def get_candidate_contours(img, img_contour, area_min):
area = 0
# crop_xywh = None
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for cnt in contours:
area = cv2.contourArea(cnt)
if area > area_min:
print(area, area_min)
cv2.drawContours(img_contour, cnt, -1, (255, 0, 255), 7)
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
x, y, w, h = cv2.boundingRect(approx)
# crop_xywh = x, y, w, h
cv2.rectangle(img_contour, (x, y), (x + w, y + h), (0, 255, 0), 5)
else:
area = 0
print(area)
if area is not 0:
return 1
else:
return 0
def approximationContour(img, contours, e=0.02):
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
epsilon = e*cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, True)
cv2.drawContours(img, [approx], 0, (0,255,255), 2)
return img
def identify_countors(image):
stations = []
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image_invert = cv2.bitwise_not(image_gray)
contours, _ = cv2.findContours(image_invert, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for cnt in contours:
approx = cv2.approxPolyDP(cnt, 0.02 * cv2.arcLength(cnt, True), True)
M = cv2.moments(cnt)
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
x, y, w, h = cv2.boundingRect(cnt)
# detect if the square is rotationed
# TODO: detect in some other way
tilted = cnt.ravel()[0] == cx
station = {
"type": get_type_by_edges(len(approx), tilted),
"pos": (x, y),
"centroid": (cx, cy),
"size": (w, h),
"contour": cnt
}
stations.append(station)
return stations
def getContours(img, img_rgb):
contours, hierarchy = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
for cnt in contours:
area = cv.contourArea(cnt)
if area > 500:
cv.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
peri = cv.arcLength(cnt, True)
approx = cv.approxPolyDP(cnt, 0.02*peri, True)
cv.drawContours(img_rgb, [approx], 0, (255,0,0),5)
cv.imshow('tmp', img_rgb)
# cv.waitKey()
objCor = len(approx)
x, y, w, h = cv.boundingRect(approx)
objectType = None
if objCor == 4:
objectType = "Rectangle"
elif w == h:
objectType = "Square"
if objCor == 8:
objectType = "Circle"
if objectType:
cv.rectangle(imgContour, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv.putText(imgContour, objectType,
(x+(w//2)-10, y+(h//2)-10), cv.FONT_HERSHEY_COMPLEX, 0.7,
(0, 0, 0), 2)
def getContours(img,
cThr=[100, 100],
showCannny=False,
minArea=1000,
filter=0,
draw=False):
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1)
imgCanny = cv2.Canny(imgBlur, cThr[0], cThr[1])
kernel = np.ones((5, 5))
imgDial = cv2.dilate(imgCanny, kernel, iterations=3)
imgThre = cv2.erode(imgDial, kernel, iterations=2)
if showCannny: cv2.imshow('Canny', imgThre)
contours, hiearchy = cv2.findContours(imgThre, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
finalContours = []
for i in contours:
area = cv2.contourArea(i)
if area > minArea:
peri = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, 0.02 * peri, True)
bbox = cv2.boundingRect(approx)
if filter > 0:
if len(approx) == filter:
finalContours.append([len(approx), area, approx, bbox, i])
else:
finalContours.append([len(approx), area, approx, bbox, i])
finalContours = sorted(finalContours, key=lambda x: x[1], reverse=True)
if draw:
for con in finalContours:
cv2.drawContours(img, con[4], -1, (0, 0, 255), 3)
return img, finalContours
def find_sudoku(image):
# preprocessing
gray_sudoku = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred_sudoku = cv2.GaussianBlur(gray_sudoku, (7, 7), 0)
thresh = cv2.adaptiveThreshold(blurred_sudoku, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV, 11, 2)
# find contours
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# get the sudoku contour
sudoku_area = 0
sudoku_contour = contours[0]
for cnt in contours:
area = cv2.contourArea(cnt)
x, y, w, h = cv2.boundingRect(cnt)
if (0.7 < float(w) / h < 1.3 # aspect ratio
and area > 150 * 150 # minimal area
and area > sudoku_area # biggest area on screen
and area > .5 * w * h): # fills bounding rect
sudoku_area = area
sudoku_contour = cnt
perimeter = cv2.arcLength(sudoku_contour, True)
# define the accuracy here
epsilon = 0.05 * perimeter
approx = cv2.approxPolyDP(sudoku_contour, epsilon, True)
# if it is not a sudoku board, just return the frame without doing anything
if len(approx) != 4:
return None
return sudoku_contour
def getBoxPoint(contour):
""" 多边形拟合凸包 """
hull = cv2.convexHull(contour)
epsilon = 0.02 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(hull, epsilon, True)
approx = approx.reshape((len(approx), 2))
return approx
def detectshape(c):
# initialize the shape name and approximate the contour
shape = "unidentified"
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.04 * peri, True)
# if the shape is a triangle, it will have 3 vertices
if len(approx) == 3:
shape = "triangle"
# if the shape has 4 vertices, it is either a square or
# a rectangle
elif len(approx) == 4:
# compute the bounding box of the contour and use the
# bounding box to compute the aspect ratio
(x, y, w, h) = cv2.boundingRect(approx)
ar = w / float(h)
# a square will have an aspect ratio that is approximately
# equal to one, otherwise, the shape is a rectangle
shape = "square" if ar >= 0.95 and ar <= 1.05 else "rectangle"
# if the shape is a pentagon, it will have 5 vertices
elif len(approx) == 5:
shape = "pentagon"
# otherwise, we assume the shape is a circle
else:
shape = "circle"
# return the name of the shape
return shape
def contour(img):
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for cnt in contours:
area = cv2.contourArea(cnt)
print(area)
if area > 10:
cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
peri = cv2.arcLength(cnt, True)
print(peri)
approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
print(len(approx))
objCor = len(approx)
x, y, w, h = cv2.boundingRect(approx)
if objCor == 3:
objectType = "Tri"
elif objCor == 4:
aspRatio = w / float(h)
if aspRatio > 0.95 and aspRatio < 1.05:
objectType = "Square"
else:
objectType = "Rectangle"
elif objCor > 4:
objectType = "Circle"
else:
objectType = "None"
cv2.rectangle(imgContour, (x, y), (x + w, y + h), (0, 255, 0),
2)
cv2.putText(imgContour, objectType,
(x + (w // 2) - 10, y + (h // 2) - 10),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 50, 20), 2)
def alineamiento(imagen,ancho,alto):
imagen_alineada=None
# le pasamos grises a la imagen
grises=cv2.cvtColor(imagen, cv2.COLOR_BGR2GRAY)
# devolvemos dos umbrales minimos y maximos
tipoumbral,umbral=cv2.threshold(grises, 150,255, cv2.THRESH_BINARY)
# mostramos el umbral
cv2.imshow("Umbral", umbral)
# contornos que devuelven dos valores, contorno y jerarquia
contorno=cv2.findContours(umbral, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
# ordenar los contornos son sorted
# reverse ordena los puntos de menor a mayor, son para los eje x
contorno=sorted(contorno,key=cv2.contourArea,reverse=True)[:1]
# recorremos los cotornor
for c in contorno:
# la variable c esta recorriendo todos los contornos
# epsilon ayuda a encontrar las areas
# arcLenght sirve para sacar las areas
epsilon=0.01*cv2.arcLength(c, True)
# aca piden las curvas que va a analizar para que las curvas no tengan tando ruido
approximacion=cv2.approxPolyDP(c, epsilon, True)
# contamos objetos que tenemos en la lista
# los 4 puntos forman un circulo
if len(approximacion)==4:
puntos=ordenarpuntos(approximacion)
# convertimos los puntos en alto y ancho
puntos1=np.float32(puntos)
puntos2=np.float32([[0,0],[ancho,0],[0,alto],[ancho,alto]])
# metodo de perspectiva
# se mantiene fijo M en caso que la camara rote
M = cv2.getPerspectiveTransform(puntos1, puntos2)
# a la imagen alineada le pasamos la informacion
imagen_alineada=cv2.warpPerspective(imagen, M, (ancho,alto))
return imagen_alineada
def find_corners_of_largest_polygon(original, img):
imgCopy = original.copy()
cnts, hierarchy = cv2.findContours(img, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
'''c = cnts[2]
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)'''
min_area = np.inf
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# screenCnt = [[]]
if len(approx) == 4:
area = cv2.contourArea(c)
if area < min_area:
# del screenCnt[::]
# screenCnt = approx
(x, y, w, h) = cv2.boundingRect(approx)
if ((int(w / h)) == 1):
screenCnt = approx
min_area = area
cv2.drawContours(imgCopy, [screenCnt], -1, (0, 255, 0), 3)
# Top-left corner has minimum x+y
s = []
for x in screenCnt:
for y in x:
s.append(sum(y))
d = np.array([])
for x in screenCnt:
for y in x:
d = np.append(d, np.diff(y))
rect = np.zeros((4, 2), dtype="float32")
# Minimum sum is top-left corner
rect[0] = screenCnt[s.index(min(s))]
# Maximum is bottom-right
rect[2] = screenCnt[s.index(max(s))]
# Minimum difference is top-right point
rect[1] = screenCnt[np.argmin(d)]
# maximum difference is bottom left
rect[3] = screenCnt[np.argmax(d)]
# Thus the order of the points is top-left, top-right, bottom-right,bottom-left
# print("rectangle", rect)
# cv2.drawContours(imgCopy, [c], -1, (0, 255, 0), 3)
# display_image('conotur waala image', imgCopy)
return rect
def zoom_number(image, cont):
inverted_threshold = filter_squares(image)
contours = contours_numbers(inverted_threshold)
largest_area = 0
x = 0
y = 0
w = 0
h = 0
curve = 0
for i, c in enumerate(contours):
xTemp, yTemp, wTemp, hTemp = cv2.boundingRect(c)
if( cv2.contourArea(c) > largest_area and \
(hTemp < image.shape[0]*.9 and wTemp < image.shape[1]*.9)\
and hTemp > 10 and wTemp > 10): #limites definidos, numero no es menor a 10 ni debe
largest_area = cv2.contourArea(c) # medir el 90% de la imagen
curve = cv2.arcLength(c, True)
x, y, w, h = cv2.boundingRect(c)
# print("contourArea for",cont," is: ",curve,"image h:",image.shape[0],"image w:",image.shape[1])
# print("contour h:",h," and w: ",w)
# cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)
if (curve != 0):
top_left_point = (x, y)
top_right_point = (x + w, y)
bottom_left_point = (x, y + h)
bottom_right_point = (x + w, y + h)
corners = np.float32([
top_left_point, top_right_point, bottom_left_point,
bottom_right_point
])
result = transform(image, corners, 50, 50)
else:
result = image
return result
def getContours(img):
contours, hierachy = cv2.findContours(img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for cnt in contours:
area = cv2.contourArea(cnt)
#print('Area: ', area)
if area > 500: # this check will confirm the area greater than threshold value to avoid noise
cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
peri = cv2.arcLength(cnt, True)
#print('Perimeter:', peri)
approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
print('Approximate Corners: ', len(approx))
objCorner = len(approx)
x, y, w, h = cv2.boundingRect(approx)
if objCorner == 3:
objType = 'Triangle'
elif objCorner == 4:
aspRatio = w / float(h)
if aspRatio > 0.95 and aspRatio < 1.05:
objType = 'Square'
else:
objType = 'Rectangle'
elif objCorner > 4:
objType = 'Circle'
else:
objType = 'Other'
cv2.rectangle(imgContour, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(imgContour, objType,
(x + (w // 2) - 10, y + (h // 2) - 10),
cv2.FONT_HERSHEY_COMPLEX, 0.7, (0, 0, 0), 2)
def detect_holes(grey_frame, original_frame, frame_copy):
thresh = cv.adaptiveThreshold(grey_frame, 1, 0, 0, 61, 10)
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE,
cv.CHAIN_APPROX_NONE)
for contour in contours:
# area = cv.contourArea(contour)
perimeter = cv.arcLength(contour, True)
if len(contour) > 100 and 100 < perimeter < 300:
# if len(contour) > 5 and 200 < area < 1000:
x, y, w, h = cv.boundingRect(contour)
img_c = frame_copy[y:y + h, x:x + w]
img = cv.resize(img_c, IMAGE_SIZE)
img_array = img_to_array(img)
img_array = img_array.reshape(
(1, ) + img_array.shape) # Converting into 4 dimension array
interpreter.set_tensor(input_details[0]['index'], img_array)
interpreter.invoke()
predictions = interpreter.get_tensor(output_details[0]['index'])
if predictions[0][0] > 0.9:
cv.rectangle(original_frame, (x, y), (x + w, y + h),
(0, 0, 255), 2)
cv.imwrite(
new_dir + '/' + str(time.time()) + str(random()) + '.jpg',
img_c)
return original_frame
def GetRightPos(image):
# 边缘检测
canny = cv2.Canny(image, 200, 400)
# 轮廓提取
img, contours, _ = cv2.findContours(canny, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
rightRectangles = []
for i, contour in enumerate(contours):
M = cv2.moments(contour)
if M['m00'] == 0:
cx, cy = 0, 0
else:
cx, cy = M['m10'] / M['m00'], M['m01'] / M['m00']
if 1000 < cv2.contourArea(contour) < 1300 and 120 < cv2.arcLength(
contour, True) < 400:
if cx > 100:
x, y, w, h = cv2.boundingRect(contour) # 外接矩形
rightRectangles.append((x, y, w, h))
if len(rightRectangles) > 0:
# 内侧方块
current = min(rightRectangles, key=lambda s: s[2] * s[3])
x, y, w, h = current[0], current[1], current[2], current[3]
return x, y, w, h
return 0, 0, 0, 0
def detect_holes(grey_frame, original_frame, frame, trackbars, count):
thresh = cv.adaptiveThreshold(grey_frame, *trackbars)
# ret, thresh = cv.threshold(grey_frame, 127, 140, 0)
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
for contour in contours:
# approx = cv.approxPolyDP(contour, 0.01 * cv.arcLength(contour, True), True)
# area = cv.contourArea(contour)
perimeter = cv.arcLength(contour, True)
if len(contour) > 100 and 100 < perimeter < 300:
# if len(contour) > 5 and 200 < area < 1000:
x, y, w, h = cv.boundingRect(contour)
img_c = original_frame[y:y + h, x:x + w]
# if count % 30 == 0:
# cv.imwrite(new_dir + '/' + str(time.time()) + str(random()) + '.jpg', img_c)
img = cv.resize(img_c, IMAGE_SIZE)
img_array = img_to_array(img)
img_array = img_array.reshape((1,) + img_array.shape) # Converting into 4 dimension array
interpreter.set_tensor(input_details[0]['index'], img_array)
interpreter.invoke()
predictions = interpreter.get_tensor(output_details[0]['index'])
if predictions[0][0] > 0.9:
# print(predictions[0][1])
# if count % 30 == 0:
# cv.imwrite(new_dir + '/' + str(time.time()) + str(random()) + '.jpg', img_c)
cv.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
def ConvexHull_Cal(contour):
IsTriangle = lambda a, b, c: a + b > c and a + c > b and b + c > a #任意两边和必须大于第三边
point_list = []
convex_angle_ls = []
concave_angle_ls = []
epsilon = 0.003 * cv2.arcLength(contour, True)
contour = cv2.approxPolyDP(contour, epsilon, True) #轮廓近似，Douglas-Peucker算法
hull = cv2.convexHull(contour, returnPoints=False)
defects = cv2.convexityDefects(contour, hull)
_, radius = cv2.minEnclosingCircle(contour)
if defects is not None:
for i in range(defects.shape[0]):
s, e, f, _ = defects[i, 0]
sta = tuple(contour[s][0])
end = tuple(contour[e][0])
far = tuple(contour[f][0])
point_list.append([sta, far, end])
#下面的角边标示含义见文件夹里的图片说明
if len(point_list) >= 2:
for it_1, it_2 in zip(point_list, point_list[1:] + point_list[:1]):
CA = scfun.Eucledian_Distance(it_1[1], it_1[2]) #far to end
AB = scfun.Eucledian_Distance(it_1[2], it_2[1]) #end to next far
#凸包的角度
if radius <= CA + AB < 2 * radius:
BC = scfun.Eucledian_Distance(it_1[1],
it_2[1]) #far to 2nd far，为底边
if IsTriangle(CA, AB, BC):
angle = acos((CA**2 + AB**2 - BC**2) / (2 * CA * AB))
convex_angle_ls.append(angle)
#凹陷的角度
DC = scfun.Eucledian_Distance(it_1[0], it_1[1]) #sta to far
if radius <= DC + CA < 2 * radius:
DA = scfun.Eucledian_Distance(it_1[0],
it_1[2]) #sta to end，为底边
if IsTriangle(DC, CA, DA):
angle = acos((CA**2 + DC**2 - DA**2) / (2 * CA * DC))
concave_angle_ls.append(angle)
convex_angle = [x for x in convex_angle_ls
if pi / 18 <= x <= pi / 6] #凸包角度:10度至30度
convex_len = len(convex_angle)
concave_angle = [
x for x in concave_angle_ls if pi / 18 <= x <= pi / 3.5
]
concave_len = len(concave_angle)
result = [convex_len, concave_len]
else:
result = [0, 0]
return result
def approximationContour(img, contours, e=0.02):
for cnt in contours:
x, y, w, h = cv.boundingRect(cnt)
print(f'x: {x}, y: {y}, w: {w}, h: {h}')
epsilon = e*cv.arcLength(cnt, True)
approx = cv.approxPolyDP(cnt, epsilon, True)
print(len(approx))
cv.drawContours(img, [approx], 0, (0,255,255), 2)
return img
def smooth_contours(contours, peri_factor=0.007):
smoothed_contours = []
for c in contours:
peri = cv2.arcLength(c, True)
smooth_c = cv2.approxPolyDP(c, peri_factor * np.sqrt(peri), True)
smoothed_contours.append(smooth_c)
return smoothed_contours
def rectwithname(img, contours, e=0.02):
result = img.copy()
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
epsilon = e*cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, True)
# if len(approx)<7:
cv2.rectangle(result,(x,y),(x+w,y+h),(255,0,255),2)
return result
def getContours(_img):
contours, hierarchy = cv2.findContours(_img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for cnt in contours:
# area = cv2.contourArea(cnt)
# cv2.drawContours(imgContour, cnt, -1, (255,0,0), 3)
cnt_length = cv2.arcLength(cnt, True)
cnt_vertexes_approx = cv2.approxPolyDP(cnt, 0.02 * cnt_length, True)
x, y, w, h = cv2.boundingRect(cnt_vertexes_approx)
cv2.rectangle(imgContour, (x, y), (x + w, y + h), (0, 255, 0), 3)
def getContours(img):
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
x, y, w, h = 0, 0, 0, 0
for cnt in contours:
area = cv2.contourArea(cnt)
if area > 500:
# cv2.drawContours(imgResult, cnt, -1, (255, 0, 0), 3)
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
x, y, w, h = cv2.boundingRect(approx)
return x + w // 2, y
def do_rectangles(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 125, 255, cv2.THRESH_BINARY)[1]
thresh = 255 - thresh
cv2.imshow('thresh', thresh)
cv2.waitKey()
kernel = np.array(
[[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1]], np.uint8)
kernel3 = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], np.uint8)
#dilation = cv2.dilate(thresh, kernel, iterations=4)
#erosion = cv2.erode(dilation, kernel, iterations=2)
#
erosion = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel3)
dilation = cv2.morphologyEx(erosion, cv2.MORPH_OPEN, kernel3)
cv2.imshow('erosion7', erosion)
cv2.imshow('dilation7', dilation)
dilation = cv2.morphologyEx(dilation, cv2.MORPH_CLOSE, kernel)
cv2.imshow('dilation3', dilation)
cv2.waitKey()
#exit()
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
cnts = imutils.grab_contours(cnts)
disp = img.copy()
clr = [0, 0, 0]
for i in range(len(cnts)):
idx = i % 3
clr[idx] = np.random.randint(0, 256)
#x, y, w, h = cv2.boundingRect(cnts[i])
cv2.drawContours(disp, cnts, i, tuple(clr), 2)
#cv2.rectangle(disp, (x, y), (x + w, y + h), clr, 2)
rect = cv2.minAreaRect(cnts[i])
box = cv2.boxPoints(rect)
box = np.int0(box)
#cv2.drawContours(disp, [box], 0, tuple(clr), 2)
cv2.imshow('disp', disp)
cv2.waitKey()
for c in cnts:
eps = 0.045 * cv2.arcLength(c, False)
approx = cv2.approxPolyDP(c, eps, True)
if len(approx) > 3:
cv2.drawContours(img, [c], 0, (0, 255, 0), 2)
cv2.imshow('img', img)
cv2.waitKey()
def getContours(img):
contours, hierarchy = cv.findContours(img, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_NONE)
print('hierarchy : ', hierarchy)
for cnt in contours:
x, y, w, h = cv.boundingRect(cnt)
print(x, y, w, h)
epsilon = 0.02 * cv.arcLength(cnt, True)
approx = cv.approxPolyDP(cnt, epsilon, True)
print(len(approx))
cv.drawContours(img_color_approx, [approx], 0, (0, 255, 255), 2)
cv.imshow("result approx", img_color_approx)
def getContours(_img):
rects = []
contours, hierarchy = cv2.findContours(_img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for cnt in contours:
area = cv2.contourArea(cnt)
if area > 500:
cnt_length = cv2.arcLength(cnt, True)
cnt_vertexes_approx = cv2.approxPolyDP(cnt, 0.02 * cnt_length,
True)
rects.append(cv2.boundingRect(cnt_vertexes_approx))
return rects
def biggestContour(contours):
biggest = np.array([])
max_area = 0
for i in contours:
area = cv2.contourArea(i)
if area > 5000:
peri = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, 0.02 * peri, True)
if area > max_area and len(approx) == 4:
biggest = approx
max_area = area
return biggest, max_area
def getBiggestcontour(contours):
biggest = np.array([])
max_area = 0
for contour in contours:
area = cv2.contourArea(contour)
if area > 2000:
peri = cv2.arcLength(contour, True)
shape = cv2.approxPolyDP(contour, 0.02 * peri, True)
if area > max_area and len(shape) == 4:
biggest = shape
max_area = area
return biggest, max_area
def getContours(self, mask):
contours, heirarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
x, y, w, h = 0, 0, 0, 0
for cnt in contours:
area = cv2.contourArea(cnt)
if area > 200:
# cv2.drawContours(self.imgResult, cnt, -1, (255, 0, 0), 2)
perimeter = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.02 * perimeter, True)
x, y, w, h = cv2.boundingRect(approx)
return x + w // 2, y
def getContour(img):
contours,hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for cntr in contours:
area = cv2.contourArea(cntr)
if area > 300:
cv2.drawContours(imgContour, cntr, -1, (0,255,0), 1)
perimeter = cv2.arcLength(cntr, True)
approx = cv2.approxPolyDP(cntr, 0.01*perimeter, True)
x,y,w,h = cv2.boundingRect(approx)
points.append([x+w//2, y])
cv2.circle(imgContour, (x+w//2, y), 10, (255, 0, 0), cv2.FILLED)
def GetCoords():
COORDSTOSEND = []
# Reading image
font = cv2.FONT_HERSHEY_COMPLEX
img2 = cv2.imread('PlayerTank.png', cv2.IMREAD_COLOR)
# Reading same image in another
# variable and converting to gray scale.
img = cv2.imread('PlayerTank.png', cv2.IMREAD_GRAYSCALE)
# Converting image to a binary image
# ( black and white only image).
_, threshold = cv2.threshold(img, 110, 255, cv2.THRESH_BINARY)
# Detecting contours in image.
contours, _ = cv2.findContours(threshold, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
tankNum = 0
# Going through every contours found in the image.
for cnt in contours:
data = []
approx = cv2.approxPolyDP(cnt, 0.009 * cv2.arcLength(cnt, True), True)
# draws boundary of contours.
#cv2.drawContours(img2, [approx], 0, (0, 0, 255), 5)
# Used to flatted the array containing
# the co-ordinates of the vertices.
n = approx.ravel()
i = 0
for j in n:
#delete x=j if it causes issues.
x = j
if (i % 2 == 0):
x = n[i]
y = n[i + 1]
data.append((x, y))
string = str(x) + " " + str(y)
i = i + 1
#print(data)
x, y = np.mean(data, axis=0)
string = str(x) + " " + str(y)
if y < 650:
print(string)
COORDSTOSEND.append((x, y))
cv2.putText(img2, str(tankNum), (int(x), int(y)), font, 4,
(0, 255, 0))
tankNum = tankNum + 1
cv2.drawContours(img2, [approx], 0, (0, 0, 255), 5)
# Showing the final image.
#cv2.imshow('image2', img2)
cv2.imwrite('ImageWithPlayerCoords.png', img2)
# Exiting the window if 'q' is pressed on the keyboard.
#if cv2.waitKey(0) & 0xFF == ord('q'):
# cv2.destroyAllWindows()
return COORDSTOSEND
