def countcotours(img):
nc=0
dir="img/"+img+".png"
image = cv2.imread(dir)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Image", gray)
cv2.waitKey(0)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
cv2.imshow("Image", blurred)
cv2.waitKey(0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
cv2.imshow("Image", thresh)
cv2.waitKey(0)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
sd = ShapeDetector()
for c in cnts:
# compute the center of the contour
if cv2.contourArea(c)<800:
continue
else:
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
shape = sd.detect(c)
#if(shape=="square" or shape=="rectangle"):
if 1==1:
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.circle(image, (cX, cY), 7, (255, 255, 255), -1)
cv2.putText(image, "center", (cX - 20, cY - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
nc=nc+1
cv2.imshow("Image", image)
cv2.waitKey(0)
return nc
def findcolors(nc,im):
cont=0
image = cv2.imread(im)
blurred = cv2.GaussianBlur(image, (5, 5), 0)
gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
lab = cv2.cvtColor(blurred, cv2.COLOR_BGR2LAB)
thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cl = ColorLabeler()
sd = ShapeDetector()
for c in cnts:
if cv2.contourArea(c)< 800:
continue
else:
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)
print(shape)
print(color)
if (shape=="square" or shape=="rectangle"):
if (color==nc):
text = "{}".format(color)
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, text, (cX, cY),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
cont=cont+1
cv2.imshow("Image", image)
cv2.waitKey(0)
return cont
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY_INV)[1]
# find contours in thresholded image and initialize shape detector
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
sd = ShapeDetector()
# loop over contours
for c in cnts:
# compute center, detect name of shape
M = cv2.moments(c)
cX = int((M["m10"] / M["m00"]) * ratio)
cY = int((M["m01"] / M["m00"]) * ratio)
shape = sd.detect(c)
# multiply the contour (x, y)-coordinates by the resize ratio,
# then draw the contours and nape of the shapes
c = c.astype("float")
c *= ratio
c = c.astype("int")
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
cv2.putText(image, shape, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255), 2)
# show image
cv2.imshow("Image", image)
cv2.waitKey(0)
original=image.copy()
gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
blurred=cv2.GaussianBlur(gray,(5,5),0)
thresh=cv2.threshold(blurred,60,255,cv2.THRESH_BINARY)[1]
cnts=cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts=cnts[1]
sd=ShapeDetector()
for c in cnts:
M=cv2.moments(c)
cX=0
cY=0
if(M["m00"])!=0:
cX=int(M["m10"]/M["m00"])
cY=int(M["m01"]/M["m00"])
shape=sd.detect(c)
cv2.drawContours(image,[c],-1,(0,255,0),2)
cv2.circle(image,(cX,cY),7,(255,255,255),2)
cv2.putText(image, shape, (cX - 20, cY - 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
plt.subplot(2,2,1),plt.imshow(original,cmap = 'gray'),plt.title('Original')
plt.axis("off")
plt.subplot(2,2,2),plt.imshow(thresh,cmap="gray"),plt.title('Threshold')
plt.axis("off")
plt.subplot(2,2,3),plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)),plt.title('Result')
plt.axis("off")
plt.show()
class PartDetector:
def __init__(self):
self.sd = ShapeDetector()
def detect(self, frame, markInFrame=False):
resized = imutils.resize(frame, width=300)
ratio = frame.shape[0] / float(resized.shape[0])
contours = self._find_contour(resized)
if len(contours) == 0:
return None, None
contour, center = self._Filter_n_find_center(contours, resized.shape,
ratio)
if contour is None:
return None, None
shape = self.sd.detect(contour)
if shape is None:
return None, None
if markInFrame:
c = contour.astype("float")
c *= ratio
c = c.astype("int")
self._mark_object(frame, c, center, shape)
return shape, center
def _find_contour(self, frame):
#cv2.imshow('frame',frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#cv2.imshow('gray',gray)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
#blurred = cv.medianBlur(gray, 5)
#cv2.imshow('blurred',blurred)
#clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
#hist_equal = clahe.apply(blurred)
hist_equal = cv2.equalizeHist(blurred)
#cv2.imshow('hist_equal',hist_equal)
ret, thresh = cv2.threshold(
hist_equal, 50, 255, cv2.THRESH_BINARY_INV) # + cv2.THRESH_OTSU)
thresh = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_MEAN_C,\
cv2.THRESH_BINARY_INV,11,2)
thresh = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.THRESH_BINARY_INV,11,2)
"""
thresh[:,:60] = 0
thresh[:,-60:] = 0
thresh[:60,:] = 0
thresh[-60:,:] = 0
"""
#cv2.imshow('thresh',thresh)
# Taking a matrix of size 5 as the kernel
#kernel = np.ones((3,3), np.uint8)
# The first parameter is the original image,
# kernel is the matrix with which image is
# convolved and third parameter is the number
# of iterations, which will determine how much
# you want to erode/dilate a given image.
#img_erosion = cv2.erode(thresh, kernel, iterations=1)
#thresh = cv2.dilate(img_erosion, kernel, iterations=1)
#cv2.imshow('erode_dilate',thresh)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
return cnts
def _Filter_n_find_center(self, cnts, shape, ratio=1):
for c in cnts:
M = cv2.moments(c)
if M["m00"] == 0:
continue
cX = int((M["m10"] / M["m00"]) * ratio)
cY = int((M["m01"] / M["m00"]) * ratio)
area = M['m00']
frameArea = shape[0] * shape[1]
areaRatio = area / frameArea
if areaRatio < 0.01 or areaRatio > 0.1:
continue
return c, [cX, cY]
return None, None
def _mark_object(self, frame, contour, center, shape, ratio=1):
cv2.circle(frame, tuple(center), 5, (0, 255, 0))
cv2.drawContours(frame, [contour], -1, (0, 255, 0), 2)
cv2.putText(frame, shape, tuple(center), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255, 255, 255), 2)
