def flank_seperation(image):
"""
Flank area
:param:
:return:
"""
color_img = image.copy()
green_channel = color_img[:, :, 1]
show_image("A2_green_channel", green_channel)
image_new = green_channel.copy()
green_channel = cv2.medianBlur(green_channel, 39)
show_image("A2_blur", green_channel)
image_new[np.where(green_channel >= 180)] = [0]
show_image("A3_patch1", image_new)
color_img[np.where(green_channel >= 180)] = [0, 0, 0]
show_image("A4_patch2", color_img)
crop_img, _ = crop.crop_radial_arc_two_centres(color_img,
centre_x1=400,
centre_y1=-320,
centre_x2=400,
centre_y2=-200,
radius1=500,
radius2=650,
theta1=235,
theta2=310)
show_image("A5_crop", crop_img)
return
def teeth_seperation(image):
num = 0
color_img = image.copy()
#gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)
#save_image("A1_gray",gray_img)
green_channel = color_img[:, :, 1]
save_image("A2_green_a", green_channel)
image_bk = green_channel.copy()
green_channel = cv2.medianBlur(green_channel, 21)
save_image("A2_green_blur", green_channel)
img1, _ = crop.crop_radial_arc_two_centres(green_channel,
centre_x1=400,
centre_y1=-270,
centre_x2=400,
centre_y2=-280,
radius1=500,
radius2=650,
theta1=230,
theta2=310)
img1_bk = img1.copy()
save_image("A3_crop", img1)
image_bk[np.where(img1 < 200)] = [0]
save_image("A4_binary", image_bk)
time2 = time.time()
return
def teeth_seperation_method2(original_img):
new_img = original_img.copy()
gray_img = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray_img, 3)
original_image = cv2.equalizeHist(blur)
gray_image = original_image.copy()
pos = np.where(gray_image < 180)
gray_image[pos] = [0]
pos = np.where(gray_image > 180)
gray_image[pos] = [255]
gray_image, _ = crop.crop_radial_arc_two_centres(gray_image,
centre_x1=400,
centre_y1=-270,
centre_x2=400,
centre_y2=-280,
radius1=450,
radius2=650,
theta1=230,
theta2=310)
# showimage("gray_imge", gray_image)
contours, hierarchy = cv2.findContours(gray_image, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)[-2:]
for cnt in contours:
contourArea = cv2.contourArea(cnt)
area = cv2.contourArea(cnt)
if area < 10000 or area > 100000:
continue
x, y, w, h = cv2.boundingRect(cnt)
ROI = new_img[y:y + h, x:x + w]
cv2.namedWindow("Largest Contour", cv2.WINDOW_NORMAL)
cv2.imshow("Largest Contour", ROI)
cv2.waitKey(0)
def teeth_seperation_method1(color_image):
"""
teeth seperation
:param image:
:return:
"""
gray_img = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray_img, 3)
original_image = cv2.equalizeHist(blur)
gray_image = original_image.copy()
pos = np.where(gray_image < 180)
gray_image[pos] = [0]
pos = np.where(gray_image > 180)
gray_image[pos] = [255]
gray_image, _ = crop.crop_radial_arc_two_centres(gray_image,
centre_x1=400,
centre_y1=-270,
centre_x2=400,
centre_y2=-280,
radius1=450,
radius2=650,
theta1=230,
theta2=310)
# showimage("gray_imge", gray_image)
contours, hierarchy = cv2.findContours(gray_image, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)[-2:]
num = 0
if contours:
for c in contours:
area = cv2.contourArea(c)
if area < 10000 or area > 100000:
continue
num += 1
print(" tooth count: " + str(num) + "\t area: " + str(area))
rect = cv2.minAreaRect(c)
width = int(rect[1][0])
height = int(rect[1][1])
angle = rect[2]
print("\t\t\t\t", angle)
box = cv2.boxPoints(rect)
box = np.int0(box)
M = cv2.moments(box)
if M["m00"] != 0:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
cX, cY = 0, 0
image_patch = sub_image(color_image, (cX, cY), angle + 90, height,
width)
showimage('A1_original_img_' + str(num), image_patch)
# blue_img = image_patch[:,:,0]
# showimage("processed_img" + str(num), blue_img)
# dent_detection(blue_img, num, angle)
return 'Teeth seperated successfully'
def teeth_seperation(image):
global threshold_value
threshold_value = 180
color_img = image.copy()
green_channel = color_img[:, :, 1]
save_image("A2_green_a", green_channel)
image_bk = green_channel.copy()
green_channel = cv2.medianBlur(green_channel, 21)
save_image("A2_green_blur", green_channel)
img1, _ = crop.crop_radial_arc_two_centres(green_channel,
centre_x1=400,
centre_y1=-270,
centre_x2=400,
centre_y2=-280,
radius1=500,
radius2=650,
theta1=230,
theta2=310)
img1_bk = img1.copy()
save_image("A3_crop", img1)
image_bk[np.where(img1 <= threshold_value)] = [0]
save_image("A4_binary", image_bk)
find_contour(image_bk)
time2 = time.time()
# find_teeth_freq(img1_bk)
#find_teeth(img1,img1_bk)
print("time taken for freq:", abs(time.time() - time2))
return
def process_separation(self, contour=False):
img = self.image
threshold_value, flank_list, teeth_list = 200, [], []
blank_img_flank = np.zeros_like(img)
blank_img_teeth = np.zeros_like(img)
img_blur = cv2.medianBlur(img, 29)
crop_img, _ = crop.crop_radial_arc_two_centres(img_blur,
centre_x1=400,
centre_y1=-320,
centre_x2=400,
centre_y2=-150,
radius1=500,
radius2=510,
theta1=230,
theta2=310)
green_channel = crop_img[:, :, 1]
cv2.imwrite("analysis/A2_blur.jpg", green_channel)
pos1 = np.where(
np.logical_and(green_channel <= threshold_value,
green_channel > 0))
pos2 = np.where(green_channel >= threshold_value)
blank_img_flank[pos1] = img[pos1]
blank_img_teeth[pos2] = img[pos2]
if contour:
flank_list = self.__find_contour(col_image=blank_img_flank, type=1)
teeth_list = self.__find_contour(col_image=blank_img_teeth, type=0)
return flank_list, teeth_list
flank_list.append(blank_img_flank)
teeth_list.append(blank_img_teeth)
return flank_list, teeth_list
def triangle_seperation(gray_image1):
"""
triangle seperation
:param image:
:return:
"""
# showimage("gray_imge", gray_image)
cv2.imshow("Gray", gray_image1)
# ret, gray_image = cv2.threshold(gray_image1, 50, 255,cv2.THRESH_BINARY_INV)
# ret, gray_image = cv2.threshold(gray_image1, 60, 255, 0)
# gray_image = gray_image1.copy()
gray_image = np.zeros(gray_image1.shape, dtype=np.uint8)
showimage("black image", gray_image)
# pos = np.where(gray_image1 < 80)
# gray_image[pos] = [255]
pos = np.where(gray_image1 < 80)
gray_image[pos] = [255]
print(gray_image.shape)
showimage("gray_imge", gray_image)
gray_image, _ = crop.crop_radial_arc_two_centres(gray_image,
centre_x1=390,
centre_y1=-80,
centre_x2=380,
centre_y2=-90,
radius1=550,
radius2=675,
theta1=230,
theta2=310)
showimage("gray_image_triangle", gray_image)
contours, hierarchy = cv2.findContours(gray_image, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)[-2:]
num = 0
if contours:
for c in contours:
area = cv2.contourArea(c)
if area < 100 or area > 100000:
continue
num += 1
print(" tooth count: " + str(num) + "\t area: " + str(area))
x, y, w, h = cv2.boundingRect(c)
img = cv2.rectangle(gray_image1, (x, y), (x + w, y + h),
(255, 0, 0), 2)
showimage("rectangle", img)
rect = cv2.minAreaRect(c)
rows, cols = gray_image.shape
width = int(rect[1][0])
height = int(rect[1][1])
center = rect[0]
angle = rect[2]
print("\t\t\t\t", angle)
box = cv2.boxPoints(rect)
box = np.int0(box)
text = str(num)
col1 = (0, 0, 0)
rot = cv2.getRotationMatrix2D(center, angle - 90, 1)
M = cv2.moments(box)
if M["m00"] != 0:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
cX, cY = 0, 0
image_patch = sub_image(gray_image1, (cX, cY), angle + 90, height,
width)
showimage('A1_original_img_' + str(num), image_patch)
# blue_img = image_patch[:,:,0]
# showimage("processed_img" + str(num), blue_img)
# dent_detection(blue_img, num, angle)
return 'Triangles separated successfully'
def teeth_seperation(color_image):
"""
teeth seperation
:param image:
:return:
"""
# gray_img = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
# blur = cv2.medianBlur(gray_img, 3)
# original_image = cv2.equalizeHist(blur)
# gray_image = original_image.copy()
# pos = np.where(gray_image < 170)
# gray_image[pos] = [0]
# pos = np.where(gray_image > 170)
# gray_image[pos] = [255]
gray = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
ret, gray_image = cv2.threshold(gray, 170, 255, 0)
showimage("gray_imge", gray_image)
# gray_image, _ = crop.crop_radial_arc_two_centres(gray_image, centre_x1=400, centre_y1=-270,
# centre_x2=400, centre_y2=-280, radius1=450, radius2=650,
# theta1=230,
# theta2=310)
gray_image, _ = crop.crop_radial_arc_two_centres(gray_image,
centre_x1=340,
centre_y1=-210,
centre_x2=340,
centre_y2=-280,
radius1=450,
radius2=675,
theta1=230,
theta2=310)
showimage("gray_imge_teeth", gray_image)
# break
contours, hierarchy = cv2.findContours(gray_image, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)[-2:]
num = 0
if contours:
for c in contours:
area = cv2.contourArea(c)
if area < 3000 or area > 100000:
continue
num += 1
print(" tooth count: " + str(num) + "\t area: " + str(area))
x, y, w, h = cv2.boundingRect(c)
# saylee kanitkar
gray_three = cv2.merge([gray_image, gray_image, gray_image])
img = cv2.rectangle(gray_three, (x, y), (x + w, y + h),
(255, 0, 0), 2)
showimage("rectangle", img)
rect = cv2.minAreaRect(c)
rows, cols = gray_image.shape
width = int(rect[1][0])
height = int(rect[1][1])
center = rect[0]
angle = rect[2]
print("\t\t\t\t", angle)
box = cv2.boxPoints(rect)
box = np.int0(box)
text = str(num)
col1 = (0, 0, 0)
rot = cv2.getRotationMatrix2D(center, angle - 90, 1)
M = cv2.moments(box)
if M["m00"] != 0:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
cX, cY = 0, 0
image_patch = sub_image(color_image, (cX, cY), angle + 90, height,
width)
showimage('A1_original_img_' + str(num), image_patch)
blue_img = image_patch[:, :, 0]
showimage("processed_img" + str(num), blue_img)
# dent_detection(blue_img, num, angle)
return 'Teeth seperated successfully'
def between_flank_part(color_image):
"""
:param color_image:
:return:
"""
showimage("color_img", color_image)
gray_img = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray_img, 9)
original_image = cv2.equalizeHist(blur)
gray_image = original_image.copy()
showimage('gray', gray_image)
pos = np.where(gray_image < 180)
gray_image[pos] = [0]
showimage('gray_image', gray_image)
crop1, _ = crop.crop_radial_arc_two_centres(gray_image,
centre_x1=400,
centre_y1=-300,
centre_x2=400,
centre_y2=-170,
radius1=410,
radius2=610,
theta1=230,
theta2=310)
showimage('crop1', crop1)
contours, hierarchy = cv2.findContours(crop1, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)[-2:]
n = 0
if contours:
for c in contours:
area = cv2.contourArea(c)
if area < 3000 or area > 15000:
continue
x, y, w, h = cv2.boundingRect(c)
# saylee kanitkar
gray_three = cv2.merge([crop1, crop1, crop1])
img = cv2.rectangle(gray_three, (x, y), (x + w, y + h),
(255, 0, 0), 2)
showimage("rectangle", img)
n += 1
rect = cv2.minAreaRect(c)
rows, cols = gray_image.shape
width = int(rect[1][0])
print(" tooth count: " + str(n) + "\t area: " + str(area))
height = int(rect[1][1])
center = rect[0]
angle = rect[2]
box = cv2.boxPoints(rect)
box = np.int0(box)
rot = cv2.getRotationMatrix2D(center, angle - 90, 1)
img = cv2.warpAffine(color_image, rot, (rows, cols))
M = cv2.moments(box)
if M["m00"] != 0:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
cX, cY = 0, 0
image_patch = sub_image(color_image, (cX, cY), angle + 90, height,
width)
showimage('original_img' + str(n), image_patch)