def patternDetection(cnts):
#Initialize classes
sd = ShapeDetector()
cl = ColorLabeler()
contours = []
for c in cnts:
shape = sd.detect(c, 3)
if shape != "":
color = cl.label(lab, c)
x,y,w,h = cv2.boundingRect(c)
cX = x+w/2
cY = y+h/2
contours.append([cX, cY, shape, color])
row = []
pattern = []
rowMin = contours[0][1]-10
rowMax = contours[0][1]+10
for c in contours:
cX, cY, shape, color = c
if rowMin <= cY and cY <= rowMax:
row.append([cX, cY, shape+' '+color])
else:
row.sort()
pattern.append(row)
row = [[cX, cY, shape+' '+color]]
rowMin = cY-10
rowMax = cY+10
return pattern
def alltheCV(image):
#resized = imutils.resize(image, width=300)
#ratio = image.shape[0] / float(resized.shape[0])
#height, width = image.shape[:2]
print("runningCV")
#Convert white background to black
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
(thresh, baw) = cv2.threshold(gray, 128, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
wab = cv2.bitwise_not(baw)
wabrgb = cv2.cvtColor(wab, cv2.COLOR_GRAY2RGB)
noBackground = cv2.bitwise_and(image, wabrgb)
#Blur and threshold the image for curve detection
blurred = cv2.GaussianBlur(noBackground, (5, 5), 0)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
gray = cv2.cvtColor(noBackground, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)[1]
#Detect contours
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0]
#Initialize classes
sd = ShapeDetector()
cl = ColorLabeler()
#Finding centers using bounding rectangle
centers = []
for c in cnts:
shape, perimeter = sd.detect(c, 1)
color = cl.label(lab, c)
x, y, w, h = cv2.boundingRect(c)
cX = x + w / 2
cY = y + h / 2
centers.append([cX, cY, perimeter, shape, color])
return centers
lab = cv2.cvtColor(noBackground, cv2.COLOR_BGR2LAB)
blurred_3 = cv2.GaussianBlur(noBackground, (1, 1), 0)
gray_3 = cv2.cvtColor(blurred_3, cv2.COLOR_BGR2GRAY)
thresh_3 = cv2.threshold(gray_3, 30, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh_3.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
#Initialize classes
sd = ShapeDetector()
cl = ColorLabeler()
contours = []
for c in cnts:
shape = sd.detect(c, 3)
if shape != "":
color = cl.label(lab, c)
x, y, w, h = cv2.boundingRect(c)
cX = x + w / 2
cY = y + h / 2
contours.append([cX, cY, shape, color])
# cv2.imshow("Image", img)
# cv2.circle(img, (cX, cY), 3, (255, 255, 255), -1)
# cv2.waitKey(0)
# cv2.imshow("Image", img)
# cv2.circle(img, (cX, cY), 3, (255, 255, 255), -1)
# cv2.waitKey(0)
# print(contours)
# Finding centers using moment of shape
# centers = []
# for c in cnts:
# if cv2.contourArea(c) > 10:
# M = cv2.moments(c)
# cX = int(M["m10"] / M["m00"])
# cY = int(M["m01"] / M["m00"])
# shape = sd.detect(c)
# color = cl.label(lab, c)
# centers.append([cX,cY, shape, color])
#Finding centers using bounding rectangle
centers = []
for c in cnts:
shape, perimeter = sd.detect(c, ratio)
color = cl.label(lab, c)
x, y, w, h = cv2.boundingRect(c)
cX = x + w / 2
cY = y + h / 2
centers.append([cX, cY, perimeter, shape, color])
centersSorted = sorted(centers, key=lambda k: [k[0], k[1]])
curr = 0
col = []
for c in centersSorted:
if curr > cY: #new column
output.append(col) #add old column
col = []
# Finding centers using moment of shape
# centers = []
# for c in cnts:
# if cv2.contourArea(c) > 10:
# M = cv2.moments(c)
# cX = int(M["m10"] / M["m00"])
# cY = int(M["m01"] / M["m00"])
# shape = sd.detect(c)
# color = cl.label(lab, c)
# centers.append([cX,cY, shape, color])
#Finding centers using bounding rectangle
centers = []
for c in cnts:
shape = sd.detect(c, ratio)
color = cl.label(lab, c)
x, y, w, h = cv2.boundingRect(c)
cX = x + w / 2
cY = y + h / 2
centers.append([cX, cY, shape, color])
centersSorted = sorted(centers, key=lambda k: [k[0], k[1]])
for c in centersSorted:
cX, cY, shape, color = c
cv2.circle(image, (cX, cY), 3, (255, 255, 255), -1)
cv2.imshow('image', image)
print(shape, color)
# print(cX, cY)
cv2.waitKey(0)