexPoly(mask, np.int32(t2RectInt), (1.0, 1.0, 1.0), 16, 0);
# Apply warpImage to small rectangular patches
img1Rect = img1[r1[1]:r1[1] + r1[3], r1[0]:r1[0] + r1[2]]
size = (r2[2], r2[3])
img2Rect = applyAffineTransform(img1Rect, t1Rect, t2Rect, size)
img2Rect = img2Rect * mask
# Copy triangular region of the rectangular patch to the output image
img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] = img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] * ( (1.0, 1.0, 1.0) - mask )
img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] = img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] + img2Rect
def get_table_bounds(self):
'''Get best possible table bounds
'''
table_bounds = None
image_gray = cv2.cvtColor(self.image_object,cv2.COLOR_BGR2GRAY)
temp_image, contours, hierarchy = cv2.findContours(image_gray,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
best_match_contour_index = None
max_contour_size = 0
count = 0
for contour in contours:
if cv2.contourArea(contour) > max_contour_size:
contour_size = cv2.contourArea(contour)
x,y,w,h = cv2.boundingRect(contour)
if x>1 and y>1 and contour_size > max_contour_size:
best_match_contour_index = count
max_contour_size = contour_size
count += 1
if best_match_contour_index:
x,y,w,h = cv2.boundingRect(contours[best_match_contour_index])
x = x - BUFFER_LENGTH
w = w + BUFFER_LENGTH
cv2.rectangle(self.image_object,(x,y),(x+w,y+h),(0,0,0),2)
cv2.rectangle(self.image_object,(x,y),(x+w,y+h),(255,0,0),4)
table_bounds = {"top":y*self.vertical_ratio, "left":x*self.horizontal_ratio, "bottom":(h+y)*self.vertical_ratio, "right":(w+x)*self.horizontal_ratio}
cv2.imwrite(self.temp_img_file, self.image_object)
return table_bounds
def constructObj(contours, rIntersec, rSize):
cnt = sorted(contours, key=getKey, reverse=True)
nCnt = len(cnt)
obj = cnt[0];
fundRect = cv2.boundingRect(obj)
ratios = np.zeros(nCnt, dtype=float)
for i in range(1, nCnt):
rect = cv2.boundingRect(cnt[i])
sRect = rect[2]*rect[3]
sIntersec = max(0, min(fundRect[0] + fundRect[2], rect[0] + rect[2]) - max(fundRect[0], rect[0])) \
* max(0, min(fundRect[1] + fundRect[3], rect[1]+rect[3]) - max(fundRect[1], rect[1]))
ratios[i] = sIntersec / float(sRect)
if cv2.contourArea(cnt[i]) > rSize*cv2.contourArea(obj) and ratios[i] > rIntersec:
#print i, obj.shape, np.shape(cnt[i])
obj = np.append(obj, cnt[i], axis=0)
fundRect = cv2.boundingRect(obj)
return obj, ratios
def recupdate(contours): areas = [cv2.contourArea(c) for c in contours] def showprev(): cv2.rectangle(frame, (self.x, self.y), (self.x + self.w, self.y + self.h), (a, b, ccCccc), 2) cv2.rectangle(hsv_img, (self.x, self.y), (self.x + self.w, self.y + self.h), (a, b, ccCccc), 2) if areas != []: threshhold = 0.9 oflast = 30 #frames max_index = np.argmax(areas) cnt = contours[max_index] nowx, nowy, noww, nowh = cv2.boundingRect(cnt) self.previouswidths.append(noww) self.previousheights.append(nowh) if (threshhold<(float(noww)/float(np.median(self.previouswidths[-oflast:])))<(1/threshhold)) and (threshhold<(float(nowh)/float(np.median(self.previousheights[-oflast:])))<(1/threshhold)): Height, Width, trash = frame.shape self.x, self.y, self.w, self.h = cv2.boundingRect(cnt) if (0.95 < self.w/self.h < 1/0.95) or len(contours) == 1: cv2.rectangle(frame, (self.x, self.y), (self.x + self.w, self.y + self.h), (a, b, ccCccc), 2) cv2.rectangle(hsv_img, (self.x, self.y), (self.x + self.w, self.y + self.h), (a, b, ccCccc), 2) else: contours = contours[:max_index] + contours[max_index + 1:] recupdate(contours) else: showprev() else: showprev()
def calibrate(self):
doublecheck = 1
firstTime = True
while(doublecheck<=5):
try:
count = 0
cnts = self.getContours()[0:2]
x1,y1,w1,h1 = cv2.boundingRect(cnts[0])
x2,y2,w2,h2 = cv2.boundingRect(cnts[1])
if(firstTime or self.closeTo(x1,self.originX, 600)):
self.originX = x1
count = count +1
if(firstTime or self.closeTo(x1,self.originY, 600)):
self.originY = y1
count = count+1
if(firstTime or self.closeTo((x2+w2)-x1,self.width, 600)):
self.width = (x2+w2)-x1
count = count +1
if(firstTime or self.closeTo((y2+h2)-y1,self.hieght,600)):
self.hieght = (y2+h2)-y1
count = count +1
if(count == 4):
firstTime = False
doublecheck = doublecheck+1
else:
firstTime = True
doublecheck = 1
except:
print("err")
continue
def morphTriangle(img1, img2, img, t1, t2, t, alpha):
# Find bounding rectangle for each triangle
r1 = cv2.boundingRect(np.float32([t1]))
r2 = cv2.boundingRect(np.float32([t2]))
r = cv2.boundingRect(np.float32([t]))
# Offset points by left top corner of the respective rectangles
t1Rect = []
t2Rect = []
tRect = []
for i in range(0, 3):
tRect.append(((t[i][0] - r[0]), (t[i][1] - r[1])))
t1Rect.append(((t1[i][0] - r1[0]), (t1[i][1] - r1[1])))
t2Rect.append(((t2[i][0] - r2[0]), (t2[i][1] - r2[1])))
# Get mask by filling triangle
mask = np.zeros((r[3], r[2], 3), dtype=np.float32)
cv2.fillConvexPoly(mask, np.int32(tRect), (1.0, 1.0, 1.0), 16, 0);
# Apply warpImage to small rectangular patches
img1Rect = img1[r1[1]:r1[1] + r1[3], r1[0]:r1[0] + r1[2]]
img2Rect = img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]]
size = (r[2], r[3])
warpImage1 = applyAffineTransform(img1Rect, t1Rect, tRect, size)
warpImage2 = applyAffineTransform(img2Rect, t2Rect, tRect, size)
# Alpha blend rectangular patches
imgRect = (1.0 - alpha) * warpImage1 + alpha * warpImage2
# Copy triangular region of the rectangular patch to the output image
img[r[1]:r[1] + r[3], r[0]:r[0] + r[2]] = img[r[1]:r[1] + r[3], r[0]:r[0] + r[2]] * (1 - mask) + imgRect * mask
def eye_region(shape): pointsl = np.ndarray((6,1,2), dtype=np.float32) pointsr = np.ndarray((6,1,2), dtype=np.float32) for i in range(36,42): pointsl[i-37,0] = (shape.part(i).x, shape.part(i).y) for i in range(42,48): pointsr[i-43,0] = (shape.part(i).x, shape.part(i).y) rectl = cv2.boundingRect(pointsl) rectr = cv2.boundingRect(pointsr) xl = rectl[0] + rectl[2]/2.0 yl = rectl[1] + rectl[3]/2.0 xr = rectr[0] + rectr[2]/2.0 yr = rectr[1] + rectl[3]/2.0 w = (xr - xl) * 3 / 10 h = (xr - xl) / 10 rectl = dlib.rectangle(int(xl-w),int(yl-h), int(xl+w),int(yl+h)) rectr = dlib.rectangle(int(xr-w),int(yr-h), int(xr+w),int(yr+h)) return rectl,rectr
def find_closest(mask, hand_dir, obj_dir):
Is = cv2.imread(mask)
b_contour = get_contour(Is)
rect = cv2.boundingRect(b_contour)
Ib = Is[rect[1]:rect[1]+rect[3], rect[0]:rect[0]+rect[2], 0]
Ib = cv2.resize(Ib, (512, 512))
cv2.imshow('Ib', Ib)
min_img = None
min_distance = float('inf')
min_t_contour = None
min_It = None
for t in ls_files(obj_dir, '.png'):
It = cv2.imread(t)
bg_mask = get_bg(It)
It[bg_mask] = 0
It[~bg_mask] = 255
t_contour = get_contour(It)
rect = cv2.boundingRect(t_contour)
It = It[rect[1]:rect[1]+rect[3], rect[0]:rect[0]+rect[2], 1]
It = cv2.resize(It, (512, 512))
dist = calc_distance(Ib, It)
if min_distance > dist:
min_distance = dist
min_img = t
min_t_contour = t_contour
min_It = It
cv2.imshow('It', min_It)
min_hand_img = os.path.join(hand_dir, os.path.basename(min_img))
min_contact_img = os.path.join(contact_dir, os.path.basename(min_img))
return min_img, min_hand_img, min_contact_img, b_contour, min_t_contour
def shape_match():
img1 = cv2.imread('l1.png', 0)
img2 = cv2.imread('l2.png', 0)
ret, thresh1 = cv2.threshold(img1, 13, 255, 1)
ret, thresh2 = cv2.threshold(img2, 13, 255, 1)
im1,contours1,hierarchy = cv2.findContours(thresh1, 2, 1)
im2,contours2,hierarchy = cv2.findContours(thresh2, 2, 1)
print len(contours1), len(contours2)
cv2.drawContours(img1,contours1, -1, (200,0,0))
cv2.drawContours(img2,contours2, -1, (200,0,0))
cv2.imshow("1",img1)
cv2.imshow("2",img2)
cv2.waitKey()
#query_cnt = get_correct_cnt(contours2, img2)
#if query_cnt is None:
# print 'parse img failed'
# return
height, width = img1.shape
area = height * width
min_area = area / 25
max_area = area / 5
for cnt in contours1:
print cv2.boundingRect(cnt)
letter_area = get_cnt_area(cnt)
if not (min_area < letter_area and letter_area < max_area):
continue
print cv2.matchShapes(cnt, query_cnt, 1, 0.0)
def detectRover(self, argFrame):
frame = self.frame
hsvFrame = self.frame
thresh = self.frame[:,:,0]
rGreen = (38,67,155,198,0,255)
rPink = (165,182,155,192,0,255)
hsvFrame = cv2.cvtColor(self.frame.copy(), cv2.COLOR_BGR2HSV)
thresh = cv2.inRange(hsvFrame.copy(),np.array([rGreen[0],rGreen[2],rGreen[4]]),np.array([rGreen[1],rGreen[3],rGreen[5]]))
thresh = cv2.medianBlur(thresh.copy(),5)
thresh = cv2.erode(thresh.copy(), erodeElem)
#thresh = cv2.erode(thresh.copy(), erodeElem)
thresh = cv2.dilate(thresh.copy(), dilateElem)
thresh = cv2.dilate(thresh.copy(), dilateElem)
_, contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) != 1:
return -1
(x,y,w,h) = cv2.boundingRect(contours[0])
greenPt = (int((x+x+w)/2),int((y+y+h)/2))
thresh = cv2.inRange(hsvFrame.copy(),np.array([rPink[0],rPink[2],rPink[4]]),np.array([rPink[1],rPink[3],rPink[5]]))
thresh = cv2.medianBlur(thresh.copy(),5)
thresh = cv2.erode(thresh.copy(), erodeElem)
#thresh = cv2.erode(thresh.copy(), erodeElem)
thresh = cv2.dilate(thresh.copy(), dilateElem)
thresh = cv2.dilate(thresh.copy(), dilateElem)
_, contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) != 1:
return -1
(x,y,w,h) = cv2.boundingRect(contours[0])
pinkPt = (int((x+x+w)/2),int((y+y+h)/2))
self.roverPos = (int((greenPt[0]+pinkPt[0])/2),int((greenPt[1]+pinkPt[1])/2))
angle = getAngle(pinkPt[0],pinkPt[1],greenPt[0],greenPt[1])
self.roverHeading = 360+angle[2]*-1
return greenPt, pinkPt
def __find_conveyor(self, initialization_frame):
"""Set x, y, w, and h to fit the conveyor size
:param initialization_frame: Init initialization_frame from which we will calibrate others frames
:return: None
"""
kernel_size = int(self.__image_width / 40)
kernel = np.ones((kernel_size, kernel_size), np.uint8)
threshold = cv2.inRange(initialization_frame, self.CONVEYOR_COLOR_MIN, self.CONVEYOR_COLOR_MAX)
_, threshold = cv2.threshold(threshold, 200, 255, cv2.THRESH_BINARY)
self.__show('Conveyor image', threshold)
threshold = cv2.morphologyEx(threshold, cv2.MORPH_OPEN, kernel, iterations=3)
threshold = cv2.morphologyEx(threshold, cv2.MORPH_CLOSE, kernel, iterations=3)
_, contours, _ = cv2.findContours(threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
biggest_contour = self.__get_biggest_contour(contours)
if biggest_contour is None:
self.__log('Conveyor not found')
return 0, 0, 0, 0
else:
self.__log('Conveyor found at: ' + str(cv2.boundingRect(biggest_contour)))
return cv2.boundingRect(biggest_contour)
def operationsStage3_nontextContoursFiltering(img_pass1):
img_pass1Copy = img_pass1.copy()
npaContours, npaHierarchy = cv2.findContours(img_pass1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
for i in range(1,len(npaContours)):
[intX, intY, intWidth, intHeight] = cv2.boundingRect(npaContours[i])
area1 = intHeight * intWidth
for j in range(1,len(npaContours)):
[X, Y, Width, Height] = cv2.boundingRect(npaContours[j])
cx = X + (Width/2)
cy = Y + (Height/2)
area2 = Width * Height
if intX < cx < intX + intWidth and intY < cy < intY + intHeight and i != j and area1 > area2:
cv2.drawContours(img_pass1Copy,npaContours,j,255,-1)
#cv2.waitKey(0)
cv2.imshow('imgPass1Copy',img_pass1Copy)
#cv2.waitKey(0)
cv2.imshow('imgPass1Copy',img_pass1Copy)
cv2.imwrite('files/1.jpg',img_pass1Copy)
return img_pass1Copy
def _get_pointer_canny(self, frame, x, y, w, h, window):
# finding out ROI
new_x = x
new_w = (7 * w) / 6
new_y = max(y-int(3*h/4.0), 0)
new_h = y + int(3*h/4.0) - new_y
target = frame[new_y:new_y+new_h, new_x:new_x+new_w]
target_gray = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)
# Finding out canny edges
canny_img = cv2.Canny(target_gray, 100, 200)
contours, hierarchy = cv2.findContours(canny_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours_img = cv2.cvtColor(target_gray, cv2.COLOR_GRAY2BGR)
# cv2.drawContours(contours_img, contours, -1, (0, 0, 255), 1)
# Finding finger tip
top_cnt = []
rects = []
min_y = 100000
for cnt in contours:
(rectx, recty, rectw, recth) = cv2.boundingRect(cnt)
if recty < min_y:
top_cnt = cnt
rects.append(cv2.boundingRect(top_cnt))
# Drawing rectangle
for rect in rects:
x, y, w, h = rect
cv2.rectangle(contours_img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imshow(window, contours_img)
return x, y
def detectCardInColumn(column, cx, cy, output = None):
cards = []
gray = cv2.inRange(column, (200,200,200), (255,255,255))
ret,thresh = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY)
if thresh == None:
return cards
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
area = cv2.contourArea(cnt)
if area > 6000 and area < 7000:
[x,y,w,h] = cv2.boundingRect(cnt)
card = column[y:(y+h), x:(x+w)]
data = detectCard(card)
success = 1 if data[0] != '' else 0
if success == 1:
cards.append((data, (cx + x, cy + y, w,h ), 0, cy + y))
if output != None:
cv2.rectangle(output,(cx + x, cy + y),(cx + x + w, cy + y + h),(0,0,255),2)
elif area > 700 and area < 1500:
[x,y,w,h] = cv2.boundingRect(cnt)
card = column[y:(y+h), x:(x+w)]
data = detectCard(card)
success = 1 if data[0] != '' else 0
if success == 1:
cards.append((data, (cx + x, cy + y, w,h ), 1, cy + y))
if output != None:
cv2.rectangle(output,(cx + x, cy + y),(cx + x + w, cy + y + h),(0,0,255),2)
return sorted(cards, key=itemgetter(3))
def match_overlap(self, contour):
# first check if the contour directly overlaps with a previous
(cx, cy), radius = cv2.minEnclosingCircle(contour)
lf = self.frames[self.last_frame]
if cx > lf.cx + 6:
return False
x1, y1, w1, h1 = cv2.boundingRect(contour)
x2, y2, w2, h2 = cv2.boundingRect(lf.contour)
if x2 < x1:
x1, y1, w1, h1 = cv2.boundingRect(lf.contour)
x2, y2, w2, h2 = cv2.boundingRect(contour)
bx, by, bw, bh = cv2.boundingRect(lf.contour)
#logging.debug("Checking LF Box: " + str(bx) + "\t" + str(bx+bw) + "\t" + str(by) + "\t" + str(by+bh))
#logging.debug("Checking Cnt : " + str(cx+radius) + "\t" + str(cy))
if not x1 <= x2 <= x1 + w1:
return False
return (y1 <= y2 <= y1 + h1) or (y2 <= y1 <= y2 + h2)
def draw_bounding(img_url, n):
"""Given input image, draw bounding rectangles on top of
first n contours"""
img = cv2.imread(img_url)
original = cv2.imread(img_url)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, img = cv2.threshold(img, 127, 255, 1)
plt.gray()
contours, hier = cv2.findContours(np.array(img),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if len(contours) == 1:
print cv2.boundingRect(contours[0])
cv2.drawContours(img, [contours[0]], 0, 0, 100)
contours, hier = cv2.findContours(np.array(img),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
#sort contours by maximum area
contours = sorted(contours, key=lambda cnt:cv2.contourArea(cnt), reverse=True)
for cnt in contours[:n]:
x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(original, (x,y), (x+w,y+h), (0,255,0),2)
plt.imshow(original)
plt.show()
def get_conts(gray):
"""
Grab two biggest contours from grayscale image
:param gray: grayscale image
:return: list of contours
"""
conts = []
ret, thresh = cv2.threshold(gray, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
max_contour_2 = 0
max_contour_1 = 0
for i in range(len(contours)):
if len(contours[i]) > len(contours[max_contour_1]):
max_contour_2 = max_contour_1
max_contour_1 = i
if contours:
rect_1 = cv2.boundingRect(contours[max_contour_1])
rect_2 = cv2.boundingRect(contours[max_contour_2])
else:
return conts
if nested_rect(rect_2, rect_1):
conts.append(contours[max_contour_2])
return conts
else:
conts.append(contours[max_contour_1])
conts.append(contours[max_contour_2])
return conts
def findMaxContours():
conts = []
for c in range(180):
# for c in [170,50,86,111]:
lower_blue = np.array([c-hdelta,0,0])
upper_blue = np.array([c+hdelta,255,250])
mask = cv2.inRange(img, lower_blue, upper_blue)
x = kersize
kernel = np.ones((x,x), np.uint8)
erosion = cv2.dilate(mask,kernel,iterations = 1)
kernel = np.ones((x/2,x/2), np.uint8)
erosion = cv2.erode(erosion,kernel,iterations = 1)
contours,h = cv2.findContours(erosion,1,2)
for i,cnt in enumerate(contours):
x,y,w,h = cv2.boundingRect(cnt)
if w > 0.6 * img.shape[1]:
app = True
for cnt2 in conts:
if cv2.pointPolygonTest(cnt2, tuple(cnt2[0][0]) , False):
app = False
if app:
conts.append(cnt)
contsS = sorted(conts, key=lambda x: cv2.boundingRect(x)[2] )
for i,cnt in enumerate(contsS[-90:] ):
cv2.drawContours(img,[cnt],0,(0,i*10 % 255, (i*223)%255 ),4)
cv2.imshow('dst',img)
return contsS[-100:]
def line_sort(a,b):
ax,ay,_,_ = cv2.boundingRect(a)
bx,by,_,_ = cv2.boundingRect(b)
if abs(ay-by) > 15:
return ay-by
else:
return ax-bx
def warp_image(img, triangulation, base_points, coord):
"""
Realize the mesh warping phase
triangulation is the Delaunay triangulation of the base points
base_points are the coordinates of the landmark poitns of the reference image
code inspired from http://www.learnopencv.com/warp-one-triangle-to-another-using-opencv-c-python/
"""
all_points, coordinates = preprocess_image_before_triangulation(img)
img_out = 255 * np.ones(img.shape, dtype=img.dtype)
for t in triangulation:
# triangles to map one another
src_tri = np.array([[all_points[x][0], all_points[x][1]] for x in t]).astype(np.float32)
dest_tri = np.array([[base_points[x][0], base_points[x][1]] for x in t]).astype(np.float32)
# bounding boxes
src_rect = cv2.boundingRect(np.array([src_tri]))
dest_rect = cv2.boundingRect(np.array([dest_tri]))
# crop images
src_crop_tri = np.zeros((3, 2), dtype=np.float32)
dest_crop_tri = np.zeros((3, 2))
for k in range(0, 3):
for dim in range(0, 2):
src_crop_tri[k][dim] = src_tri[k][dim] - src_rect[dim]
dest_crop_tri[k][dim] = dest_tri[k][dim] - dest_rect[dim]
src_crop_img = img[src_rect[1]:src_rect[1] + src_rect[3], src_rect[0]:src_rect[0] + src_rect[2]]
# affine transformation estimation
mat = cv2.getAffineTransform(
np.float32(src_crop_tri),
np.float32(dest_crop_tri)
)
dest_crop_img = cv2.warpAffine(
src_crop_img,
mat,
(dest_rect[2], dest_rect[3]),
None,
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_REFLECT_101
)
# Use a mask to keep only the triangle pixels
# Get mask by filling triangle
mask = np.zeros((dest_rect[3], dest_rect[2], 3), dtype=np.float32)
cv2.fillConvexPoly(mask, np.int32(dest_crop_tri), (1.0, 1.0, 1.0), 16, 0)
# Apply mask to cropped region
dest_crop_img = dest_crop_img * mask
# Copy triangular region of the rectangular patch to the output image
img_out[dest_rect[1]:dest_rect[1] + dest_rect[3], dest_rect[0]:dest_rect[0] + dest_rect[2]] = \
img_out[dest_rect[1]:dest_rect[1] + dest_rect[3], dest_rect[0]:dest_rect[0] + dest_rect[2]] * (
(1.0, 1.0, 1.0) - mask)
img_out[dest_rect[1]:dest_rect[1] + dest_rect[3], dest_rect[0]:dest_rect[0] + dest_rect[2]] = \
img_out[dest_rect[1]:dest_rect[1] + dest_rect[3], dest_rect[0]:dest_rect[0] + dest_rect[2]] + dest_crop_img
return img_out[coord[2]:coord[3], coord[0]:coord[1]]
def drawRects(img, ctrs):
i = 1
rectList = []
for ct in ctrs[0]:
x, y, w, h = cv2.boundingRect(ct)
#process only vertical rectagles (ie, w<h) with w and h > 1
if w < h and w > 10 and h > 10:
#print i, ". ", len(ct), " -- ", cv2.boundingRect(ct), (x+w/2), cv2.minAreaRect(ct)
rectList.append([cv2.boundingRect(ct), cv2.minAreaRect(ct)])
clr=(random.randrange(0,255),random.randrange(0,255),random.randrange(0,255))
#cv2.drawContours(image=img, contours=ct, contourIdx=-1, color=clr , thickness=-1)
cv2.rectangle(img, (x,y), (x+w,y+h), clr, 5)
cv2.fillConvexPoly(img, ct, clr)
cv2.rectangle(img, (x+w/2-3,y), (x+w/2+3,y+h), (255,255,255), -1)
cv2.rectangle(img, (x,y+h/2-3), (x+w,y+h/2+3), (255,255,255), -1)
rotRect = cv2.minAreaRect(ct)
box = cv2.cv.BoxPoints(rotRect)
box = np.int0(box)
print box
cv2.drawContours(img, [box], 0, (0,0,255),2)
#cv2.imshow("asdsdasdadasdasd",img)
#key = cv2.waitKey(1000)
i = i + 1
cv2.rectangle(img, (318,0), (322,640), (255,255,255), -1)
cv2.imshow("Output",img)
print "done"
return rectList
def cutTaxPayer(img):
new_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
new_img = cv2.adaptiveThreshold(new_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 9 , 9)
contours0, hierarchy = cv2.findContours(new_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
H, W = img.shape[:2]
result = np.zeros((H, W), np.uint8)
for i in range(len(contours0) - 1, -1, -1):
cnt = contours0[i]
x, y, w, h = cv2.boundingRect(cnt)
if w < 10 or h < 10:
contours0.pop(i)
else :
cv2.rectangle(result, (x, y), (x + w, y + h), (255), 1)
for h in range(H):
for w in range(W):
if result[h][w] == 255:
cv2.line(result, (0, h), (W, h), (255))
break
contours1, hierarchy1 = cv2.findContours(result, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# for cnt in contours1:
# x, y, w, h = cv2.boundingRect(cnt)
# cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 0), 1)
# print x, y
# cv2.imshow("xxx1", img)
if len(contours1) < 2:
return np.ones((H / 4, W * 3 / 4, 3), np.uint8) * 255
x, y, w, h = cv2.boundingRect(contours1[-2])
conter = (x + w / 2, y + h / 2)
img = cv2.getRectSubPix(img, (w, h), conter)
return img
def find(self, frame):
if frame == None:
self.xbar = 99.0 # xbar, ybar should be in the range [-1.0, 1.0]
self.ybar = 99.0
self.diam = 99.0
return None
# The capture was successful. Start processing
hsv_image = cv2.cvtColor(frame, cv.CV_BGR2HSV)
# Choose mask based on self._is_red
if self._is_red:
# Red alliance
mask_neg = cv2.inRange(hsv_image, np.array((0, 50, 50)), np.array((10, 255, 255)))
mask_pos = cv2.inRange(hsv_image, np.array((170, 50, 50)), np.array((180, 255, 255)))
mask = mask_pos | mask_neg
else:
# Blue alliance
mask = cv2.inRange(hsv_image, np.array((105, 50, 50)), np.array((130, 255, 255)))
#Eroding and Dilating mask
opened = cv2.erode(mask, kernel, iterations = 7)
opened = cv2.dilate(opened, kernel, iterations = 7)
contours, hierarchy = cv2.findContours(opened, cv.CV_RETR_EXTERNAL, cv.CV_CHAIN_APPROX_NONE)
largest_size = 0
largest_index = 0
ball_found = False
if contours:
for index, contour in enumerate(contours):
if cv2.contourArea(contour) > largest_size:
#get co-ordinates and dimensions
x,y,w,h = cv2.boundingRect(contours[largest_index])
#ball squareness
ballratio = 1.0 * w/h
if ballratio > self.target_ratio and ballratio < 1.0/self.target_ratio:
largest_size = cv2.contourArea(contour)
largest_index = index
if largest_size > 0:
if w > self._MaxWidth:
moments = cv2.moments(contours[largest_index])
if moments['m00'] != 0:
self.xbar = 2.0*moments['m10']/moments['m00']/self._width - 1.0
self.ybar = 2.0*moments['m01']/moments['m00']/self._height - 1.0
x,y,w,h = cv2.boundingRect(contours[largest_index])
self.diam = (w + h)/ self._width
ball_found = True
if not ball_found:
# No ball found so set the member variables to invalid values
self.xbar = 99.0
self.ybar = 99.0
self.diam = 99.0
# Return the frame, the contours and largest image in case we
# want to show them on the screen
return (frame, contours, largest_index)
def getRects(ctrs, imageOut=None):
i = 1
rectList = []
#print "getRects(): {0} contours".format(len(ctrs[0]))
for ct in ctrs[0]:
#ct = ct.astype(np.int32)
bbox = cv2.boundingRect(ct)
x, y, w, h = bbox
length = ""
#process only vertical rectagles (ie, w<h) with w and h > 1
if w < h and w > 30 and h > 70:
#print i, ". ", len(ct), " -- ", cv2.boundingRect(ct), (x+w/2), cv2.minAreaRect(ct)
#dist = 320-(x+w/2)
#direction = 1
#if dist < 0:
# direction = -1
#print "Distance to center: ", dist, "pixels -- ", dist*0.0192, "inches --", dist*0.0192*1622/9.89,"revolutions"
#if (x < 320) and ((x+w) > 320):
if h > 173:
length = "large"
elif h > 140:
length = "medium"
elif h > 100:
length = "small"
#print i, " : ", cv2.boundingRect(ct), " -- ", length, "---", x, x+w, y, h
#color detection code here...
color = "red"
rectList.append([cv2.boundingRect(ct), cv2.minAreaRect(ct),length, color])
if imageOut is not None:
clr=(random.randrange(0,255),random.randrange(0,255),random.randrange(0,255))
#cv2.drawContours(image=imageOut, contours=ct, contourIdx=-1, color=clr , thickness=-1)
cv2.rectangle(imageOut, (x,y), (x+w,y+h), clr, 5)
#cv2.fillConvexPoly(imageOut, ct, clr)
cv2.rectangle(imageOut, (x+w/2-3,y), (x+w/2+3,y+h), (255,255,255), -1)
cv2.rectangle(imageOut, (x,y+h/2-3), (x+w,y+h/2+3), (255,255,255), -1)
rotRect = cv2.minAreaRect(ct)
box = cv2.cv.BoxPoints(rotRect)
box = np.int0(box)
#print box
#cv2.drawContours(imageOut, [box], 0, (0,0,255),2)
i = i + 1
if imageOut is not None:
cv2.rectangle(imageOut, (318,0), (322,640), (255,255,255), -1)
#cv2.imshow("Rects", imageOut)
#print "done"
## sort rectList by the first tuple - so that they are from left to right in image.
rectList.sort(key=lambda tup: tup[0])
return rectList
def text_regions(image):
img = cv2.imread(image)
img = imutils.resize(img,width = 1000)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))
joining_kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(10,5))
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#Detects dark regions, in this case writing
black = cv2.morphologyEx(img_gray, cv2.MORPH_BLACKHAT, kernel)
#Finds areas within dark regions that have vertical edges
gradX = cv2.Sobel(black, ddepth = cv2.CV_32F, dx = 1, dy =0, ksize = -1)
gradX= np.absolute(gradX)
(minVal, maxVal) = (np.min(gradX), np.max(gradX))
gradX = (255*((gradX - minVal)/(maxVal - minVal))).astype("uint8")
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, kernel)
thresh = cv2.threshold(gradX, 127 , 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, joining_kernel)
cv2.imshow("thresh", thresh)
cv2.waitKey(0)
contours, hierarchy = cv2.findContours(thresh,
cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
dollars = []
for contour in contours:
[x,y,w,h] = cv2.boundingRect(contour)
#get rid of very large and small contours
if w < 60 or w > 75 or h < 13 or h > 18 :
continue
#gets rid of contours in the check that cant be the money region(
#(ex theyre on the right side of check)
if x < 500:
continue
dollars = img_gray[y-10:y+h+10, x-10:x+w+10]
cv2.imshow("dollars", dollars)
cv2.waitKey(0)
#detecting contours in the dollars field
ret, im_th = cv2.threshold(dollars, 90, 255, cv2.THRESH_BINARY_INV)
ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
numbers = []
for ctr in ctrs:
[x,y,w,h] = cv2.boundingRect(ctr)
print("%d %d" %(w,h))
if w < 7 and h < 7: #ignore very small regions
continue
temp = dollars[y-3:y+h+3, x-2:x+w+2]
temp = cv2.resize(temp,(28,28), interpolation = cv2.INTER_AREA)
cv2.imshow("dollars", temp)
cv2.waitKey(0)
numbers.append(temp)
return numbers
def drawStuff(image,summaries):
#load image
orig = cv2.imread(image,cv2.IMREAD_UNCHANGED)
img = cv2.imread(image,cv2.IMREAD_UNCHANGED)
#autocanny
edges = auto_canny(img) #returns edged image
ret,thresh = cv2.threshold(edges,127,255,0)
contours,hierarchy = cv2.findContours(thresh, 1, 2)
print len(contours)
for i in range(0, len(contours)-1):
cnt = contours[i]
x,y,w,h = cv2.boundingRect(cnt)
r = cv2.boundingRect(cnt)
a = buildSobelMap(orig[r[1]:r[1]+r[3], r[0]:r[0]+r[2]])
b = buildNgMat(a)
feature = getNGFeatureVector(b)
output = StringIO.StringIO()
for i in range(0,len(feature)):
output.write(str(feature[i]))
output.write(",")
output.write("'?'")
content = output.getvalue()
output.close()
split = content.split(",")
split2 =([s.replace('\'', '') for s in split])
for i in range(0,(len(split2)-1)):
d=0
a = split2[i].replace('[','')
split2[i] = a
b = split2[i].replace(']','')
split2[i] = b
c = split2[i].split(" ")
for j in range(0,len(c)):
d+=fn(c[j])
split2[i]=d/3
pred = predict(summaries,split2)
if (pred==1.0):
cv2.rectangle(orig,(x,y),(x+w,y+h),(225,0,0))
elif (pred==0.0):
cv2.rectangle(orig,(x,y),(x+w,y+h),(0,0,225))
#cv2.rectangle(orig,(x,y),(x+w,y+h),(0,0,255))
#display
cv2.imshow(image,orig)
cv2.waitKey(0)
cv2.destroyAllWindows()
def extract_digits_and_symbols(image, charCnts, minW=5, minH=15):
# grab the internal Python iterator for the list of character
# contours, then initialize the character ROI and location
# lists, respectively
charIter = charCnts.__iter__()
rois = []
locs = []
# keep looping over the character contours until we reach the end
# of the list
while True:
try:
# grab the next character contour from the list, compute
# its bounding box, and initialize the ROI
c = next(charIter)
(cX, cY, cW, cH) = cv2.boundingRect(c)
roi = None
# check to see if the width and height are sufficiently
# large, indicating that we have found a digit
if cW >= minW and cH >= minH:
# extract the ROI
roi = image[cY:cY + cH, cX:cX + cW]
rois.append(roi)
locs.append((cX, cY, cX + cW, cY + cH))
# otherwise, we are examining one of the special symbols
else:
# MICR symbols include three separate parts, so we
# need to grab the next two parts from our iterator,
# followed by initializing the bounding box
# coordinates for the symbol
parts = [c, next(charIter), next(charIter)]
(sXA, sYA, sXB, sYB) = (np.inf, np.inf, -np.inf,
-np.inf)
# loop over the parts
for p in parts:
# compute the bounding box for the part, then
# update our bookkeeping variables
(pX, pY, pW, pH) = cv2.boundingRect(p)
sXA = min(sXA, pX)
sYA = min(sYA, pY)
sXB = max(sXB, pX + pW)
sYB = max(sYB, pY + pH)
# extract the ROI
roi = image[sYA:sYB, sXA:sXB]
rois.append(roi)
locs.append((sXA, sYA, sXB, sYB))
# we have reached the end of the iterator; gracefully break
# from the loop
except StopIteration:
break
# return a tuple of the ROIs and locations
return (rois, locs)
def analyse_image(image_path):
# print ("1")
# image_path="IMG-20180814-WA0002.jpg";
if not os.path.exists(image_path):
print("Image path does not exist")
return "{\"Error\":\"path_does_not_exist\"}"
image = cv2.imread(image_path)
if image is None:
return "{\"Error\":\"the file is not a image\"}"
image_candidates, sidx, cidx = get_objects(image, 13, 13, 20, 25, 20)
if len(image_candidates) == 0:
return "{\"Error\":\"no hierarchy\"}"
if len(image_candidates) != 2:
for i in range(1, 4):
if i == 1:
image_candidates, sidx, cidx = get_objects(
image, 13, 13, 20, 25, 10)
if len(image_candidates) == 2:
break
elif i == 2:
image_candidates, sidx, cidx = get_objects(
image, 13, 13, 20, 25, 20)
if len(image_candidates) == 2:
break
elif i == 3:
image_candidates, sidx, cidx = get_objects(
image, 13, 13, 20, 25, 65)
if len(image_candidates) == 2:
break
if len(image_candidates) != 2:
return "{\"Error\":\"objects not found\"}"
Strip = image_candidates[sidx]
color_checker = image_candidates[cidx]
#show_Image(image_candidates[1],'image_candidates[1]')
if Strip is None:
return "{\"Error\":\"Strip not found\"}"
#getting the position of 'Siemens' text in the image
Siemen_x, Siemens_y = get_strip_position(Strip)
if Siemen_x == 0 and Siemens_y == 0:
return "{\"Error\":\"no hierarchy in Strip\"}"
Y, X, Z = np.shape(Strip)
Sort = get_sort_direction(X, Y, Siemen_x, Siemens_y)
Strip_candidates = Strip_analysis(Strip, 23, 23, 20, 35, 5, 12)
if len(Strip_candidates) != 11:
for i in range(1, 14):
print(i)
if i == 1:
Strip_candidates = Strip_analysis(Strip, 23, 23, 20, 35, 5, 12)
if len(Strip_candidates) == 11:
break
elif i == 2:
Strip_candidates = Strip_analysis(Strip, 11, 11, 6, 12, 12, 15)
if len(Strip_candidates) == 11:
break
if i == 3:
Strip_candidates = Strip_analysis(Strip, 21, 21, 5, 4, 7, 9)
if len(Strip_candidates) == 11:
break
elif i == 4:
Strip_candidates = Strip_analysis(Strip, 15, 15, 0, 13, 12, 15)
if len(Strip_candidates) == 11:
break
elif i == 5:
#dont change 3159
Strip_candidates = Strip_analysis(Strip, 27, 27, 7, 29, 9, 11)
if len(Strip_candidates) == 11:
break
if i == 6:
Strip_candidates = Strip_analysis(Strip, 25, 25, 13, 25, 5, 8)
if len(Strip_candidates) == 11:
break
if i == 7:
Strip_candidates = Strip_analysis(Strip, 23, 23, 15, 25, 5, 12)
if len(Strip_candidates) == 11:
break
if i == 8:
Strip_candidates = Strip_analysis(Strip, 61, 61, 2, 25, 5, 10)
if len(Strip_candidates) == 11:
break
if i == 9:
# dont change 3159
Strip_candidates = Strip_analysis(Strip, 61, 61, 2, 35, 9, 12)
if len(Strip_candidates) == 11:
break
if i == 9:
# dont change 3159
Strip_candidates = Strip_analysis(Strip, 61, 61, 2, 35, 9, 12)
if len(Strip_candidates) == 11:
break
if i == 10:
Strip_candidates = Strip_analysis(Strip, 43, 43, 3, 25, 5, 15)
if len(Strip_candidates) == 11:
break
if i == 11:
Strip_candidates = Strip_analysis(Strip, 31, 31, 2, 25, 5, 7)
if len(Strip_candidates) == 11:
break
if i == 12:
Strip_candidates = Strip_analysis(Strip, 31, 31, 2, 18, 5, 7)
if len(Strip_candidates) == 11:
break
#works for latest
if i == 13:
Strip_candidates = Strip_analysis(Strip, 31, 31, 5, 25, 10, 12)
if len(Strip_candidates) == 11:
break
if len(Strip_candidates) != 11 and len(Strip_candidates) != 11:
return "{\"Error\":\"Strip_segmentation failed\"}"
#sorrt the contours based on the decided criteria
Strip_cnts, bounding_boxes = sort_contours(Strip_candidates, Sort)
idx = 0
pad_dict = {}
padlist = [
"LEU", "NIT", "URO", "PRO", "PH", "BLO", "SG", "KET", "BIL", "GLU", "k"
]
# print("length",len(Strip_cnts))
for c2 in Strip_cnts:
Strip_x, Strip_y, Strip_w, Strip_h = cv2.boundingRect(c2)
pad = Strip[Strip_y:Strip_y + Strip_h, Strip_x:Strip_x + Strip_w]
M = cv2.moments(c2)
cx = int((Strip_x + Strip_x + Strip_w) / 2)
cy = int((Strip_y + Strip_y + Strip_h) / 2)
idx += 1
if idx < 12:
bgr = Strip[cy, cx]
[mean_red, mean_green, mean_blue] = get_BGR(Strip, cx, cy)
pad_dict[padlist[idx - 1]] = [mean_red, mean_green, mean_blue]
#show_Image(pad, "pad" + str(idx))
#print(pad_dict)
colorchecker = color_checker
gray = cv2.cvtColor(colorchecker, cv2.COLOR_BGR2GRAY)
cnts, candidates = get_the_contours(colorchecker, gray, 7, 7, 13, 13, 0,
20, 8, 10)
if len(candidates) != 30:
for i in range(1, 9):
print(i)
if i == 1:
cnts, candidates = get_the_contours(colorchecker, gray, 5, 5,
11, 11, 0, 20, 8, 10)
if len(candidates) == 30:
# print("breaking here")
break
elif i == 2:
cnts, candidates = get_the_contours(colorchecker, gray, 9, 9,
15, 15, 4, 20, 8, 10)
# dont change.. for 3159
if len(candidates) == 30:
break
elif i == 3:
cnts, candidates = get_the_contours(colorchecker, gray, 7, 7,
21, 21, 9, 20, 5, 8)
if len(candidates) == 30:
break
elif i == 4:
cnts, candidates = get_the_contours(colorchecker, gray, 7, 7,
13, 13, 4, 20, 8, 10)
if len(candidates) == 30:
break
#
elif i == 5:
cnts, candidates = get_the_contours(colorchecker, gray, 7, 7,
21, 21, 5, 16, 5, 8)
if len(candidates) == 30:
#dont change.. for 3159
break
if i == 6:
cnts, candidates = get_the_contours(colorchecker, gray, 6, 6,
17, 17, 5, 25, 5, 7)
if len(candidates) == 30:
break
# elif i == 7:
# cnts, candidates = get_the_contours(colorchecker, gray, 9,9, 15, 15, 10,10, 5,8)
# if len(candidates) == 30:
# break
if len(candidates) != 30:
return "{\"Error\":\"color_checker segmentation failed\"}"
Maxcant = max(cnts, key=lambda cnts: cv2.contourArea(cnts))
x, y, w, h = cv2.boundingRect(Maxcant)
colorlist = []
if (w > h):
result = sort_by_row_col(deepcopy(candidates), 5, 6)
if len(result) == 30: #else should be handled from here
X25_Ratio = get_Red_Green_Ratio(colorchecker, result[24].cX,
result[24].cY)
X6_Ratio = get_Red_Green_Ratio(colorchecker, result[5].cX,
result[5].cY)
if X25_Ratio > 2:
colorlist = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
]
# print("x25_yes")
elif X6_Ratio > 2:
colorlist = [
30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16,
15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1
]
# print("x6_yes")
elif (h > w):
result = sort_by_row_col(deepcopy(candidates), 6, 5)
if len(result) == 30:
X1_Ratio = get_Red_Green_Ratio(colorchecker, result[0].cX,
result[0].cY)
X30_Ratio = get_Red_Green_Ratio(colorchecker, result[29].cX,
result[29].cY)
if X1_Ratio > 2:
colorlist = [
25, 19, 13, 7, 1, 26, 20, 14, 8, 2, 27, 21, 15, 9, 3, 28,
22, 16, 10, 4, 29, 23, 17, 11, 5, 30, 24, 18, 12, 6
]
# print("x1_yes")
elif X30_Ratio > 2:
colorlist = [
6, 12, 18, 24, 30, 5, 11, 17, 23, 29, 4, 10, 16, 22, 28, 3,
9, 15, 21, 27, 2, 8, 14, 20, 26, 1, 7, 13, 19, 25
]
# print("x29_yes")
idx = 0
color_dict = {}
if len(colorlist) != 30:
return "{\"Error\":\"color_checker segmentation failed\"}"
if len(result) == 30:
for con in result:
if len(colorlist) == 30:
color_dict[colorlist[idx]] = get_BGR(colorchecker, con.cX,
con.cY)
x, y, w, h = cv2.boundingRect(con.contour)
idx += 1
print(color_dict)
pad = pd.DataFrame.from_dict(
pad_dict,
orient='index',
columns=['Mean_red', 'Mean_Green', 'Mean_Blue'])
print(pad)
pad.iloc[:, 0] = pad.iloc[:, 0].div(int(pad.loc['k', 'Mean_red']))
pad.iloc[:, 1] = pad.iloc[:, 1].div(int(pad.loc['k', 'Mean_Green']))
pad.iloc[:, 2] = pad.iloc[:, 2].div(int(pad.loc['k', 'Mean_Blue']))
color_checker_CC = pd.DataFrame.from_dict(
color_dict,
orient='index',
columns=['Mean_red', 'Mean_Green', 'Mean_Blue'])
color_checker_CC.iloc[:, 0] = color_checker_CC.iloc[:, 0].div(
int(color_checker_CC.iloc[7, 0]))
color_checker_CC.iloc[:, 1] = color_checker_CC.iloc[:, 1].div(
int(color_checker_CC.iloc[7, 1]))
color_checker_CC.iloc[:, 2] = color_checker_CC.iloc[:, 2].div(
int(color_checker_CC.iloc[7, 2]))
color_checker_CS = pd.read_csv(
'ColorChecker30.csv',
header=None,
skiprows=2,
names=['CC_no', 'Mean_red', 'Mean_Green', 'Mean_Blue'],
index_col='CC_no')
color_checker_CS.iloc[:, 0] = color_checker_CS.iloc[:, 0].div(
int(color_checker_CS.iloc[7, 0]))
color_checker_CS.iloc[:, 1] = color_checker_CS.iloc[:, 1].div(
int(color_checker_CS.iloc[7, 1]))
color_checker_CS.iloc[:, 2] = color_checker_CS.iloc[:, 2].div(
int(color_checker_CS.iloc[7, 2]))
diff = color_checker_CC.sub(color_checker_CS)
corrected_dict = {}
look_up_dict = {}
look_up_dict = {
'LEU': {
'Neg': 0.9079,
'Trace': 0.82,
'Small': 0.6627,
'Moderate': 0.4995,
'Large': 0.348
},
'NIT': {
'Negative': 0.9793,
'Positive': 0.8947
},
'URO': {
0.2: 0.8123,
1: 0.7053,
2: 0.626,
4: 0.5384,
'> 8.0': 0.4817
},
'PRO': {
'Neg': 0.8871,
'Trace': 0.7117,
30: 0.6096,
100: 0.5176,
'>=300': 0.4228
},
'BLO': {
'Neg': 0.6531,
'Trace-Lysed': 0.5031,
'Moderate': 0.2289
},
'SG': {
'<=1.00': 0.2173,
'1.01': 0.2781,
'1.015': 0.4088,
'1.02': 0.4522,
'>=1.030': 0.6096
},
'KET': {
'Neg': 0.7519,
'Trace': 0.6063,
15: 0.5122,
40: 0.3934,
80: 0.2534,
'>=160': 0.1685
},
'BIL': {
'Neg': 0.9404,
'Small': 0.8072,
'Moderate': 0.7263,
'Large': 0.6186
},
'GLU': {
'Neg': 0.7659,
100: 0.7346,
250: 0.6903,
500: 0.3729,
'>=1000': 0.3118
},
'PH': {
'5.0': 0.8893,
5.5: 0.8792,
6.0: 0.8336,
6.5: 0.768,
7.0: 0.561,
7.5: 0.4238,
8.5: 0.3273
}
}
get__values(corrected_dict, "GLU", 'Mean_Green', 29, 26, 1, diff,
color_checker_CC, pad, look_up_dict)
get__values(corrected_dict, "BIL", 'Mean_Green', 28, 1, 19, diff,
color_checker_CC, pad, look_up_dict)
get__values(corrected_dict, "KET", 'Mean_Green', 2, 24, 1, diff,
color_checker_CC, pad, look_up_dict)
get__values(corrected_dict, "SG", 'Mean_Green', 26, 29, 28, diff,
color_checker_CC, pad, look_up_dict)
get__values(corrected_dict, "BLO", 'Mean_Green', 28, 19, 29, diff,
color_checker_CC, pad, look_up_dict)
get__values(corrected_dict, "PH", 'Mean_red', 12, 28, 26, diff,
color_checker_CC, pad, look_up_dict)
get__values(corrected_dict, "PRO", 'Mean_red', 29, 26, 6, diff,
color_checker_CC, pad, look_up_dict)
get__values(corrected_dict, "URO", 'Mean_Green', 2, 25, 24, diff,
color_checker_CC, pad, look_up_dict)
get__values(corrected_dict, "NIT", 'Mean_Green', 2, 28, 12, diff,
color_checker_CC, pad, look_up_dict)
get__values(corrected_dict, "LEU", 'Mean_red', 1, 30, 4, diff,
color_checker_CC, pad, look_up_dict)
json_string = json.dumps(corrected_dict)
return json_string
ust_deger = np.array([20, 255, 255]) #20 255 255
renk_filtresi_sonuc = cv2.inRange(kesilmis_kare_hsv, alt_deger, ust_deger)
renk_filtresi_sonuc = cv2.morphologyEx(renk_filtresi_sonuc,
cv2.MORPH_CLOSE, kernel)
sonuc = kesilmis_kare.copy()
cnts, hierarchy = cv2.findContours(renk_filtresi_sonuc, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)
max_genislik = 0
max_uzunluk = 0
max_index = -1
for t in range(len(cnts)):
cnt = cnts[t]
x, y, w, h = cv2.boundingRect(cnt)
if (w > max_genislik and h > max_uzunluk):
max_uzunluk = h
max_genislik = w
max_index = t
if (len(cnts) > 0):
x, y, w, h = cv2.boundingRect(cnts[max_index])
cv2.rectangle(sonuc, (x, y), (x + w, y + h), [0, 255, 0], 2)
el_resim = renk_filtresi_sonuc[y:y + h, x:x + w]
cv2.imshow("el resim", el_resim)
cv2.imshow("kare", kare)
cv2.imshow("kesilmiş", kesilmis_kare)
cv2.imshow("renk filtresi ", renk_filtresi_sonuc)
cv2.imshow("sonuc", sonuc)
def letter_seg(lines_img, x_lines, i):
copy_img = lines_img[i].copy()
x_linescopy = x_lines[i].copy()
letter_img = []
letter_k = []
chalu_img, contours, hierarchy = cv2.findContours(copy_img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
if cv2.contourArea(cnt) > 5:
x,y,w,h = cv2.boundingRect(cnt)
# letter_img.append(lines_img[i][y:y+h, x:x+w])
letter_k.append((x,y,w,h))
letter_width_sum = 0
count = 0
for cnt in contours:
if cv2.contourArea(cnt) > 20:
x, y, w, h = cv2.boundingRect(cnt)
letter_width_sum += h
count += 1
#mean_height = letter_width_sum/count
letter = sorted(letter_k, key=lambda student: student[0])
for e in range(len(letter)):
if e < len(letter)-1:
if abs(letter[e][0] - letter[e+1][0]) <= 2:
x,y,w,h = letter[e]
x2,y2,w2,h2 = letter[e+1]
if h >= h2:
letter[e] = (x,y2,w,h+h2)
letter.pop(e+1)
elif h < h2:
letter[e+1] = (x2,y,w2,h+h2)
letter.pop(e)
for e in range(len(letter)):
letter_img_tmp = lines_img[i][letter[e][1]-0:letter[e][1]+letter[e][3]+0,letter[e][0]-0:letter[e][0]+letter[e][2]+0]
letter_img_tmp = cv2.resize(letter_img_tmp, dsize=(28, 28), interpolation=cv2.INTER_AREA)
width = letter_img_tmp.shape[1]
height = letter_img_tmp.shape[0]
count_y = np.zeros(shape=(width))
for x in range(width):
for y in range(height):
if letter_img_tmp[y][x] == 255:
count_y[x] = count_y[x] +1
print(count_y)
max_list = []
for z in range(len(count_y)):
if z>=5 and z<= len(count_y)-6:
if max(count_y[z-5:z+6]) == count_y[z] and count_y[z] >= 2:
max_list.append(z)
elif z<5:
if max(count_y[0:z+6]) == count_y[z] and count_y[z] >= 2:
max_list.append(z)
elif z > len(count_y)-6:
if max(count_y[z-5:len(count_y)-1]) == count_y[z] and count_y[z] >= 2:
max_list.append(z)
print(max_list)
rem_list = []
final_max_list = []
for z in range(len(max_list)):
if z > 0:
if max_list[z]-max_list[z-1] <= 3:
rem_list.append(z-1)
for z in range(len(max_list)):
if z not in rem_list:
final_max_list.append(max_list[z])
print(final_max_list)
if len(final_max_list) <= 1:
print(False)
else:
max_len = len(final_max_list) - 1
for j in range(max_len):
list = count_y[final_max_list[j]:final_max_list[j+1]]
min_list = sorted(list)[:3]
avg = sum(min_list)/len(min_list)
print(avg)
x_linescopy.pop(0)
word = 1
letter_index = 0
for e in range(len(letter)):
#print(str(letter[e][0]) + ',' + str(letter[e][1]) + ',' + str(letter[e][2]) + ',' + str(letter[e][3]) + ',' + str(e))
if(letter[e][0]<x_linescopy[0]):
letter_index += 1
letter_img_tmp = lines_img[i][letter[e][1]-0:letter[e][1]+letter[e][3]+5,letter[e][0]-2:letter[e][0]+letter[e][2]+2]
letter_img = cv2.resize(letter_img_tmp, dsize =(28, 28), interpolation = cv2.INTER_AREA)
cv2.imwrite('./segmented_img/img1/'+str(i+1)+'_'+str(word)+'_'+str(letter_index)+'.jpg', 255-letter_img)
else:
x_linescopy.pop(0)
word += 1
letter_index = 1
letter_img_tmp = lines_img[i][letter[e][1]-0:letter[e][1]+letter[e][3]+5,letter[e][0]-2:letter[e][0]+letter[e][2]+2]
letter_img = cv2.resize(letter_img_tmp, dsize =(28, 28), interpolation = cv2.INTER_AREA)
cv2.imwrite('./segmented_img/img1/'+str(i+1)+'_'+str(word)+'_'+str(letter_index)+'.jpg', 255-letter_img)
def upload_file():
img_raw = parse_image(request.get_data())
nparr = np.fromstring(img_raw, np.uint8)
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5,5), 0)
edged = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 4)
#(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
_, cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted([(c, cv2.boundingRect(c)[0]) for c in cnts], key=lambda x: x[1])
math_detect = []
for (c, _) in cnts:
(x, y, w, h) = cv2.boundingRect(c)
if w >=5 and h>5:
roi = edged[y:y+int(1.2*h), x:x+w]
thresh = roi.copy()
thresh = deskew(thresh, 28)
thresh = center_extent(thresh, (28, 28))
thresh = np.reshape(thresh, (28, 28, 1))
thresh = thresh / 255
predictions = model.predict(np.expand_dims(thresh, axis=0))
digit = np.argmax(predictions[0])
cv2.rectangle(image, (x,y), (x+w, y+h), (0,255,0), 2)
cv2.putText(image, label_names[digit], (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 2, (0,255,0), 2)
if label_names[digit] == "1":
countt = 0
mem = []
for i in range(len(thresh[9])):
if thresh[9][i] > 0:
countt +=1
mem.append(i)
if countt >= 3 :
math_detect.append("1")
else:
math_detect.append("/")
else:
math_detect.append(label_names[digit])
def convert_math(math_detect):
for i in range(0, len(math_detect)):
if math_detect[i] == '10':
math_detect[i] = '*'
elif math_detect[i] == '11':
math_detect[i] = '-'
elif math_detect[i] == '12':
math_detect[i] = '+'
return math_detect
def calculate_string(math_detect):
math_detect = convert_math(math_detect)
calculator = ''.join(str(item) for item in math_detect)
result = calculator
return result
result = calculate_string(math_detect)
return result
def convert_object(file_path, screen_size=None, new_file_suffix="scanned"):
""" Identifies 4 corners and does four point transformation """
debug = True if log.level == logging.DEBUG else False
image = cv2.imread(str(file_path))
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(
gray, 11, 17, 17
) # 11 //TODO 11 FRO OFFLINE MAY NEED TO TUNE TO 5 FOR ONLINE
gray = cv2.medianBlur(gray, 5)
edged = cv2.Canny(gray, 30, 400)
if debug:
previewImage("Edged Image", edged)
# find contours in the edged image, keep only the largest
# ones, and initialize our screen contour
contours, hierarcy = cv2.findContours(
edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE
)
log.debug("Contours found: %s", len(contours))
# approximate the contour
ContourArea = namedtuple('ContourArea', ['curve', 'area'])
contourAreas = [ContourArea(curve=x, area=cv2.contourArea(x))
for x in contours]
contourAreas = sorted(contourAreas, key=attrgetter('area'))
if debug:
previewContours(image, [x.curve for x in contourAreas])
screens = [] # 4 point polygons, repressenting possible screens (rectangles)
for contour in contourAreas:
peri = cv2.arcLength(contour.curve, True)
polygon_less_vertices = cv2.approxPolyDP(contour.curve,
epsilon=0.02 * peri, # approximation accuracy
closed=True)
num_vertices = len(polygon_less_vertices)
if num_vertices == 4:
(x, y, width, height) = cv2.boundingRect(contour.curve)
log.debug(f'x={x} y={y} width={width} height={height}')
screens.append(Screen(fourpoints=polygon_less_vertices, width=width))
if debug:
log.debug(f"Screens found {len(screens)}: {screens}")
previewContours(image, [x.fourpoints for x in screens])
# find largest screen
largest_screen = max(screens, key=attrgetter('width'))
if debug:
previewContours(image, [largest_screen.fourpoints])
# now that we have our screen contour, we need to determine
# the top-left, top-right, bottom-right, and bottom-left
# points so that we can later warp the image -- we'll start
# by reshaping our contour to be our finals and initializing
# our output rectangle in top-left, top-right, bottom-right,
# and bottom-left order
pts = largest_screen.fourpoints.reshape(4, 2)
log.debug("Found bill rectagle at %s", pts)
rect = order_points(pts)
log.debug(rect)
warped = transform_to_four_points(image, pts)
# convert the warped image to grayscale and then adjust
# the intensity of the pixels to have minimum and maximum
# values of 0 and 255, respectively
warp = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
# Replacement for `skimage.exposure.rescale_intensity`
# Contrast Limited Adaptive Histogram Equalization
clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8))
warp = clahe.apply(warp)
# show the original and warped images
if debug:
previewImage("Original", image)
previewImage("warp", warp)
warp_file = str(file_path.parent / f"{file_path.stem}-{new_file_suffix}.jpg")
cv2.imwrite(warp_file, warp)
log.debug(f"Result: {warp_file}")
if screen_size:
return cv2.cvtColor(
cv2.resize(warp, screen_size), cv2.COLOR_GRAY2RGB
)
else:
return cv2.cvtColor(warp, cv2.COLOR_GRAY2RGB)
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
################# Now finding Contours ###################
img, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
samples = np.empty((0, 100))
responses = []
keys = [i for i in range(48, 58)]
for cnt in contours:
if cv2.contourArea(cnt) > 50:
[x, y, w, h] = cv2.boundingRect(cnt)
if h > 28:
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 0, 255), 2)
roi = thresh[y:y + h, x:x + w]
roismall = cv2.resize(roi, (10, 10))
cv2.imshow('norm', im)
key = cv2.waitKey(0)
if key == 27: # (escape to quit)
sys.exit()
elif key in keys:
responses.append(int(chr(key)))
sample = roismall.reshape((1, 100))
samples = np.append(samples, sample, 0)
def t_crop(crop_img, img):
grey = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)
# applying gaussian blur
value = (35, 35)
blurred = cv2.GaussianBlur(grey, value, 0)
# thresholdin: Otsu's Binarization method
_, thresh1 = cv2.threshold(blurred, 127, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
# show thresholded image
cv2.imshow('Thresholded', thresh1)
# check OpenCV version to avoid unpacking error
(version, _, _) = cv2.__version__.split('.')
if version == '3':
image, contours, hierarchy = cv2.findContours(thresh1.copy(), \
cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
elif version == '2':
contours, hierarchy = cv2.findContours(thresh1.copy(),cv2.RETR_TREE, \
cv2.CHAIN_APPROX_NONE)
# find contour with max area
cnt = max(contours, key=lambda x: cv2.contourArea(x))
# create bounding rectangle around the contour (can skip below two lines)
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(crop_img, (x, y), (x + w, y + h), (0, 0, 255), 0)
# finding convex hull
hull = cv2.convexHull(cnt)
# drawing contours
drawing = np.zeros(crop_img.shape, np.uint8)
cv2.drawContours(drawing, [cnt], 0, (0, 255, 0), 0)
cv2.drawContours(drawing, [hull], 0, (0, 0, 255), 0)
# finding convex hull
hull = cv2.convexHull(cnt, returnPoints=False)
# finding convexity defects
defects = cv2.convexityDefects(cnt, hull)
count_defects = 0
cv2.drawContours(thresh1, contours, -1, (0, 255, 0), 3)
# applying Cosine Rule to find angle for all defects (between fingers)
# with angle > 90 degrees and ignore defects
for i in range(defects.shape[0]):
s, e, f, d = defects[i, 0]
start = tuple(cnt[s][0])
end = tuple(cnt[e][0])
far = tuple(cnt[f][0])
if d > 9000:
count_defects += 1
# find length of all sides of triangle
'''a = math.sqrt((end[0] - start[0])**2 + (end[1] - start[1])**2)
b = math.sqrt((far[0] - start[0])**2 + (far[1] - start[1])**2)
c = math.sqrt((end[0] - far[0])**2 + (end[1] - far[1])**2)
# apply cosine rule here
angle = math.acos((b**2 + c**2 - a**2)/(2*b*c)) * 57
# ignore angles > 90 and highlight rest with red dots
if angle <= 90:
count_defects += 1
cv2.circle(crop_img, far, 1, [0,0,255], -1)
#dist = cv2.pointPolygonTest(cnt,far,True)
# draw a line from start to end i.e. the convex points (finger tips)
# (can skip this part)
cv2.line(crop_img,start, end, [0,255,0], 2)
cv2.circle(crop_img,far,5,[0,0,255],-1)'''
# define actions required
count_defects += 1
if count_defects == 2 or count_defects == 1:
#keyboard.press_and_release('enter')
pyautogui.press('enter')
else:
cv2.putText(img,"0", (50, 50),\
cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
ha = 0
#time.sleep(1)
# show appropriate images in windows
#cv2.imshow('Gesture', img)
all_img = np.hstack((drawing, crop_img))
#cv2.imshow('Contours', all_img)
print("thumbdown")
blue = cv2.dilate(blue, kernal)
res1 = cv2.bitwise_and(img, img, mask=blue)
yellow = cv2.dilate(yellow, kernal)
res2 = cv2.bitwise_and(img, img, mask=yellow)
#Tracking the Red Color
(_, contours, hierarchy) = cv2.findContours(red, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for pic, contour in enumerate(contours):
area = cv2.contourArea(contour)
if (area > 300):
x, y, w, h = cv2.boundingRect(contour)
img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv2.putText(img, "RED color", (x, y), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (0, 0, 255))
#Tracking the Blue Color
(_, contours, hierarchy) = cv2.findContours(blue, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for pic, contour in enumerate(contours):
area = cv2.contourArea(contour)
if (area > 300):
x, y, w, h = cv2.boundingRect(contour)
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.putText(img, "Blue color", (x, y), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 0, 0))
kernel =np.ones((5,5),np.uint8)
frame5 =cv2.dilate(frame4,kernel,iterations=4)
#identify contours on thershold images
contours,nada=cv2.findContours(frame5.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
frame6=frame0.copy()
targets=[]
for c in contours:
if cv2.contourArea(c)<500:
continue
#contour data
M=cv2.moments(c)
cx=int(M['m10']/M['m00'])
cy=int(M['m01']/M['m00'])
x,y,w,h=cv2.boundingRect(c)
rx=x+int(w/2)
ry=y+int(h/2)
ca=cv2.contourArea(c)
#plot contour
cv2.drawContours(frame6,[c],0,(0,0,255),2)
cv2.rectangle(frame6,(x,y),(x+w,y+h),(0,255,0),2)
cv2.circle(frame6,(cx,cy),2,(0,0,255),2)
cv2.circle(frame6,(rx,ry),2,(0,255,0),2)
#save contours
targets.append((rx,ry,ca))
area=sum([x[2] for x in targets])
mx=0
my=0
if targets:
# 그림의 가로세로 길이의 8분의 1 정도만 가장자리를 검게 바꿔준다.
cut_num = [(int)(closing.shape[0]/8), (int)(closing.shape[1]/8)]
print(cut_num)
closing[:cut_num[0], :] = 125
closing[-cut_num[0]:, :] = 125
closing[:, :cut_num[1]] = 125
closing[:, -cut_num[1]:] = 125
# closing = cv.morphologyEx(y2, cv.MORPH_CLOSE, kernel)
contours, _ = cv.findContours(closing[:, :, 0], cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
center=[]
for i in range(len(contours)):
cnt = contours[i]
area = cv.contourArea(cnt)
x, y, w, h = cv.boundingRect(cnt)
# by 김주희_contour 면적 _200702
rect_area = w * h
compare_area.append(rect_area)
# 컨투어 영역이 1인것 없애보기 -임시
# if rect_area == 1:
# closing[x, y, 0]
# by 김주희_가로 세로의 비율 _200702
aspect_ratio = float(w)/h
def detect(s):
cap = cv2.VideoCapture(s)
frame = cap.read()[1]
fshape = frame.shape
fheight = fshape[0]
fwidth = fshape[1]
fgbg = cv2.createBackgroundSubtractorMOG2()
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi', fourcc, 25, (fwidth, fheight))
half_width = fwidth / 2
count1 = 0
count2 = 0
start_time = time.time()
while True:
ret, frame = cap.read()
if not ret:
break
recent_time = time.time()
# Total count of vehicles for 2 min
if ((recent_time - start_time) > 120):
#TODO : send count and timestamp to webpage for plotting
count1 = 0
count2 = 0
start_time = recent_time
# print(frame.shape)
mask = fgbg.apply(frame)
kernel = np.ones((10, 10), np.uint8)
cv2.line(frame, (0, 250), (frame.shape[1], 250), (0, 255, 0), 2)
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
cnts = cv2.findContours(opening.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[1]
green = (0, 255, 0)
red = (0, 0, 255)
text = "Number of vehicles"
text1 = "Left side :" + str(count1)
text2 = "Right side :" + str(count2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, text, (20, 30), font, 0.8, (255, 255, 0), 2)
cv2.putText(frame, text1, (20, 60), font, 0.6, (255, 0, 255), 2)
cv2.putText(frame, text2, (380, 60), font, 0.6, (255, 0, 255), 2)
out.write(frame)
for cnt in cnts:
area = cv2.contourArea(cnt)
if area < 1000:
continue
area = cv2.contourArea(cnt)
(x, y, w, h) = cv2.boundingRect(cnt)
cv2.rectangle(frame, (x, y), (x + w, y + h), green, 3)
# cv2.putText(frame,str(area), (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
cnt_x = int(x + w / 2)
cnt_y = int(y + h / 2)
cv2.circle(frame, (cnt_x, cnt_y), 7, (255, 255, 255), -1)
#left side
if (cnt_y < 254 and cnt_y > 246 and cnt_x < half_width):
cv2.line(frame, (0, 250), (frame.shape[1], 250), red, 2)
count1 += 1
# winsound.Beep(2000,500)
#right side
if (cnt_y < 254 and cnt_y > 246 and cnt_x > half_width):
cv2.line(frame, (0, 250), (frame.shape[1], 250), red, 2)
count2 += 1
# winsound.Beep(2000,500)
# print(count)
cv2.imshow('frame', frame)
# cv2.imshow('opening',opening)
k = cv2.waitKey(1) & 0xFF
if k == ord('q'):
break
out.release()
cap.release()
cv2.destroyAllWindows()
return count1, count2
def character(counter, marker, distance):
print('Starting recognition thread')
guesses = [0] * 35 # create a list of 35 lists
for i in range(1, counter + 1):
try:
allContoursWithData = [] # declare empty lists
validContoursWithData = [] # we will fill these shortly
# set heights and width to be able to read the image when comparing to flatten images
h = 30
w = 30
img = cv2.imread("colour%d.png" % i)
height, width, numchannels = img.shape
roi = img[int((height / 2) -
(height / 2) * 0.85):int((height / 2) +
(height / 2) * 0.85),
int((width / 2) -
(width / 2) * 0.85):int((width / 2) +
(width / 2) * 0.85)]
resize = cv2.resize(roi, (100, 100))
# Convert the image to grayscale and turn to outline of the letter
gray = cv2.cvtColor(resize, cv2.COLOR_BGR2GRAY)
newheight, newwidth = gray.shape
# imgMaxContrastGrayscale = maximizeContrast(gray)
###########
gauss = cv2.GaussianBlur(gray, (5, 5), 0)
# thresh = cv2.adaptiveThreshold(gauss, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 0)
kernel = np.ones((4, 4), np.uint8)
# mask = cv2.inRange(gauss, 170, 255)
edged = cv2.Canny(
gauss, 10,
30) # the lower the value the more detailed it would be
dilate = cv2.dilate(edged, kernel, iterations=1)
kernel = np.ones((3, 3), np.uint8)
open = cv2.morphologyEx(dilate,
cv2.MORPH_OPEN,
kernel,
iterations=1)
close = cv2.morphologyEx(open,
cv2.MORPH_CLOSE,
kernel,
iterations=3)
dilation = cv2.dilate(close, kernel, iterations=4)
kernel = np.ones((4, 4), np.uint8)
erode = cv2.erode(dilation, kernel, iterations=4)
# open = cv2.morphologyEx(erode, cv2.MORPH_OPEN, kernel, iterations=1)
# Removes the noises on the grayscale image
denoised = cv2.fastNlMeansDenoising(erode, None, 10, 7, 21)
mask = cv2.inRange(gray, 100, 255) # works in lab, 100 at home,
# cv2.waitKey(0)
_, otsu = cv2.threshold(gauss, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# imgBlurred = cv2.GaussianBlur(gray, (5, 5), 0) # blur
if Step_letter:
cv2.imshow("mask", mask)
cv2.imshow("gg", denoised)
cv2.imshow("img", img)
cv2.imshow("gray", gray)
cv2.imshow("ed", edged)
cv2.imshow("dil", dilate)
cv2.imshow("otsu", otsu)
cv2.waitKey(0)
# Fill in the letter to detect the letter easily
# kernel = np.ones((4, 4), np.uint8)
# closing = cv2.morphologyEx(denoised, cv2.MORPH_CLOSE, kernel)
# dilation = cv2.dilate(closing, kernel, iterations=1)
knn = cv2.ml.KNearest_create() # initalise the knn
# joins the train data with the train_labels
knn.train(npaFlattenedImages, cv2.ml.ROW_SAMPLE,
npaClassifications)
# filter image from grayscale to black and white
imgThresh = cv2.adaptiveThreshold(
gauss, # input image
255, # make pixels that pass the threshold full white
cv2.
ADAPTIVE_THRESH_GAUSSIAN_C, # use gaussian rather than mean, seems to give better results
cv2.
THRESH_BINARY, # invert so foreground will be white, background will be black
11, # size of a pixel neighborhood used to calculate threshold value
0) # constant subtracted from the mean or weighted mean
newkernal = np.ones((3, 3), np.uint8)
opening = cv2.morphologyEx(imgThresh,
cv2.MORPH_OPEN,
newkernal,
iterations=1)
eroding = cv2.erode(opening, kernel, iterations=1)
dilating = cv2.dilate(eroding, kernel, iterations=1)
imgThreshCopy = otsu.copy(
) # make a copy of the thresh image, this in necessary b/c findContours modifies the image
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/otsu/{0}_{1}contour.png'
.format(marker, i), imgThreshCopy)
(npaContours, _) = cv2.findContours(
imgThreshCopy, # input image, make sure to use a copy since the function will modify this image in the course of finding contours
cv2.RETR_LIST, # retrieve the outermost contours only
cv2.CHAIN_APPROX_SIMPLE
) # compress horizontal, vertical, and diagonal segments and leave only their end points
if Step_letter:
cv2.imshow("npaContours", imgThreshCopy)
cv2.imshow("planb", imgThresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
for npaContour in npaContours: # for each contour
contourWithData = ContourWithData(
) # instantiate a contour with data object
contourWithData.npaContour = npaContour # assign contour to contour with data
contourWithData.boundingRect = cv2.boundingRect(
contourWithData.npaContour) # get the bounding rect
contourWithData.calculateRectTopLeftPointAndWidthAndHeight(
) # get bounding rect info
contourWithData.fltArea = cv2.contourArea(
contourWithData.npaContour) # calculate the contour area
allContoursWithData.append(
contourWithData
) # add contour with data object to list of all contours with data
# end for
for contourWithData in allContoursWithData: # for all contours
if contourWithData.checkIfContourIsValid(
newheight, newwidth): # check if valid
validContoursWithData.append(
contourWithData) # if so, append to valid contour list
# end if
# end for
validContoursWithData.sort(key=operator.attrgetter(
"intRectX")) # sort contours from left to right
validContoursWithData = removeInnerOverlappingChars(
validContoursWithData) # removes overlapping letters
for contourWithData in validContoursWithData: # for each contour
new = cv2.cvtColor(
cv2.rectangle(
roi, # draw rectangle on original testing image
(contourWithData.intRectX,
contourWithData.intRectY), # upper left corner
(contourWithData.intRectX +
contourWithData.intRectWidth,
contourWithData.intRectY +
contourWithData.intRectHeight),
# lower right corner
(0, 255, 0), # green
2),
cv2.COLOR_BGR2GRAY) # thickness
imgROI = otsu[contourWithData.intRectY +
1:contourWithData.intRectY +
contourWithData.intRectHeight -
1, # crop char out of threshold image
contourWithData.intRectX +
1:contourWithData.intRectX +
contourWithData.intRectWidth - 1]
imgROIResized = cv2.resize(
imgROI, (w, h)
) # resize image, this will be more consistent for recognition and storage
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/otsu/{0}_{1}chosen.png'
.format(marker, i), imgROIResized)
# for i in range(0, 360, 90):
# angle = i
# rotate = cv2.getRotationMatrix2D((w / 2, h / 2), angle, 1.0)
# imgROIResized = cv2.warpAffine(imgROIResized, rotate, (w, h))
npaROIResized = imgROIResized.reshape(
(1, w * h)) # flatten image into 1d numpy array
npaROIResized = np.float32(
npaROIResized
) # convert from 1d numpy array of ints to 1d numpy array of floats
if Step_letter:
cv2.imshow("resize", imgROIResized)
cv2.imshow(
"imgTestingNumbers", img
) # show input image with green boxes drawn around found digits
cv2.waitKey(0)
# end if
# looks for the 3 nearest neighbours comparing to the flatten images (k = neighbours)
retval, npaResults, neigh_resp, dists = knn.findNearest(
npaROIResized, k=1)
# current guess
gg = int(npaResults[0][0])
if Step_letter:
print(gg)
# Tranform guess in ASCII format into range 0-35
if 49 <= gg <= 57:
guesses[gg - 49] += 1
elif 65 <= gg <= 90:
guesses[gg - 56] += 1
except:
continue
# find modal character guess
# Initialise mode and prev variables for first loop through
if Step_letter:
print(guesses)
mode = 0
prev = guesses[0]
for j in range(35):
new = guesses[j]
if new > prev:
prev = guesses[j]
mode = j
# Transform back into ASCII
if 0 <= mode <= 8:
mode = mode + 49
elif 9 <= mode <= 34:
mode = mode + 56
return chr(mode)
def detection():
print('Starting detection')
# Initialising variable
counter = 0
marker = 1
positions = []
headings = []
centres = []
height_of_target = []
square = 2
# if Static_Test:
# cap = cv2.VideoCapture("TestData2.mp4") # video use
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 800) #800
cap.set(3, 800) #800
cap.set(4, 800) #800
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 800)
cap.set(cv2.CAP_PROP_FPS, 60)
time.sleep(2) # allows the camera to start-up
print('Camera on')
# Run detection when camera is turn on
while (cap.isOpened()): # for video use
# while True:
# the camera will keep running even after the if statement so it can detect multiple ground marker
if counter == 0 or start - end < 5:
if Static_Test:
distance = input("Distance it was taken")
# start - end < 5
if not Static_Test:
distance = 1
ret, frame = cap.read()
# Gathering data from Pixhawk
if GPS:
position = vehicle.location.global_relative_frame
heading = vehicle.heading
# end if
# starting the timer for the length of time it hasn't found a target
start = time.time()
# applying image processing
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # converts to gray
blurred = cv2.GaussianBlur(
gray, (5, 5),
0) # blur the gray image for better edge detection
edged = cv2.Canny(
blurred, 14,
10) # the lower the value the more detailed it would be
# find contours in the thresholded image and initialize the
(contours,
_) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE) # grabs contours
# outer square
for c in contours:
peri = cv2.arcLength(
c, True
) # grabs the contours of each points to complete a shape
# get the approx. points of the actual edges of the corners
approx = cv2.approxPolyDP(c, 0.01 * peri, True)
if 4 <= len(approx) <= 6:
(x, y, w, h) = cv2.boundingRect(
approx
) # gets the (x,y) of the top left of the square and the (w,h)
aspectRatio = w / float(
h) # gets the aspect ratio of the width to height
area = cv2.contourArea(
c) # grabs the area of the completed square
hullArea = cv2.contourArea(cv2.convexHull(c))
solidity = area / float(hullArea)
keepDims = w > 25 and h > 25
keepSolidity = solidity > 0.9 # to check if it's near to be an area of a square
keepAspectRatio = 0.6 <= aspectRatio <= 1.4
if keepDims and keepSolidity and keepAspectRatio: # checks if the values are true
# captures the region of interest with a 5 pixel lesser in all 2D directions
roi = frame[y:y + h, x:x + w]
height, width, numchannels = frame.shape
centre_region = (x + w / 2, y + h / 2)
if GPS:
centre_target = (y + h / 2, x + w / 2)
# grabs the angle for rotation to make the square level
angle = cv2.minAreaRect(approx)[
-1] # -1 is the angle the rectangle is at
if 0 == angle:
angle = angle
elif -45 > angle > 90:
angle = -(90 + angle)
elif -45 > angle:
angle = 90 + angle
else:
angle = angle
rotated = cv2.getRotationMatrix2D(
tuple(centre_region), angle, 1.0)
imgRotated = cv2.warpAffine(
frame, rotated,
(width, height)) # width and height was changed
imgCropped = cv2.getRectSubPix(imgRotated, (w, h),
tuple(centre_region))
HSVCropp = cv2.cvtColor(imgCropped, cv2.COLOR_BGR2HSV)
if square == 2:
color = imgCropped[int((h / 2) -
(h / 4)):int((h / 2) +
(h / 4)),
int((w / 2) -
(w / 4)):int((w / 2) +
(w / 4))]
else:
color = imgCropped
if Step_detection:
cv2.imshow("crop", imgCropped)
cv2.imshow("okay", color)
print(HSVCropp[int((h / 2) - (h * (6 / 10))),
int((w / 2) - (w * (6 / 10)))])
# # Convert the image to grayscale and turn to outline of the letter
# g_rotated = cv2.cvtColor(imgCropped, cv2.COLOR_BGR2GRAY)
# b_rotated = cv2.GaussianBlur(g_rotated, (5, 5), 0)
# e_rotated = cv2.Canny(b_rotated, 70, 20)
#
# # uses the outline to detect the corners for the cropping of the image
# (contours, _) = cv2.findContours(e_rotated.copy(), cv2.RETR_LIST,
# cv2.CHAIN_APPROX_SIMPLE)
#
# # inner square detection
# for cny in contours:
# perin = cv2.arcLength(cny, True)
# approxny = cv2.approxPolyDP(cny, 0.01 * perin, True)
# if 4 <= len(approxny) <= 6:
# (xx, yy), (ww, hh), angle = cv2.minAreaRect(approxny)
# aspectRatio = ww / float(hh)
# keepAspectRatio = 0.7 <= aspectRatio <= 1.3
# angle = cv2.minAreaRect(approxny)[-1]
# keep_angle = angle == 0, 90, 180, 270, 360
# if keepAspectRatio and keep_angle:
# (xxx, yyy, www, hhh) = cv2.boundingRect(approxny)
# color = imgCropped[yyy:yyy + hhh, xxx:xxx + www]
# appends the data of the image to the list
if GPS:
positions.append(
[position.lat, position.lon, position.alt])
headings.append(heading)
centres.append(centre_target)
height_of_target.append(h)
# time that the target has been last seen
end = time.time()
time.sleep(0.5)
# keep count of number of saved images
counter = counter + 1
cv2.imwrite("colour%d.png" % counter, color)
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/results/{0}_{1}.png'
.format(marker, counter), color)
print("Detected and saved a target")
if Static_Test:
# testing purposes
if not os.path.exists(distance):
os.makedirs(distance)
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/{0}/results{1}_{2}.png'
.format(distance, marker, counter), color)
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/{0}/captured{1}_{2}.png'
.format(distance, marker, counter), roi)
cv2.imwrite(
'C:/Users/kevin/Desktop/2018-2019/method A/{0}/orginal{1}_{2}.png'
.format(distance, marker, counter), frame)
else:
distance = 0
if Step_detection:
cv2.imshow("roi", roi)
cv2.imshow("cropped", imgCropped)
cv2.waitKey(0)
# end if
if counter == 7:
counter, marker = solution(counter, marker,
distance)
else:
counter, marker = solution(counter, marker, distance)
if Step_camera:
cv2.imshow('frame', frame)
cv2.imshow('edge', edged)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
# end if
cap.release()
cv2.destroyAllWindows()
def recognizeDigits(filename, currentBarcodeNum): global indexTask3 global recognizedNumber #Load my predictive model samples, responses = loadPredictiveModelSamples() #Train my data using KNearest model = initKNN(samples, responses) #For each digit in file for img in filename: indexTask3 = indexTask3 + 1 #Read digit image im = cv2.imread(img,1) #Pre-process image thresh = imageProccessing(im) #Find contours for each digtis contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) #For each countours for cnt in contours: #If the area is greater than 50 if cv2.contourArea(cnt)>50: [x,y,w,h] = cv2.boundingRect(cnt) #Set some constraints to avoid detecting non digits if h>30 and w>10 and abs(w-h) > 10: #Resize the digit into 10x10 and make it into float points for KNN roi = thresh[y:y+h,x:x+w] roismall = cv2.resize(roi,(10,10)) roismall = roismall.reshape((1,100)) roismall = np.float32(roismall) #Find nearest similar digit from sample using k = 1 retval, results, neigh_resp, dists = model.find_nearest(roismall, k = 1) #String recognised string = str(int((results[0][0]))) #Assign recognizedNumber to String recognised recognizedNumber = string #Save recognizedNumber to text file saveTxtFile(currentBarcodeNum) #Append each recognizedNumber to an array for showing the result on terminal outputArray.append(recognizedNumber) #Reset recognizedNumber recognizedNumber = "0" combineNumberIntoFile(outputArray, currentBarcodeNum)
def image(img,typecolor):
trouve=False
imgcopy=cv2.GaussianBlur(img, (3,3), 0)
abcd=[]
numbers= []
for sss in numbers:
del(sss)
imgcopy2=img.copy()
if (typecolor==1):
imgcopy2=cv2.cvtColor(imgcopy2,cv2.COLOR_BGR2GRAY)
elif(typecolor==2):
imgcopy2 = cv2.cvtColor(imgcopy2,cv2.COLOR_BGR2YCrCb)
imgcopy2 = imgcopy2[:,:,0]
imgThresh = cv2.Canny(img, 200, 255)
imgThreshCopy = imgThresh.copy()
imgThreshCopy3=imgThreshCopy.copy()
npaContours, npaHierarchy = cv2.findContours(imgThreshCopy,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for number in npaContours:
[intX, intY, intWidth, intHeight] = cv2.boundingRect(number)
fltArea = cv2.contourArea(number)
ratio=(intWidth)/(intHeight)
if (ratio > MIN_RATIO) & (ratio < MAX_RATIO):
if (fltArea > MIN_CONTOUR_AREA) & (fltArea < MAX_CONTOUR_AREA):
numbers.append([intX, intY, intWidth, intHeight])
numbers = sorted(numbers, key=lambda selule:selule[0] , reverse = False)
for nmr in numbers:
conteurState=0
conteurI=nmr[0]
conteurJ=nmr[1]
while(conteurI<=nmr[2]+nmr[0]):
while(conteurJ<=nmr[3]+nmr[1]):
if ((imgcopy2[conteurJ,conteurI] < 225) & (imgcopy2[conteurJ,conteurI] > 40)):
conteurState+=1
conteurJ+=1
conteurI+=1
if ((conteurState *100) /( nmr[2]*nmr[3]) > 20 ):
del(nmr)
abcd = sorted(numbers, key=lambda selule:selule[1] , reverse = False)
cont1 =0
cont2 =-1
prec=0
state=False
for nmr in abcd:
if state==False:
if cont2==-1:
prec=nmr[1]
cont1+=1
cont2=0
else:
if (abs (nmr[1] - prec ) > 5):
cont2+=cont1
cont1=1
else:
cont1+=1
if cont1 ==10 :
state=True
prec=nmr[1]
else:
del(abcd[cont2])
for iii in range(0,cont2):
del (abcd[0])
if state==True:
trouve=True
abcd = sorted(abcd, key=lambda selule:selule[0] , reverse = False)
for aaaa in abcd:
image_implimentation=imgThresh.copy()
image_implimentation=cv2.cvtColor(image_implimentation,cv2.COLOR_GRAY2BGR)
cv2.rectangle(image_implimentation
,(aaaa [0] -2, aaaa [1] -2)
,(aaaa [2]+aaaa [0] +2, aaaa [3]+aaaa [1]+2)
, (0,0,255)
, 2)
elif state==False:
for kk in numbers:
del(kk)
for ss in npaContours:
del(ss)
for jj in abcd :
del(jj)
bnbn=imgThreshCopy.copy()
npaContours2, npaHierarchy2 = cv2.findContours(imgThreshCopy,
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for nmrr in npaContours2:
[intX, intY, intWidth, intHeight] = cv2.boundingRect(nmrr)
fltArea = cv2.contourArea(nmrr)
ratio=(intWidth)/(intHeight)
if (ratio > MIN_RATIO_PLAQUE) & (ratio < MAX_RATIO_PLAQUE) :
if (fltArea > MIN_CONTOUR_AREA_PLAQUE) & (fltArea < MAX_CONTOUR_AREA_PLAQUE):
intXX=intX
intYY=intY
intWidthX=intX+intWidth
intHeightY=intY+intHeight
imgThreshCopy2 = imgThresh.copy()
npaContours5, npaHierarchy = cv2.findContours(imgThreshCopy2,
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for number in npaContours5:
[intX, intY, intWidth, intHeight] = cv2.boundingRect(number)
if (intX>=intXX and intX+intWidth<=intWidthX)and(intY>=intYY and intY+intHeight<=intHeightY):
fltArea = cv2.contourArea(number)
ratio=(intWidth)/(intHeight)
if (fltArea > MIN_CONTOUR_AREA) & (fltArea < MAX_CONTOUR_AREA):
if (ratio > MIN_RATIO) & (ratio < MAX_RATIO) :
numbers.append([intX, intY, intWidth, intHeight])
numbers = sorted(numbers, key=lambda selule:selule[0] , reverse = False)
for nmr in numbers:
conteurState=0
conteurI=nmr[0]
conteurJ=nmr[1]
while(conteurI<nmr[2]):
while(conteurJ<nmr[3]):
if ((imgcopy[conteurI,conteurJ] < 230) and (imgcopy[conteurI,conteurJ] > 25)):
conteurState+=1
if ((conteurState *100) /( nmr[2]*nmr[3]) > 20 ):
del(nmr)
abcd = sorted(numbers, key=lambda selule:selule[1] , reverse = False)
cont1 =0
cont2 =0
prec=0
state=False
for nmr5 in abcd:
if state==False:
if cont1==0:
prec=nmr5[1]
cont1+=1
else:
if (abs (nmr5[1] - prec ) > 5):
cont2+=cont1
cont1=0
else:
cont1+=1
if cont1 ==10 :
state=True
prec=nmr5[1]
else:
del(nmr5)
end = []
for i in abcd:
if i not in end:
end.append(i)
for i in abcd:
del(i)
abcd = sorted(end, key=lambda selule:selule[0] , reverse = False)
for kkk in abcd:
[intX, intY, intWidth, intHeight]=kkk
cv2.destroyAllWindows()
trouve=state
return [trouve,abcd]
def largest_contours_rect(saliency):
contours, hierarchy = cv2.findContours(saliency * 1, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea)
return cv2.boundingRect(contours[-1])
def main():
cam = cv2.VideoCapture("video.mp4")
cv2.namedWindow(WINDOW_NAME)
img_counter = 0
while True:
ret, frame = cam.read()
if not ret:
print("failed to grab frame")
break
global img
cv2.setMouseCallback(WINDOW_NAME, capture_click)
oldX,oldY,oldW,oldH = -1,-1,-1,-1
global lowers,uppers
# Blurring
blur = cv2.blur(frame,(1,1))
blur0=cv2.medianBlur(blur,5)
blur1= cv2.GaussianBlur(blur0,(1,1),0)
blur2= cv2.bilateralFilter(blur1,9,200,200)
# Sharping
sharp=cv2.addWeighted(frame,3,blur2,-2,0)
# Erosion
kernel = np.ones((1,1),np.uint8)
sharp = cv2.erode(sharp,kernel,iterations = 1)
img = sharp
if(len(lowers) > 0):
curLow = lowers[0]
curUpp = uppers[0]
kernel = np.ones((1,1),np.uint8)
sharp = cv2.erode(sharp,kernel,iterations = 1)
cv2.imshow("er",sharp)
# Create the mask
mask = cv2.inRange(sharp,curLow,curUpp)
cv2.imshow("mask",mask)
# Find the contours
contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
if len(contours)>0:
# Sort the contours
cont_sort = sorted(contours, key=cv2.contourArea, reverse=True)
area = max(cont_sort, key=cv2.contourArea)
(xg,yg,wg,hg) = cv2.boundingRect(area)
cv2.rectangle(frame,(xg,yg),(xg+wg, yg+hg),(69,69,255),2)
cv2.imshow(WINDOW_NAME, frame)
cv2.imshow(WINDOW_NAME, frame)
k = cv2.waitKey(1)
if k%256 == 27:
# ESC pressed
print("Escape hit, closing...")
break
elif k%256 == 32:
# SPACE pressed
img_name = "opencv_frame_{}.png".format(img_counter)
cv2.imwrite(img_name, frame)
print("{} written!".format(img_name))
img_counter += 1
cam.release()
cv2.destroyAllWindows()
def draw_contours(image, contours, color):
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
def runDetector(angle, image): #First pre-process the image image = processImage(angle, image) #Make it into gray scale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #Find the greatest gradient from the gray scale image closed = featureDetection(gray) #Find the contours in the thresholded image, then sort the contours, keeping only the largest one area #This area should be the barcode area (cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) c = sorted(cnts, key = cv2.contourArea, reverse = True)[0] #Get a rectangle for the detected area rect = cv2.minAreaRect(c) box = np.int0(cv2.cv.BoxPoints(rect)) #Points of the reactangle x,y,w,h = cv2.boundingRect(c) #Extracted area called ori ori = extractBarcodeArea(x,y,w,h,image) #Save the the points x and y from the rectangle for later use savedX = x savedY = y #Process the extracted area make it call roi (Region of Interest) roi = cv2.cvtColor(ori, cv2.COLOR_BGR2GRAY) #refine roi with closing and eroding to get the barcode area more precisely closing = processROI(roi) #Again find the countours for roi (cnts, _) = cv2.findContours(closing.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) c = sorted(cnts, key = cv2.contourArea, reverse = True)[0] #Get a rectangle for the detected area in roi rect = cv2.minAreaRect(c) box2 = np.int0(cv2.cv.BoxPoints(rect)) #Draw contours on the detected area cv2.drawContours(roi, [box2], -1, (0, 255, 0), 2) #Copy roi for furthur processing copyOfROI = roi.copy() #Points of rectangle from roi x,y,w,h = cv2.boundingRect(c) #Crop roi to only the numbers using percantage croppedArea, cropped_h = cropImagePercentage(0.18, roi, x, y, h , w) #Before cropping the barcode numbers, check make sure the image has enough size to be cropped if ( x+savedX-10 ) <= 0: final = image[y+cropped_h+savedY:y+h+savedY, x+savedX:x+w+savedX] else: final = image[y+cropped_h+savedY:y+h+savedY+5, x+savedX-10:x+w+savedX] #Saves the barcode number to directory saveFinalImage(final)
def main():
cv2.namedWindow('Gamma Correction',cv2.WINDOW_NORMAL)
cv2.namedWindow('Correction',cv2.WINDOW_NORMAL)
cv2.namedWindow('Auto White Balance',cv2.WINDOW_NORMAL)
cv2.namedWindow('Clahe',cv2.WINDOW_NORMAL)
cv2.namedWindow('Clahe LAB',cv2.WINDOW_NORMAL)
cv2.namedWindow('Gaussian',cv2.WINDOW_NORMAL)
cv2.namedWindow('Anisotropic Diffusion',cv2.WINDOW_NORMAL)
cv2.namedWindow('Median Blur',cv2.WINDOW_NORMAL)
cv2.namedWindow('Median Blur 2',cv2.WINDOW_NORMAL)
cv2.namedWindow('YCrCb Median Blur',cv2.WINDOW_NORMAL)
cv2.namedWindow('Output',cv2.WINDOW_NORMAL)
cv2.namedWindow('Thresh Output',cv2.WINDOW_NORMAL)
cv2.namedWindow('Video',cv2.WINDOW_NORMAL)
cv2.resizeWindow('Video',500,500)
cv2.resizeWindow('Clahe',500,500)
cv2.resizeWindow('Clahe LAB',500,500)
cv2.resizeWindow('Gaussian',500,500)
cv2.resizeWindow('Anisotropic Diffusion',500,500)
cv2.resizeWindow('Median Blur',500,500)
cv2.resizeWindow('Median Blur 2',500,500)
cv2.resizeWindow('YCrCb Median Blur',500,500)
cv2.resizeWindow('Output',500,500)
cv2.resizeWindow('Thresh Output',500,500)
imgpath = "/home/charmi/Desktop/Trident_Work/OpenCV/Output/SavedVideos2/Output15.avi"
'''
a ='/home/charmi/Desktop/Trident_Work/OpenCV/Output/SavedVideos/OutputThresh'
b =1
c = '.avi'
filename = a+str(b)+c
#d ='/home/charmi/Desktop/Trident_Work/OpenCV/Output/SavedVideos/Output'
#filename2=d+str(b)+c
while(os.path.exists(filename)):
b+=1
filename = a+str(b)+c
#filename2=d+str(b)+c
codec = cv2.VideoWriter_fourcc('W', 'M', 'V', '2')
framerate = 10
resolution = (640, 480)
VideoFileOutput = cv2.VideoWriter(filename, codec, framerate, resolution)
#VideoFileOutput2 = cv2.VideoWriter(filename2, codec, framerate, resolution)
'''
cv2.createTrackbar('+ve Gamma','Gamma Correction',1,20,emptyFunction)
cv2.createTrackbar('-ve Gamma','Gamma Correction',0,20,emptyFunction)
cv2.createTrackbar('Hue','Correction',1,200,emptyFunction)
cv2.createTrackbar('Saturation','Correction',1,200,emptyFunction)
cv2.createTrackbar('Value','Correction',1,200,emptyFunction)
cv2.createTrackbar('Low H','Thresh Output',0,180,emptyFunction)
cv2.createTrackbar('Low S','Thresh Output',0,255,emptyFunction)
cv2.createTrackbar('Low V','Thresh Output',0,255,emptyFunction)
cv2.createTrackbar('High H','Thresh Output',0,360,emptyFunction)
cv2.createTrackbar('High S','Thresh Output',0,255,emptyFunction)
cv2.createTrackbar('High V','Thresh Output',0,255,emptyFunction)
cv2.createTrackbar('Kernel Size','YCrCb Median Blur',3,15,emptyFunction)
cv2.createTrackbar('Laplacian ksize','Output',3,15,emptyFunction)
cv2.createTrackbar('Clip Limit (Blue)','Clahe',0,100,emptyFunction)
cv2.createTrackbar('Tile Grid Size (Blue)','Clahe',1,20,emptyFunction)
cv2.createTrackbar('Clip Limit (Green)','Clahe',0,100,emptyFunction)
cv2.createTrackbar('Tile Grid Size (Green)','Clahe',1,20,emptyFunction)
cv2.createTrackbar('Clip Limit (Red)','Clahe',0,100,emptyFunction)
cv2.createTrackbar('Tile Grid Size (Red)','Clahe',1,20,emptyFunction)
cv2.createTrackbar('Pair','Clahe',0,1,emptyFunction)
cv2.createTrackbar('Kernel Size','Gaussian',3,15,emptyFunction)
cv2.createTrackbar('Standard Deviation','Gaussian',0,50,emptyFunction)
cv2.createTrackbar('+ve Alpha','Gaussian',0,20,emptyFunction)
cv2.createTrackbar('+ve Beta','Gaussian',0,20,emptyFunction)
cv2.createTrackbar('-ve Alpha','Gaussian',0,20,emptyFunction)
cv2.createTrackbar('-ve Beta','Gaussian',0,20,emptyFunction)
cv2.createTrackbar('Alpha','Anisotropic Diffusion',0,10,emptyFunction)
cv2.createTrackbar('Sensitivity','Anisotropic Diffusion',0,500,emptyFunction)
cv2.createTrackbar('Iterations','Anisotropic Diffusion',1,500,emptyFunction)
cv2.createTrackbar('Kernel','Median Blur',3,15,emptyFunction)
cv2.createTrackbar('Kernel','Median Blur 2',3,25,emptyFunction)
cv2.createTrackbar('FGauss','Gaussian',0,1,emptyFunction)
cv2.createTrackbar('FGamma','Gamma Correction',0,1,emptyFunction)
cv2.createTrackbar('FAWBal','Auto White Balance',0,1,emptyFunction)
cv2.createTrackbar('FAni','Anisotropic Diffusion',0,1,emptyFunction)
cv2.createTrackbar('FB','Median Blur',0,1,emptyFunction)
cv2.createTrackbar('FG','Median Blur',0,1,emptyFunction)
cv2.createTrackbar('FR','Median Blur',0,1,emptyFunction)
cv2.createTrackbar('FMed','Median Blur 2',0,1,emptyFunction)
cv2.createTrackbar('FL','Clahe LAB',0,1,emptyFunction)
cv2.createTrackbar('FA','Clahe LAB',0,1,emptyFunction)
cv2.createTrackbar('FB','Clahe LAB',0,1,emptyFunction)
cv2.createTrackbar('FB','Clahe',0,1,emptyFunction)
cv2.createTrackbar('FG','Clahe',0,1,emptyFunction)
cv2.createTrackbar('FR','Clahe',0,1,emptyFunction)
cv2.createTrackbar('FL','Output',0,1,emptyFunction)
cv2.createTrackbar('FM','YCrCb Median Blur',0,1,emptyFunction)
cap = cv2.VideoCapture(imgpath)
ret, image=cap.read()
(h, w, c) = image.shape
cv2.circle(image, (w//2, h//2), 7, (255, 255, 255), -1)
width2 = float(w)/2
cv2.setTrackbarPos('+ve Gamma','Gamma Correction',16)
cv2.setTrackbarPos('-ve Gamma','Gamma Correction',0)
cv2.setTrackbarPos('Hue','Correction',100)
cv2.setTrackbarPos('Saturation','Correction',100)
cv2.setTrackbarPos('Value','Correction',100)
cv2.setTrackbarPos('Low H','Thresh Output',8)
cv2.setTrackbarPos('Low S','Thresh Output',149)
cv2.setTrackbarPos('Low V','Thresh Output',170)
cv2.setTrackbarPos('High H','Thresh Output',34)
cv2.setTrackbarPos('High S','Thresh Output',255)
cv2.setTrackbarPos('High V','Thresh Output',255)
cv2.setTrackbarPos('Kernel Size','YCrCb Median Blur',3)
cv2.setTrackbarPos('Laplacian ksize','Output',3)
cv2.setTrackbarPos('Clip Limit (Blue)','Clahe',0)
cv2.setTrackbarPos('Tile Grid Size (Blue)','Clahe',4)
cv2.setTrackbarPos('Clip Limit (Green)','Clahe',0)
cv2.setTrackbarPos('Tile Grid Size (Green)','Clahe',4)
cv2.setTrackbarPos('Clip Limit (Red)','Clahe',0)
cv2.setTrackbarPos('Tile Grid Size (Red)','Clahe',4)
cv2.setTrackbarPos('Pair','Clahe',1)
cv2.setTrackbarPos('Kernel Size','Gaussian',3)
cv2.setTrackbarPos('Standard Deviation','Gaussian',5)
cv2.setTrackbarPos('+ve Alpha','Gaussian',11)
cv2.setTrackbarPos('+ve Beta','Gaussian',2)
cv2.setTrackbarPos('-ve Alpha','Gaussian',2)
cv2.setTrackbarPos('-ve Beta','Gaussian',0)
cv2.setTrackbarPos('Alpha','Anisotropic Diffusion',1)
cv2.setTrackbarPos('Sensitivity','Anisotropic Diffusion',20)
cv2.setTrackbarPos('Iterations','Anisotropic Diffusion',2)
cv2.setTrackbarPos('Kernel','Median Blur',5)
cv2.setTrackbarPos('Kernel','Median Blur 2',3)
cv2.setTrackbarPos('FGauss','Gaussian',0)
cv2.setTrackbarPos('FGamma','Gamma Correction',1)
cv2.setTrackbarPos('FAWBal','Auto White Balance',0)
cv2.setTrackbarPos('FAni','Anisotropic Diffusion',1)
cv2.setTrackbarPos('FB','Median Blur',1)
cv2.setTrackbarPos('FG','Median Blur',1)
cv2.setTrackbarPos('FR','Median Blur',1)
cv2.setTrackbarPos('FMed','Median Blur 2',0)
cv2.setTrackbarPos('FL','Clahe LAB',0)
cv2.setTrackbarPos('FA','Clahe LAB',0)
cv2.setTrackbarPos('FB','Clahe LAB',0)
cv2.setTrackbarPos('FB','Clahe',1)
cv2.setTrackbarPos('FG','Clahe',1)
cv2.setTrackbarPos('FR','Clahe',1)
cv2.setTrackbarPos('FL','Output',0)
cv2.setTrackbarPos('FM','YCrCb Median Blur',0)
ret = True
flag=1
xdiff=0
txdiff=0
cX=0
cY=0
maxArea=0
while (1):
maxArea=0
if flag==1:
ret,img = cap.read()
fgs = cv2.getTrackbarPos('FGauss','Gaussian')
fgm = cv2.getTrackbarPos('FGamma','Gamma Correction')
fab = cv2.getTrackbarPos('FAWBal', 'Auto White Balance')
fad = cv2.getTrackbarPos('FAni','Anisotropic Diffusion')
fmb = cv2.getTrackbarPos('FB','Median Blur')
fmg = cv2.getTrackbarPos('FG','Median Blur')
fmr = cv2.getTrackbarPos('FR','Median Blur')
fmed = cv2.getTrackbarPos('FMed','Median Blur 2')
fcll = cv2.getTrackbarPos('FL','Clahe LAB')
fcla = cv2.getTrackbarPos('FA','Clahe LAB')
fclb = cv2.getTrackbarPos('FB','Clahe LAB')
fcb = cv2.getTrackbarPos('FB','Clahe')
fcg = cv2.getTrackbarPos('FG','Clahe')
fcr = cv2.getTrackbarPos('FR','Clahe')
fyl = cv2.getTrackbarPos('FL','Output')
fym = cv2.getTrackbarPos('FM','YCrCb Median Blur')
hl = cv2.getTrackbarPos('Low H','Thresh Output')
hh = cv2.getTrackbarPos('High H','Thresh Output')
sl = cv2.getTrackbarPos('Low S','Thresh Output')
sh = cv2.getTrackbarPos('High S','Thresh Output')
vl = cv2.getTrackbarPos('Low V','Thresh Output')
vh = cv2.getTrackbarPos('High V','Thresh Output')
clipLim1=(cv2.getTrackbarPos('Clip Limit (Blue)','Clahe'))
clipLim1=float(clipLim1)/1000
tgs1=cv2.getTrackbarPos('Tile Grid Size (Blue)','Clahe')
clipLim2=(cv2.getTrackbarPos('Clip Limit (Green)','Clahe'))
clipLim2=float(clipLim2)/1000
tgs2=cv2.getTrackbarPos('Tile Grid Size (Green)','Clahe')
clipLim3=(cv2.getTrackbarPos('Clip Limit (Red)','Clahe'))
clipLim3=float(clipLim3)/1000
tgs3=cv2.getTrackbarPos('Tile Grid Size (Red)','Clahe')
pgamma=cv2.getTrackbarPos('+ve Gamma','Gamma Correction')
ngamma=cv2.getTrackbarPos('-ve Gamma','Gamma Correction')
hc=cv2.getTrackbarPos('Hue','Correction')
sc=cv2.getTrackbarPos('Saturation','Correction')
vc=cv2.getTrackbarPos('Value','Correction')
pgamma=float(pgamma)/10
ngamma=float(ngamma)/10
hc=float(hc)/100
sc=float(sc)/100
vc=float(vc)/100
ks=cv2.getTrackbarPos('Kernel Size','Gaussian')
sd=(cv2.getTrackbarPos('Standard Deviation','Gaussian'))
sd=float(sd)/10
alpha=(cv2.getTrackbarPos('+ve Alpha','Gaussian'))
beta=(cv2.getTrackbarPos('+ve Beta','Gaussian'))
nalpha=(cv2.getTrackbarPos('-ve Alpha','Gaussian'))
nbeta=(cv2.getTrackbarPos('-ve Beta','Gaussian'))
alpha=float(alpha)/10
beta=float(beta)/10
nalpha=float(nalpha)/10
nbeta=float(nbeta)/10
alph=(cv2.getTrackbarPos('Alpha','Anisotropic Diffusion'))
alph=float(alph)/10
sens=cv2.getTrackbarPos('Alpha','Anisotropic Diffusion')
itern=cv2.getTrackbarPos('Alpha','Anisotropic Diffusion')
mker=cv2.getTrackbarPos('Kernel','Median Blur')
medker=cv2.getTrackbarPos('Kernel','Median Blur 2')
ymker=cv2.getTrackbarPos('Kernel Size','YCrCb Median Blur')
ylksize=cv2.getTrackbarPos('Laplacian ksize','Output')
if ks%2==0:
ks+=1
if mker%2==0:
mker+=1
if medker%2==0:
medker+=1
if ymker%2==0:
ymker+=1
if ylksize%2==0:
ylksize+=1
fg=cv2.getTrackbarPos('Pair','Clahe')
if ret:
if cv2.waitKey(2) == 27:
break
if cv2.waitKey(2) == 97:
flag = 1
if cv2.waitKey(2) == 32:
flag=0
low = np.array([hl,sl,vl])
high = np.array([hh,sh,vh])
#Gamma Correction
t1=time.time()
gc=adjust_gamma(img,pgamma-ngamma)
t2=time.time()-t1
#print(t2)
cv2.imshow('Gamma Correction',gc)
if(fgm==0):
gc=img
#Gaussian
gaussian = cv2.GaussianBlur(gc ,(ks,ks), sd)
gauss = cv2.addWeighted(img, alpha-nalpha, gaussian, beta-nbeta, 0)
cv2.imshow('Gaussian',gauss)
if(fgs==0):
gauss=gc
#Correcting HSV Values
st1=cv2.cvtColor(gauss,cv2.COLOR_BGR2HSV)
st1[:, :, 0]=st1[:, :, 0]*hc
st1[:, :, 1]=st1[:, :, 1]*sc
st1[:, :, 2]=st1[:, :, 2]*vc
st3=cv2.cvtColor(st1,cv2.COLOR_HSV2BGR)
cv2.imshow('Correction',st3)
#Auto White Balance
result1 = cv2.cvtColor(st3, cv2.COLOR_BGR2LAB)
avg_a = np.average(result1[:, :, 1])
avg_b = np.average(result1[:, :, 2])
result1[:, :, 1] = result1[:, :, 1] - ((avg_a - 128) * (result1[:, :, 0] / 255.0) * 1.5)
result1[:, :, 2] = result1[:, :, 2] - ((avg_b - 128) * (result1[:, :, 0] / 255.0) * 1.5)
result1 = cv2.cvtColor(result1, cv2.COLOR_LAB2BGR)
cv2.imshow('Auto White Balance',result1)
if (fab==0):
result1=st3
#Anisotropic Diffusion
adf = cv2.ximgproc.anisotropicDiffusion(result1, alph, sens, itern)
cv2.imshow('Anisotropic Diffusion',adf)
if(fad==0):
adf=result1
#Median Blur
b, g, r = cv2.split(adf)
b1 = cv2.medianBlur(b,mker)
if(fmb==0):
b1=b
g1 = cv2.medianBlur(g,mker)
if(fmg==0):
g1=g
r1 = cv2.medianBlur(r,mker)
if(fmr==0):
r1=r
medfil = cv2.merge((b1, g1, r1))
cv2.imshow('Median Blur',medfil)
#Clahe LAB
clahe1 = cv2.createCLAHE(clipLimit=clipLim1,tileGridSize=(tgs1,tgs1))
clahe2 = cv2.createCLAHE(clipLimit=clipLim2,tileGridSize=(tgs2,tgs2))
clahe3 = cv2.createCLAHE(clipLimit=clipLim3,tileGridSize=(tgs3,tgs3))
lab=cv2.cvtColor(medfil,cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
l1 = clahe1.apply(l)
if(fcll==0):
l1=l
a1 = clahe2.apply(a)
if(fcla==0):
a1=a
b1 = clahe3.apply(b)
if(fclb==0):
b1=b
cmer=cv2.merge((l1,a1,b1))
cllab=cv2.cvtColor(cmer,cv2.COLOR_LAB2BGR)
cv2.imshow('Clahe LAB',cllab)
#Clahe BGR
if(fg==1):
cv2.setTrackbarPos('Clip Limit (Green)','Clahe',int(clipLim1*1000))
cv2.setTrackbarPos('Tile Grid Size (Green)','Clahe',tgs1)
cv2.setTrackbarPos('Clip Limit (Red)','Clahe',int(clipLim1*1000))
cv2.setTrackbarPos('Tile Grid Size (Red)','Clahe',tgs1)
clahe1 = cv2.createCLAHE(clipLimit=clipLim1,tileGridSize=(tgs1,tgs1))
clahe2 = cv2.createCLAHE(clipLimit=clipLim2,tileGridSize=(tgs2,tgs2))
clahe3 = cv2.createCLAHE(clipLimit=clipLim3,tileGridSize=(tgs3,tgs3))
b, g, r = cv2.split(cllab)
b1 = clahe1.apply(b)
if(fcb==0):
b1=b
g1 = clahe2.apply(g)
if(fcg==0):
g1=g
r1 = clahe3.apply(r)
if(fcr==0):
r1=r
cl=cv2.merge((b1,g1,r1))
cv2.imshow('Clahe',cl)
#Median Blur
medblur = cv2.medianBlur(cl,medker)
cv2.imshow('Median Blur 2',medblur)
if(fmed==0):
medblur=cl
#YCrCb Laplacian
ycrcb=cv2.cvtColor(medblur, cv2.COLOR_BGR2YCR_CB)
y,cr,cb=cv2.split(ycrcb)
dst = cv2.Laplacian( y, cv2.CV_16S, ksize=ylksize)
absDst = cv2.convertScaleAbs( dst )
out=cv2.merge((absDst,cr,cb))
output=cv2.cvtColor(out, cv2.COLOR_YCR_CB2BGR)
cv2.imshow('Output',output)
if(fyl==0):
output=medblur
#YCrCb Median
med=cv2.medianBlur(absDst,ymker)
tempOut=cv2.merge((med,cr,cb))
tempOutput=cv2.cvtColor(tempOut, cv2.COLOR_YCR_CB2BGR)
cv2.imshow('YCrCb Median Blur',tempOutput)
if(fym==0):
tempOutput=output
#Masking
hsv1= cv2.cvtColor(output, cv2.COLOR_BGR2HSV)
obj1 = cv2.inRange(hsv1, low, high)
res1 = cv2.bitwise_and(output, output, mask=obj1)
#DETECTION
gray=cv2.cvtColor(res1, cv2.COLOR_BGR2GRAY)
ret1,thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
(contours,hierarchy) = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for pic, contour in enumerate(contours):
area1 = cv2.contourArea(contour)
if(area1>700):
x,y,w,h = cv2.boundingRect(contour)
if(w*1.5<h and h>120):
cv2.rectangle(res1,(x,y),(x+w,y+h),(0,165,255),2)
M = cv2.moments(contour)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
cv2.circle(res1, (cX, cY), 7, (255, 255, 255), -1)
if(area1>maxArea):
maxArea=area1
txdiff=width2-cX
if(txdiff!=xdiff):
xdiff=txdiff
print('Pixel Difference:', xdiff)
#Display
cv2.imshow('Thresh Output',res1)
cv2.imshow('Video',img)
#VideoFileOutput.write(res1)
#VideoFileOutput2.write(img)
else:
break
cv2.destroyAllWindows()
#VideoFileOutput.release()
#VideoFileOutput2.release()
cap.release()
img = cv2.imread("400.jpg") # 이미지 읽어오기
img_gray = cv2.cvtColor(img,
cv2.COLOR_RGB2GRAY) # 컬러 사진이기 때문에 흑백으로 변환해야함. 흑백으로 변환
# 그러면 사진의 연결되어 있는 것만 픽셀화함. numpy의 배열값으로 변환 해준다.
img_checker = cv2.adaptiveThreshold(img_gray, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV, 191, 15)
plt.imshow(img_checker)
plt.show()
# 이미지에서 숫자가 있다면, 그 경계를 찾아주는 함수입니다.
contours, hierarchy = cv2.findContours(img_checker, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# 그래서 경계선을 찾을것을 바탕으로 사각형화 시키는 것. 시작점의 X,y좌표와 너비와 높이를 배열에 저장.
rects = [cv2.boundingRect(contour) for contour in contours]
# 그 사각형 좌표들을 for문을 돌면서 체킹
for rect in rects:
if rect[2] * rect[
3] < 1000: # 너무 작은 점도 인식하기때문에, 그 작은 점의 넓이가 1000정도 되면 매우 작은 점은 인식하지 않고 넘길 수 있다.
continue
# 사진 내에 숫자가 인식된 경우, 그 테두리를 초록색으로 그립니다.
cv2.rectangle(img, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3)
if rect[2] < 50: # 전처리시 1 같은 너비가 좁은 숫자는 인식률이 좀 낮아서 너비를 넓히는 방향으로 진행.
margin = 100
else:
margin = 30
# x좌표-마진부터 x좌표+너비+마진 크기만큼 즉, 사각형의 배열의 일정 부분보다 좀더 가져올 수 있다.
roi = img_checker[rect[1] - margin:rect[1] + rect[3] + margin,
def detectNumber(im, newResponses, barcodeNum):
global newSamples
global index
x_array = []
x_dict = {}
overlap = False
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)
#Find contours using external so it draws a box on each digit
contours,hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
samples = np.empty((0,100))
responses = []
keys = [i for i in range(48,58)]
#For each countour in this image and if the area is greater than 50
for cnt in contours:
if cv2.contourArea(cnt)>50:
[x,y,w,h] = cv2.boundingRect(cnt)
## Set some constraint to avoid detecting none digit area
if h>30 and w>10 and abs(w-h) > 10:
#Check for overlap rectangles, e.g, Zero may be detected more than once
for stored_x in x_array:
#If a detected area is less or equal to 10 than an already detected area, it means the both are too close
#Which results in overlapping rectangles
#If overlaps then break out of this loop and try next contours
if abs(stored_x - x) <= 10:
overlap = True
break
#If no overlap is occured
if overlap == False:
#Crop digit into a roi
roi = cropDigit(thresh, x, y, w, h)
ori = cropDigit(im, x, y, w, h)
#Denoise image if blurred
ori = cv2.fastNlMeansDenoisingColored(ori,None,10,10,7,21)
#Save temp image
saveTempImage(barcodeNum, ori)
#Save temp image's x value as a key to a dictionary along with its name
x_dict[x] = "a" + str(index) + ".png"
#Store x value into an array that is used for checking overlapping
x_array.append(x)
else:
overlap = False
#Sort image in order using their x values
sortImage(barcodeNum, x_dict)
thresh = cv2.threshold(image,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
labels = measure.label(thresh,neighbors=8,background=0)
charCandidates = np.zeros(thresh.shape,dtype="uint8")
for label in np.unique(labels):
if label == 0:
continue
labelMask = np.zeros(thresh.shape, dtype="uint8")
labelMask[labels == label] = 255
cnts = cv2.findContours(labelMask, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
if len(cnts) > 0:
c = max(cnts, key=cv2.contourArea)
(boxX, boxY, boxW, boxH) = cv2.boundingRect(c)
aspectRatio = boxW / float(boxH)
solidity = cv2.contourArea(c) / float(boxW * boxH)
heightRatio = boxH / float(image.shape[0])
keepAspectRatio = aspectRatio < 1.0
keepSolidity = solidity > 0.15
keepHeight = heightRatio > 0.4 and heightRatio < 0.95
if keepAspectRatio and keepSolidity and keepHeight:
hull = cv2.convexHull(c)
cv2.drawContours(charCandidates, [hull], -1, 255, -1)
def process(options: TAOptions) -> List[TAResult]:
results = []
output_prefix = join(options.output_directory, options.input_stem)
print(f"Extracting traits from '{options.input_name}'")
# read grayscale image
gray_image = imageio.imread(options.input_file, as_gray=True)
if len(gray_image) == 0:
raise ValueError(f"Image is empty: {options.input_name}")
# read color image
color_image = imageio.imread(options.input_file, as_gray=False)
if len(color_image) == 0:
raise ValueError(f"Image is empty: {options.input_name}")
# kernel = np.ones((7, 7), np.uint8)
# dilated_image = cv2.dilate(blurred_image, kernel, iterations=1)
# eroded_image = cv2.erode(dilated_image, kernel, iterations=1)
# imageio.imwrite(f"{output_prefix}.dilated.png", dilated_image)
# imageio.imwrite(f"{output_prefix}.eroded.png", eroded_image)
# binary threshold
masked_image = thresholding.binary_threshold(gray_image.astype(np.uint8))
imageio.imwrite(f"{output_prefix}.mask.png", skimage.img_as_uint(masked_image))
# closing (dilation/erosion)
kernel = np.ones((7, 7), np.uint8)
dilated_image = cv2.dilate(masked_image, kernel, iterations=1)
eroded_image = cv2.erode(dilated_image, kernel, iterations=1)
imageio.imwrite(f"{output_prefix}.dilated.png", dilated_image)
imageio.imwrite(f"{output_prefix}.eroded.png", eroded_image)
# circle detection
# print(f"Finding circles")
# detected_circles = cv2.HoughCircles(cv2.blur(eroded_image.copy(), (5, 5)),
# cv2.HOUGH_GRADIENT, 1, 40, param1=40,
# param2=39, minRadius=20, maxRadius=500)
# circle_detection_copy = color_image.copy()
# if detected_circles is not None:
# detected_circles = np.uint16(np.around(detected_circles))
# for pt in detected_circles[0, :]:
# a, b, r = pt[0], pt[1], pt[2]
# cv2.circle(circle_detection_copy, (a, b), r, (0, 255, 0), 2)
# cv2.circle(circle_detection_copy, (a, b), 1, (0, 0, 255), 3)
# cv2.imwrite(f"{output_prefix}.circles.png", circle_detection_copy)
# contour detection
# TODO exclude shapes which are square or rectangular within a certain error range
# TODO compute and return area/curvature/solidity for each contour
print(f"Finding contours")
closed_image = cv2.morphologyEx(dilated_image.copy(), cv2.MORPH_CLOSE, kernel)
contours, hierarchy = cv2.findContours(closed_image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours_image = color_image.copy()
min_area = 10000
max_area = 200000
filtered_counters = []
i = 0
for contour in contours:
i += 1
cnt = cv2.approxPolyDP(contour, 0.035 * cv2.arcLength(contour, True), True)
bounding_rect = cv2.boundingRect(cnt)
(x, y, w, h) = bounding_rect
min_rect = cv2.minAreaRect(cnt)
area = cv2.contourArea(contour)
rect_area = w * h
if max_area > area > min_area and abs(area - rect_area) > 0.3:
filtered_counters.append(contour)
# draw and label contours
cv2.drawContours(contours_image, [contour], 0, (0, 255, 0), 3)
cv2.putText(contours_image, str(i), (x + 30, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# draw min bounding box
# box = np.int0(cv2.boxPoints(min_rect))
# cv2.drawContours(contours_image, [box], 0, (0, 0, 255), 2)
# draw min bounding box
# box = np.int0(cv2.boxPoints(bounding_rect))
# cv2.drawContours(contours_image, [bounding_rect], 0, (0, 0, 255), 2)
result = TAResult(
id=str(i),
area=area,
solidity=min(round(area / rect_area, 4), 1),
max_height=h,
max_width=w)
results.append(result)
print(f"Kept {len(filtered_counters)} of {len(contours)} total contours")
cv2.imwrite(f"{output_prefix}.contours.png", contours_image)
# edge detection
print(f"Finding edges")
edges_image = cv2.Canny(color_image, 100, 200)
cv2.imwrite(f"{output_prefix}.edges.png", edges_image)
return results
# resize the image by 0.5
imgThres = cv2.resize(imgThres, (0, 0), None, 0.5, 0.5)
frame = cv2.resize(frame, (0, 0), None, 0.5, 0.5)
img = cv2.resize(img, (0, 0), None, 0.5, 0.5)
# find contours
image, contours, hierarchy = cv2.findContours(imgThres, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
image2, contours2, hierarchy2 = cv2.findContours(img, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# find contour and match rectangles
# contours from HSV image
rectangles = []
for i in range(0, len(contours)):
x, y, w, h = cv2.boundingRect(contours[i])
if w >= 5 and h >= 5:
rectangles.append((x, y, w, h))
# cv2.rectangle(frame, (x, y), (x + w, y + h), (160, 32, 240), 2)
rectangles = rectangleFilter(rectangles)
for i in rectangles:
x, y, w, h = i[0], i[1], i[2], i[3]
cv2.rectangle(frame, (x, y), (x + w, y + h), (160, 32, 240), 2)
# contours from frame difference
rectangles2 = []
for i in range(0, len(contours2)):
x, y, w, h = cv2.boundingRect(contours2[i])
if w >= 5 and h >= 5:
rectangles2.append((x, y, w, h))
def checktrigger(self):
crop_top = 0
crop_bottom = 1
crop_left = 0
crop_right = 1
l = int(self.res[0] * crop_left)
r = int(self.res[0] * crop_right)
t = int(self.res[1] * crop_top)
b = int(self.res[1] * crop_bottom)
vid_frames = []
avg_centroids = []
total_frames = 0
total_time = 0
logging.info("WATCHDOG_USEC=" + self.watchdog_usec)
reading = True
while reading:
reading, f = self.cam.read()
self.notifier.notify("WATCHDOG=1")
start = dt.now()
if not self.cont:
break
frame = f[t:b, l:r]
imgrey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
imgrey = cv2.GaussianBlur(imgrey, (21, 21), 0)
if self.background is None:
self.background = imgrey.copy().astype("float")
self.save_bg()
logging.info("initialised background")
continue
cv2.accumulateWeighted(imgrey, self.background, 0.5)
frame_delta = cv2.absdiff(imgrey, cv2.convertScaleAbs(self.background))
frame_threshold = cv2.threshold(frame_delta, 20, 255, cv2.THRESH_BINARY)[1]
frame_dilated = cv2.dilate(frame_threshold, None, iterations=10)
im2, contours, hier = cv2.findContours(frame_dilated, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
motion = [c for c in contours if cv2.contourArea(c) >= self.min_area]
frame_centroids = []
for c in motion:
x, y, w, h = cv2.boundingRect(c)
# cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
m = cv2.moments(c)
cx = int(m['m10'] / m['m00'])
frame_centroids.append(cx)
if len(motion) == 0:
if self.moving_frames != 0:
self.silent_frames += 1
if self.silent_frames == self.max_silent_frames:
if self.moving_frames >= self.min_moving_frames:
logging.info("movement ended")
self.make_vid(vid_frames, avg_centroids)
else:
logging.info("false alarm, sorry")
self.save_bg()
self.moving_frames = 0
vid_frames.clear()
avg_centroids.clear()
else:
vid_frames.append(f)
avg_centroids.append(avg_centroids[-1])
else:
self.moving_frames += 1
self.silent_frames = 0
vid_frames.append(f)
avg_centroids.append(np.mean(frame_centroids))
if self.moving_frames == 1:
logging.info("what was that??")
logging.info("Chance of rain in 3 hours beginning " + \
str(int(self.weatherPerson.slot / 60)).ljust(4, '0') + \
" is " + self.weatherPerson.lastRainForecast + "%")
if self.moving_frames == self.min_moving_frames:
logging.info("movement detected")
if self.moving_frames == self.max_moving_frames:
logging.warning("this has been going on too long, stopping")
self.make_vid(vid_frames, avg_centroids)
self.save_bg()
self.moving_frames = 0
vid_frames.clear()
avg_centroids.clear()
total_time += (dt.now() - start).microseconds / 1000000
total_frames += 1
self.fps = total_frames / total_time
frame_centroids.clear()
self.cam.release()
logging.info("camera closed, thread terminated")
for j in range(0, len(A)):
Ant = np.zeros((1, len(A[j])))
for i in range(0, len(A[j])):
Ant[0][i] = float(A[j][i])
Ant = map(tuple, Ant)
Dic.update({j + 1: Ant[0]})
##########################################Representation and classification of the coded targets################################################
#### finding the ROI of each shot-code
_, contours, _ = cv2.findContours(img_5, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for (counter, cnt) in enumerate(contours):
(x, y, w, h) = cv2.boundingRect(cnt)
ROI = img_prime[4 * y - 5:4 * (y + h) + 5, 4 * x - 5:4 * (x + w) + 5]
#### obtain all the subedges of the ROI, if there is just one subregion, ignore it
EDG = cv2.Canny(ROI, 0, 255)
_, subcontours, _ = cv2.findContours(EDG.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if len(subcontours) == 1:
break
#### fitting an ellipse to each subedge and finding the one with minimum fitting error
I = np.zeros(ROI.shape)
E = []
CENTER = []
# plt.title('Original Image'), plt.xticks([]), plt.yticks([])
# plt.subplot(122),plt.imshow(edges,cmap = 'gray')
# plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
#
# plt.show()
image = cv2.imread("manga2.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # grayscale
# thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2) # threshold
thresh = cv2.Canny(gray, 100, 200)
#hopefully this would get rid of some noise as text is relatively dense
thresh = cv2.medianBlur(thresh, 1)
cv2.imshow('image', thresh)
cv2.waitKey(0)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
dilated = cv2.dilate(thresh, kernel, iterations=12) # dilate
s, contours, hierarchy = cv2.findContours(
dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # get contours
# for each contour found, draw a rectangle around it on original image
for contour in contours:
[x, y, w, h] = cv2.boundingRect(contour)
if h > 200 and w > 200:
continue
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
print("done")
# write original image with added contours to disk
cv2.imwrite("contoured.jpg", image)
while True:
ret, frame = cap.read()
if ret:
fgmask = bgsMOG.apply(frame, None, 0.01)
# To find the contours of the objects
_, contours, hierarchy = cv2.findContours(fgmask,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# cv2.drawContours(frame,contours,-1,(0,255,0),cv2.cv.CV_FILLED,32)
try:
hierarchy = hierarchy[0]
except:
hierarchy = []
a = []
for contour, hier in zip(contours, hierarchy):
(x, y, w, h) = cv2.boundingRect(contour)
if w > 30 and h > 30:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0),
2)
(x, y, w, h) = cv2.boundingRect(contour)
x1 = w / 2
y1 = h / 2
cx = x + x1
cy = y + y1
a.append([cx, cy])
# print(len(a))
cv2.imshow('BGS', fgmask)
cv2.imshow('Ori+Bounding Box', frame)
def get_contour_precedence(contour, cols, tolerance_factor=10):
origin = cv2.boundingRect(contour)
return ((origin[1] // tolerance_factor) * tolerance_factor) * cols + origin[0]