def step2(cell_id):
print("============Step 2 Start============")
img = cv.imread(
"G:\\2020summer\\Project\\Cell_classfication_1.0.0\\temp_1.bmp")
img_result = img.copy()
#img=cv.cvtColor(img,cv.COLOR_BGR2BGRA)
#-----preprocess-----
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
#cv.imshow("gray", gray)
gauss = cv.GaussianBlur(gray, (5, 5), 5)
#cv.imshow("gauss1",gauss)
#print("gauss_test: ",gauss[361][616])
'''
x_sample = : 616
y_sample = : 361
'''
img_shape = gauss.shape
# read from local
f = open("G:\\2020summer\\Project\\Cell_classfication_1.0.0\\dict.txt",
'r')
dict_ = eval(f.read())
f.close()
# distance from single point to center
distance_from_single_point_to_center_list = []
for m in range(cell_id, cell_id + 1):
x_sample = dict_[m][0]
y_sample = dict_[m][1]
angle_temp_list = angle_round(x_sample, y_sample, 65) # 第三个参数为圆的半径
for i in range(1, 73):
x1 = angle_temp_list[i - 1][0]
y1 = angle_temp_list[i - 1][1]
cx = x_sample
cy = y_sample
temp_list = pixel_between_two_points(cx, round(x1), cy, round(y1))
#print(len(temp_list))
ray_lenth = 0
compare_distance_value = 0
compare_color_value = 255
color_hist = []
#单条射线的所有点，颜色深度的集合，找出前三名
color_deep_rank = {}
for m in range(0, len(temp_list)):
x_temp = temp_list[m][0]
y_temp = temp_list[m][1]
single_lenth = cell_wall_ray_lenth(cx, cy, x_temp, y_temp)
color_deep_rank[y_temp, x_temp] = gauss[y_temp][x_temp]
sorted_color_deep_rank = sorted(color_deep_rank.items(),
key=lambda kv: (kv[1], kv[0]),
reverse=False)
if gauss[y_temp][x_temp] <= compare_color_value:
compare_color_value = gauss[y_temp][x_temp]
compare_distance_valuevalue = single_lenth
x_final = x_temp
y_final = y_temp
else:
pass
#==hist graph
color_hist.append(gauss[y_temp][x_temp])
#print("**************** test: compare_color_value: ", compare_color_value,"dic rank: ", sorted_color_deep_rank[0],sorted_color_deep_rank[1],sorted_color_deep_rank[2])
#plt.plot(color_hist, color="black")
#plt.show()
cv.circle(img, (round(x_final), round(y_final)), 1, (0, 0, 255),
-1)
#cv.circle(img, (round(sorted_color_deep_rank[1][0][1]), round(sorted_color_deep_rank[1][0][0])), 1, (0, 255, 255), -1)
#cv.circle(img, (round(sorted_color_deep_rank[2][0][1]), round(sorted_color_deep_rank[2][0][0])), 1, (0, 255, 255), -1)
#cv.circle(img, (round(sorted_color_deep_rank[3][0][1]), round(sorted_color_deep_rank[3][0][0])), 1,(0, 255, 255), -1)
#cv.circle(img, (round(sorted_color_deep_rank[4][0][1]), round(sorted_color_deep_rank[4][0][0])), 1,(0, 255, 255), -1)
#fixed_data_list = un_angle_round(x_sample,y_sample,fixed_data)
#cv.circle(img, (round(fixed_data_list[i-1][0]), round(fixed_data_list[i-1][1])), 1, (0, 255, 255), -1)
#cv.circle(img, (round(x1), round(y1)), 1, (255, 0, 255), -1) #半径显示
#distance test and upload
distance_from_single_point_to_center_list.append(
distance(x_final, y_final, cx, cy))
#<data_clean
# data_clean>
#< list save
file = open('test_list.txt', 'w')
for fp in distance_from_single_point_to_center_list:
file.write(str(fp))
file.write('\n')
file.close()
#list save >
from k_means_1D import k_means_1d_def
#temp_list_1=k_means_1d_def(3,30)[2]
#plt.plot([temp_list_1[0]]*len(a), color="red", linestyle='--')
#plt.plot([temp_list_1[1]] * len(a), color="red", linestyle='--')
#plt.show()
#cv.imshow("img_test_round", img)
cv.imwrite(
"G:\\2020summer\\Project\\Cell_classfication_1.0.0\\step2_output.bmp",
img)
print("============Step 2 End / Dataset Saved============")
cv.waitKey()
def step5(cell_id, total_cells_number, output_not_circle):
img = cv.imread("bin\\temp_1.bmp")
if cell_id == 1:
cv.imwrite("bin\\output\\temp_display.bmp", img)
display = cv.imread("bin\\output\\temp_display.bmp")
# -----preprocess-----
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# cv.imshow("gray", gray)
gauss = cv.GaussianBlur(gray, (5, 5), 5)
img_shape = gauss.shape
# read from local
f = open("bin\\dict.txt", 'r')
dict_ = eval(f.read())
f.close()
#print("read from local : ", dict_)
# distance from single point to center
distance_from_single_point_to_center_list = []
for m in range(cell_id, cell_id + 1): # 对某些点进行测试# 28上一次测试的
# <input
file1 = open('bin\\data_smooth_output.txt', 'r')
fixed_data = [float(x.strip()) for x in file1]
file1.close()
# input>
x_sample = dict_[m][0]
y_sample = dict_[m][1]
angle_temp_list = angle_round(x_sample, y_sample, 65) # 第三个参数为圆的半径
for i in range(1, 73):
x1 = angle_temp_list[i - 1][0]
y1 = angle_temp_list[i - 1][1]
cx = x_sample
cy = y_sample
temp_list = pixel_between_two_points(cx, round(x1), cy, round(y1))
ray_lenth = 0
compare_distance_value = 0
compare_color_value = 255
color_hist = []
# 单条射线的所有点，颜色深度的集合，找出前三名
color_deep_rank = {}
for m in range(0, len(temp_list)):
x_temp = temp_list[m][0]
y_temp = temp_list[m][1]
single_lenth = cell_wall_ray_lenth(cx, cy, x_temp, y_temp)
color_deep_rank[y_temp, x_temp] = gauss[y_temp][x_temp]
sorted_color_deep_rank = sorted(color_deep_rank.items(),
key=lambda kv: (kv[1], kv[0]),
reverse=False)
if gauss[y_temp][x_temp] <= compare_color_value:
compare_color_value = gauss[y_temp][x_temp]
compare_distance_valuevalue = single_lenth
x_final = x_temp
y_final = y_temp
else:
pass
# ==hist graph
color_hist.append(gauss[y_temp][x_temp])
#print("**************** test: compare_color_value: ", compare_color_value, "dic rank: ",sorted_color_deep_rank[0], sorted_color_deep_rank[1], sorted_color_deep_rank[2])
# plt.plot(color_hist, color="black")
# plt.show()
#cv.circle(display, (round(x_final), round(y_final)), 1, (0, 0, 255), -1)
# cv.circle(img, (round(sorted_color_deep_rank[1][0][1]), round(sorted_color_deep_rank[1][0][0])), 1, (0, 255, 255), -1)
# cv.circle(img, (round(sorted_color_deep_rank[2][0][1]), round(sorted_color_deep_rank[2][0][0])), 1, (0, 255, 255), -1)
fixed_data_list = un_angle_round(x_sample, y_sample, fixed_data)
#print(fixed_data_list)
cv.circle(display, (round(
fixed_data_list[i - 1][0]), round(fixed_data_list[i - 1][1])),
1, (0, 255, 0), -1)
#cv.circle(display, (round(x1), round(y1)), 1, (255, 0, 255), -1) # 半径显示
# distance test and upload
distance_from_single_point_to_center_list.append(
distance(x_final, y_final, cx, cy))
standard_r = math.ceil(mean(fixed_data))
standard_error = 0
for i in range(0, 72):
standard_error += abs(fixed_data[i] - standard_r)
area_calculate_from_points(fixed_data_list)
#holo_area_calculate_from_points(fixed_data_list,gray,200)
plt.show()
#mean of r
cv.circle(display, (x_sample, y_sample), math.ceil(standard_r),
(0, 0, 255), 0)
#print("not circle degree is: ",format(((standard_error/72)/standard_r),'.2%'))
output_not_circle.append(round(((standard_error / 72) / standard_r)))
#=========nucleus=======
file1 = open('bin\\area_of_nucleus.txt', 'r')
dataset1 = [float(x.strip()) for x in file1]
file1.close()
# print("The area of cell nucleus is: ",round(dataset1[cell_id-1]))
if cell_id <= total_cells_number:
cv.putText(display, "Chromophobe Kidney Cancer Test", (80, 30),
cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
cv.putText(display, "- If the Result Value is less than 0,",
(80, img.shape[0] - 110), cv.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 0), 1)
cv.putText(
display,
"then there is a high probability that the image is Chromophobe.",
(80, img.shape[0] - 90), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0),
1)
cv.putText(display, "- If the non-circle value is less than 0.24,",
(80, img.shape[0] - 70), cv.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 0), 1)
cv.putText(
display,
"then there is a high probability that the image is Chromophobe.",
(80, img.shape[0] - 50), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0),
1)
cv.putText(display, "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
(80, img.shape[0] - 30), cv.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 0), 1)
#cv.imshow("step4 output", display)
cv.imwrite("bin\\output\\temp_display.bmp", display)
cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
except:
pass
if counter_number == 40:
x1 = cX
y1 = cY
if counter_number == 36:
x2 = cX
y2 = cY
#cv.drawContours(img_masked, [cnts[i]], -1, (255, 255, 255), -1)#mask contours
#-----put Text-----
print("total cells number : ", counter_number)
cv.line(img_masked, (x1, y1), (x2, y2), (0, 0, 255), 2)
list_of_two_points = pixel_between_two_points(x1, x2, y1, y2)
print(list_of_two_points)
#-----output information on the line
height_of_two_points = []
height_of_two_points_B = []
height_of_two_points_G = []
height_of_two_points_R = []
for m in range(0, len(list_of_two_points)):
height = img[list_of_two_points[m][1], list_of_two_points[m][0]]
try:
height_B = img[list_of_two_points[m][1], list_of_two_points[m][0]][0]
height_G = img[list_of_two_points[m][1], list_of_two_points[m][0]][1]
height_R = img[list_of_two_points[m][1], list_of_two_points[m][0]][2]
height_of_two_points_B.append(height_B)
def step1():
cell_area_hist_list = []
print("============Step 1 Start============")
# 初始化
path = 'G:\\2020summer\\Project\\Cell_classfication_1.0.0\\Single_Cell'
for i in os.listdir(path):
path_file = os.path.join(path, i)
if os.path.isfile(path_file):
os.remove(path_file)
else:
for f in os.listdir(path_file):
path_file2 = os.path.join(path_file, f)
if os.path.isfile(path_file2):
os.remove(path_file2)
#-----read-----
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename()
#img=cv.imread("G:\\2020summer\\Project\\Chromophobe_dataset1\\4.jpg")
img = cv.imread(file_path)
img_original = img
print("Img size: [Width :", img.shape[0], "]", "[Height :", img.shape[1],
"]")
img = cv.copyMakeBorder(img,
80,
450,
60,
60,
cv.BORDER_CONSTANT,
value=[255, 255, 255])
#img=cv.cvtColor(img,cv.COLOR_BGR2BGRA)
img_masked = img.copy()
img_nucleus_white_img = img.copy()
#-----preprocess-----
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
#cv.imshow("gray", gray)
gauss = cv.GaussianBlur(gray, (5, 5), 5)
#cv.imshow("gauss1",gauss)
ret, thresh = cv.threshold(gauss, 190, 255, 0)
cv.imwrite(
"G:\\2020summer\\Project\\Chromophobe_dataset1\\figure3_left.jpg",
thresh)
#cv.imshow("thresh",thresh)
erode = cv.erode(thresh, None, iterations=1)
#cv.imshow("erode",erode)
#-----remove outlines-----
#cv.imshow("erode",erode)
for i in range(0, img.shape[0]):
for j in range(0, img.shape[1]):
erode[0][j] = 255
#-----find contours-----
cnts, hierarchy = cv.findContours(erode.copy(), cv.RETR_LIST,
cv.CHAIN_APPROX_NONE)
def cnt_area(cnt):
area = cv.contourArea(cnt)
return area
counter_number = 0
location_cells_center = {}
area_of_cells_nucleus = []
Whole_pic_cell_area_ave_percent = []
Whole_pic_cell_color_ave = []
for i in range(0, len(cnts)):
if 250 <= cnt_area(cnts[i]) <= 0.2 * (img.shape[0] * img.shape[1]):
cell_area_hist_list.append(cnt_area(cnts[i]))
#print(cnts[i])
#cell_area_hist_list.append(area_calculate_from_points(cnts[i]))
counter_number += 1
#print(cnts[i])
#print("======")
cv.drawContours(img_masked, cnts[i], -1, (0, 0, 255),
2) #draw contours
cv.drawContours(img_nucleus_white_img, [cnts[i]], -1,
(255, 255, 255), -1) #masked white
M = cv.moments(cnts[i])
#检索每个细胞的内部颜色
x, y, w, h = cv.boundingRect(cnts[i])
#cv.imshow('single_cell', newimage)
cell_area_percent = 0
for row in range(y, y + h):
for col in range(x, x + w):
result = cv.pointPolygonTest(cnts[i], (col, row), False)
if result == -1:
cv.circle(gray, (col, row), 1, 255, -1)
cv.circle(img, (col, row), 1, (255, 255, 255), -1)
cell_area_percent += 1
cv.rectangle(img, (x, y), (x + w, y + h), (153, 153, 0), 1)
newimage_gray = gray[y:y + h, x:x + w]
cv.imwrite(
"G:\\2020summer\\Project\\Cell_classfication_1.0.0\\Single_Cell\\"
+ str(counter_number) + ".jpg", newimage_gray)
Single_Cell_Color_Distrution = []
for row in range(h):
for col in range(w):
if newimage_gray[row, col] != 255:
Single_Cell_Color_Distrution.append(newimage_gray[row,
col])
'''
plt.hist(Single_Cell_Color_Distrution,bins=50)
plt.title(str(counter_number))
plt.show()
'''
#print("this cell area percent= ",str(cell_area_percent/(w*h)))
numpy.set_printoptions(precision=3)
Whole_pic_cell_area_ave_percent.append(cell_area_percent / (w * h))
Whole_pic_cell_color_ave.append(
numpy.mean(Single_Cell_Color_Distrution))
"""#找出masked细胞内点的坐标
rect = cv.minAreaRect(cnts[i])
cx, cy = rect[0]
box = cv.boxPoints(rect)
box = np.int0(box)
cv.drawContours(img_masked, [box], 0, (0, 0, 255), 2)
#cv.circle(img_masked, (np.int32(cx), np.int32(cy)), 2, (255, 0, 0), 2, 8, 0)
box_gray_color=[]
for by in range(box[2][1],box[0][1]+1):
for bx in range(box[1][0],box[3][0]+1):
#print(bx,by)
#cv.circle(img_masked,(bx, by), 1, (255, 0, 0), 2, 8, 0)
box_gray_color.append(gray[bx,by])
plt.hist(box_gray_color)
plt.hist(box_gray_color,bins=50)
plt.title(str(counter_number))
plt.show()
dist=cv.pointPolygonTest(cnts[i],(50,50),True)
"""
try:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
cv.circle(img_masked, (cX, cY), 3, (255, 255, 255), -1)
cv.putText(img_masked, str(counter_number), (cX - 20, cY - 20),
cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
area_of_cells_nucleus.append(cnt_area(cnts[i]))
location_cells_center[counter_number] = [cX, cY]
except:
pass
if counter_number == 1:
x1 = cX
y1 = cY
if counter_number == 2:
x2 = cX
y2 = cY
if counter_number == 20:
x_sample = cX
y_sample = cY
#cv.drawContours(img_masked, [cnts[i]], -1, (255, 255, 255), -1)#mask contours
print("Whole pic average cell nucleus area percent: ",
Whole_pic_cell_area_ave_percent)
print("Whole pic average cell nucleus area percent_ave: ",
numpy.mean(Whole_pic_cell_area_ave_percent))
print("Whole pic average cell nucleus color deep percent: ",
Whole_pic_cell_color_ave)
print("Whole pic average cell nucleus color deep percent_ave: ",
numpy.mean(Whole_pic_cell_color_ave))
cv.imshow('single_cell', img)
#-----put Text-----
print("total cells number : ", counter_number)
#cv.line(img_masked, (x1,y1), (x2,y2), (0,0,255), 2)
list_of_two_points = pixel_between_two_points(x1, x2, y1, y2)
#-----output information on the line
height_of_two_points = []
height_of_two_points_B = []
height_of_two_points_G = []
height_of_two_points_R = []
for m in range(0, len(list_of_two_points)):
height = img[list_of_two_points[m][1], list_of_two_points[m][0]]
try:
height_B = img[list_of_two_points[m][1],
list_of_two_points[m][0]][0]
height_G = img[list_of_two_points[m][1],
list_of_two_points[m][0]][1]
height_R = img[list_of_two_points[m][1],
list_of_two_points[m][0]][2]
height_of_two_points_B.append(height_B)
height_of_two_points_G.append(height_G)
height_of_two_points_R.append(height_R)
except:
pass
#print(height)
height_of_two_points.append(height)
img_sample = img.copy()
cv.circle(img_sample, (x_sample, y_sample), 3, (0, 0, 255), -1)
font = cv.FONT_HERSHEY_SIMPLEX
cv.putText(img_sample, "Sample_Point", (x_sample - 20, y_sample - 20),
font, 0.7, (255, 255, 255), 2)
#cv.imshow("img_sample_location_RED_DOT", img_sample)
# save to local
f = open("G:\\2020summer\\Project\\Cell_classfication_1.0.0\\dict.txt",
'w')
f.write(str(location_cells_center))
f.close()
# < list save
file1 = open('area_of_nucleus.txt', 'w')
for fp in area_of_cells_nucleus:
file1.write(str(fp))
file1.write('\n')
file1.close()
# list save >
cv.imwrite("G:\\2020summer\\Project\\Cell_classfication_1.0.0\\temp.bmp",
img_masked)
cv.imwrite("G:\\2020summer\\Project\\Cell_classfication_1.0.0\\temp_1.bmp",
img_nucleus_white_img)
cv.imwrite(
"G:\\2020summer\\Project\\Chromophobe_dataset1\\figure3_right.jpg",
img_masked)
#================hist of cells area==================
#plt.hist(cell_area_hist_list)
#plt.show()
#=================================
#-----
#=================================UI/
cv.putText(img_masked, "Overview", (80, 40), cv.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 0), 2)
image_size_text = "Image size: [Width :" + str(
img_original.shape[0]) + "]" + "[Height :" + str(
img_original.shape[1]) + "]"
cv.putText(img_masked, image_size_text, (80, img.shape[0] - 400),
cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
cv.putText(img_masked, "Total cells number: " + str(counter_number),
(80, img.shape[0] - 350), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0),
2)
cv.putText(img_masked, "Close window to continue",
(80, img.shape[0] - 250), cv.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 0), 1)
#=================================/UI
cv.imshow('img_copy', img_masked)
print("============Step 1 End============")
cv.waitKey()
return counter_number
#input>
for m in range(65,66):#对某些点进行测试# 28上一次测试的
x_sample = dict_[m][0]
y_sample = dict_[m][1]
angle_temp_list = angle_round(x_sample, y_sample, 65) # 第三个参数为圆的半径
for i in range(1,73):
x1=angle_temp_list[i-1][0]
y1 = angle_temp_list[i - 1][1]
cx=x_sample
cy=y_sample
temp_list=pixel_between_two_points(cx,round(x1),cy,round(y1))
ray_lenth = 0
compare_distance_value=0
compare_color_value=255
color_hist=[]
#单条射线的所有点，颜色深度的集合，找出前三名
color_deep_rank={}
for m in range(0,len(temp_list)):
x_temp=temp_list[m][0]
y_temp=temp_list[m][1]
single_lenth=cell_wall_ray_lenth(cx,cy,x_temp,y_temp)
color_deep_rank[y_temp, x_temp] = gauss[y_temp][x_temp]
def step4(cell_id):
display = cv.imread(
"G:\\2020summer\\Project\\Cell_classfication_1.0.0\\temp.bmp")
img = cv.imread(
"G:\\2020summer\\Project\\Cell_classfication_1.0.0\\temp_1.bmp")
img_result = img.copy()
# img=cv.cvtColor(img,cv.COLOR_BGR2BGRA)
# -----preprocess-----
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# cv.imshow("gray", gray)
gauss = cv.GaussianBlur(gray, (5, 5), 5)
img_shape = gauss.shape
# read from local
f = open("G:\\2020summer\\Project\\Cell_classfication_1.0.0\\dict.txt",
'r')
dict_ = eval(f.read())
f.close()
#print("read from local : ", dict_)
# distance from single point to center
distance_from_single_point_to_center_list = []
for m in range(cell_id, cell_id + 1): # 对某些点进行测试# 28上一次测试的
# <input
file1 = open('data_smooth_output.txt', 'r')
fixed_data = [float(x.strip()) for x in file1]
file1.close()
# input>
x_sample = dict_[m][0]
y_sample = dict_[m][1]
angle_temp_list = angle_round(x_sample, y_sample, 65) # 第三个参数为圆的半径
for i in range(1, 73):
x1 = angle_temp_list[i - 1][0]
y1 = angle_temp_list[i - 1][1]
cx = x_sample
cy = y_sample
temp_list = pixel_between_two_points(cx, round(x1), cy, round(y1))
ray_lenth = 0
compare_distance_value = 0
compare_color_value = 255
color_hist = []
# 单条射线的所有点，颜色深度的集合，找出前三名
color_deep_rank = {}
for m in range(0, len(temp_list)):
x_temp = temp_list[m][0]
y_temp = temp_list[m][1]
single_lenth = cell_wall_ray_lenth(cx, cy, x_temp, y_temp)
color_deep_rank[y_temp, x_temp] = gauss[y_temp][x_temp]
sorted_color_deep_rank = sorted(color_deep_rank.items(),
key=lambda kv: (kv[1], kv[0]),
reverse=False)
if gauss[y_temp][x_temp] <= compare_color_value:
compare_color_value = gauss[y_temp][x_temp]
compare_distance_valuevalue = single_lenth
x_final = x_temp
y_final = y_temp
else:
pass
# ==hist graph
color_hist.append(gauss[y_temp][x_temp])
#print("**************** test: compare_color_value: ", compare_color_value, "dic rank: ",sorted_color_deep_rank[0], sorted_color_deep_rank[1], sorted_color_deep_rank[2])
# plt.plot(color_hist, color="black")
# plt.show()
cv.circle(display, (round(x_final), round(y_final)), 1,
(0, 0, 255), -1)
# cv.circle(img, (round(sorted_color_deep_rank[1][0][1]), round(sorted_color_deep_rank[1][0][0])), 1, (0, 255, 255), -1)
# cv.circle(img, (round(sorted_color_deep_rank[2][0][1]), round(sorted_color_deep_rank[2][0][0])), 1, (0, 255, 255), -1)
fixed_data_list = un_angle_round(x_sample, y_sample, fixed_data)
#print(fixed_data_list)
cv.circle(display, (round(
fixed_data_list[i - 1][0]), round(fixed_data_list[i - 1][1])),
1, (0, 255, 0), -1)
#cv.circle(display, (round(x1), round(y1)), 1, (255, 0, 255), -1) # 半径显示
# distance test and upload
distance_from_single_point_to_center_list.append(
distance(x_final, y_final, cx, cy))
standard_r = math.ceil(mean(fixed_data))
standard_error = 0
for i in range(0, 72):
standard_error += abs(fixed_data[i] - standard_r)
area_calculate_from_points(fixed_data_list)
#holo_area_calculate_from_points(fixed_data_list,gray,200)
plt.show()
#mean of r
cv.circle(display, (x_sample, y_sample), math.ceil(standard_r),
(0, 255, 255), 0)
print("not circle degree is: ",
format(((standard_error / 72) / standard_r), '.2%'))
#=========nucleus=======
file1 = open('area_of_nucleus.txt', 'r')
dataset1 = [float(x.strip()) for x in file1]
file1.close()
print("The area of cell nucleus is: ", round(dataset1[cell_id - 1]))
cv.putText(display, "Chromophobe Kidney Cancer Test", (80, 30),
cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 1)
cv.putText(display, time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
(80, 60), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
cv.putText(
display, "The area of this cell is:" +
str(area_calculate_from_points(fixed_data_list)),
(80, (img.shape[1] - 220)), cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 1)
cv.putText(
display, "not circle degree is: " +
str(format(((standard_error / 72) / standard_r), '.2%')),
(80, img.shape[1] - 200), cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 1)
#cv.imshow("step4 output", display)
cv.imwrite(
"G:\\2020summer\\Project\\Cell_classfication_1.0.0\\output_single\\" +
str(cell_id) + ".bmp", display)
#cv.waitKey()
output1_non_circle_degree = (standard_error / 72) / standard_r
return (output1_non_circle_degree)
def step1(path_slides=''):
cv.destroyAllWindows()
plt.close()
os_path = os.getcwd()
cell_area_hist_list = []
# print("============Step 1 Start============")
# -----read-----
# if read_type==0:
# file_path=filedialog.askopenfilename()
#
# #img=cv.imread("G:\\2020summer\\Project\\Chromophobe_dataset1\\4.jpg")
# else:
# file_path=path2imgs
file_path = path_slides
threshold_value = 190
# threshold_value=threshold_test(file_path)
if not os.path.exists(os_path + '\\bin\\output'):
os.makedirs(os_path + '\\bin\\output')
if not os.path.exists(os_path + '\\bin\\output'):
os.makedirs(os_path + '\\result')
if not os.path.exists(os_path + '\\bin\\output'):
os.mkdir(os_path + '\\output_single')
if not os.path.exists(os_path + '\\bin\\output'):
os.makedirs(os_path + '\\result_pdf')
img = cv.imread(file_path)
img_original = img
# print("Img size: [Width :",img.shape[0],"]","[Height :",img.shape[1],"]")
if img.shape[0] < 450 or img.shape[1] < 600:
img = cv.copyMakeBorder(img,
80,
450,
360,
360,
cv.BORDER_CONSTANT,
value=[255, 255, 255])
else:
img = cv.copyMakeBorder(img,
80,
450,
60,
60,
cv.BORDER_CONSTANT,
value=[255, 255, 255])
img_masked = img.copy()
img_nucleus_white_img = img.copy()
# -----preprocess-----
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# cv.imshow("gray", gray)
gauss = cv.GaussianBlur(gray, (5, 5), 5)
# cv.imshow("gauss1",gauss)
ret, thresh = cv.threshold(gauss, threshold_value, 255, 0)
# cv.imwrite("bin\\figure3_left.jpg",thresh)
# cv.imshow("thresh",thresh)
erode = cv.erode(thresh, None, iterations=1)
# cv.imshow("erode",erode)
# -----remove outlines-----
# cv.imshow("erode",erode)
for i in range(0, img.shape[0]):
for j in range(0, img.shape[1]):
erode[0][j] = 255
# -----find contours-----
img, cnts, hierarchy = cv.findContours(erode.copy(), cv.RETR_LIST,
cv.CHAIN_APPROX_NONE)
def cnt_area(cnt):
area = cv.contourArea(cnt)
return area
counter_number = 0
location_cells_center = {}
area_of_cells_nucleus = []
Whole_pic_cell_area_ave_percent = []
Whole_pic_cell_color_ave = []
x1 = 1
y1 = 1
x2 = 1
y2 = 1
x_sample = 1
y_sample = 1
for i in tqdm(range(0, len(cnts)), desc='Cell Nucleus Detecting'):
x, y, w, h = cv.boundingRect(cnts[i])
if 250 <= cnt_area(
cnts[i]) <= 0.1 * (img.shape[0] * img.shape[1]) and cnt_area(
cnts[i]) / (w * h) > 0.4:
# print('if true checkpoint')
cell_area_hist_list.append(cnt_area(cnts[i]))
# print(cnts[i])
# cell_area_hist_list.append(area_calculate_from_points(cnts[i]))
counter_number += 1
# print(cnts[i])
# print("======")
cv.drawContours(img_masked, cnts[i], -1, (0, 0, 255),
2) # draw contours
cv.drawContours(img_nucleus_white_img, [cnts[i]], -1,
(255, 255, 255), -1) # masked white
M = cv.moments(cnts[i])
# 检索每个细胞的内部颜色
x, y, w, h = cv.boundingRect(cnts[i])
# cv.imshow('single_cell', newimage)
cell_area_percent = 0
for row in range(y, y + h):
for col in range(x, x + w):
result = cv.pointPolygonTest(cnts[i], (col, row), False)
if result == -1:
cv.circle(gray, (col, row), 1, 255, -1)
cv.circle(img, (col, row), 1, (255, 255, 255), -1)
cell_area_percent += 1
cv.rectangle(img, (x, y), (x + w, y + h), (153, 153, 0), 1)
newimage_gray = gray[y:y + h, x:x + w]
Single_Cell_Color_Distrution = []
for row in range(h):
for col in range(w):
if newimage_gray[row, col] != 255:
Single_Cell_Color_Distrution.append(newimage_gray[row,
col])
# print('Single_Cell_Color_Distrution: ' + str(Single_Cell_Color_Distrution))
'''
plt.hist(Single_Cell_Color_Distrution,bins=50)
plt.title(str(counter_number))
plt.show()
'''
# print("this cell area percent= ",str(cell_area_percent/(w*h)))
np.set_printoptions(precision=3)
Whole_pic_cell_area_ave_percent.append(cnt_area(cnts[i]) / (w * h))
if len(Single_Cell_Color_Distrution) > 0:
Whole_pic_cell_color_ave.append(
np.nanmean(Single_Cell_Color_Distrution))
"""#找出masked细胞内点的坐标 find coordinates of cells in masked image
rect = cv.minAreaRect(cnts[i])
cx, cy = rect[0]
box = cv.boxPoints(rect)
box = np.int0(box)
cv.drawContours(img_masked, [box], 0, (0, 0, 255), 2)
#cv.circle(img_masked, (np.int32(cx), np.int32(cy)), 2, (255, 0, 0), 2, 8, 0)
box_gray_color=[]
for by in range(box[2][1],box[0][1]+1):
for bx in range(box[1][0],box[3][0]+1):
#print(bx,by)
#cv.circle(img_masked,(bx, by), 1, (255, 0, 0), 2, 8, 0)
box_gray_color.append(gray[bx,by])
plt.hist(box_gray_color)
plt.hist(box_gray_color,bins=50)
plt.title(str(counter_number))
plt.show()
dist=cv.pointPolygonTest(cnts[i],(50,50),True)
"""
try:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
cv.circle(img_masked, (cX, cY), 3, (255, 255, 255), -1)
cv.putText(img_masked, str(counter_number), (cX - 20, cY - 20),
cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
area_of_cells_nucleus.append(cnt_area(cnts[i]))
location_cells_center[counter_number] = [cX, cY]
except:
pass
# if counter_number==1:
# x1=cX
# y1=cY
# if counter_number==2:
# x2=cX
# y2=cY
# if counter_number==3:
# x_sample=cX
# y_sample=cY
# cv.drawContours(img_masked, [cnts[i]], -1, (255, 255, 255), -1)#mask contours
ave_cell_nucleus_color = np.nanmean(Whole_pic_cell_color_ave)
# pdb.set_trace()
# print("Whole pic cell nucleus rectango area list: ", Whole_pic_cell_area_ave_percent)
# print("Whole pic average cell nucleus area percent_ave: ", np.nanmean(Whole_pic_cell_area_ave_percent))
# print("Whole pic average cell nucleus color deep percent: ", Whole_pic_cell_color_ave)
# print("Whole pic average cell nucleus color deep percent_ave: ", ave_cell_nucleus_color)
cv.imwrite("bin\\cell_clean.bmp", img)
# -----put Text-----
print("total cells number : ", counter_number)
cv.line(img_masked, (x1, y1), (x2, y2), (0, 0, 255), 2)
list_of_two_points = pixel_between_two_points(x1, x2, y1, y2)
# -----output information on the line
height_of_two_points = []
height_of_two_points_B = []
height_of_two_points_G = []
height_of_two_points_R = []
for m in range(0, len(list_of_two_points)):
height = img[list_of_two_points[m][1], list_of_two_points[m][0]]
try:
height_B = img[list_of_two_points[m][1],
list_of_two_points[m][0]][0]
height_G = img[list_of_two_points[m][1],
list_of_two_points[m][0]][1]
height_R = img[list_of_two_points[m][1],
list_of_two_points[m][0]][2]
height_of_two_points_B.append(height_B)
height_of_two_points_G.append(height_G)
height_of_two_points_R.append(height_R)
except:
pass
# print(height)
height_of_two_points.append(height)
img_sample = img.copy()
cv.circle(img_sample, (x_sample, y_sample), 3, (0, 0, 255), -1)
font = cv.FONT_HERSHEY_SIMPLEX
cv.putText(img_sample, "Sample_Point", (x_sample - 20, y_sample - 20),
font, 0.7, (255, 255, 255), 2)
# save to local
f = open("bin\\dict.txt", 'w')
f.write(str(location_cells_center))
f.close()
# < list save
file1 = open('bin\\area_of_nucleus.txt', 'w')
for fp in area_of_cells_nucleus:
file1.write(str(fp))
file1.write('\n')
file1.close()
# list save >
cv.imwrite("bin\\temp.bmp", img_masked)
cv.imwrite("bin\\temp_1.bmp", img_nucleus_white_img)
cv.imwrite("bin\\figure3_right.jpg", img_masked)
# ================hist of cells area==================
plt.hist(cell_area_hist_list)
# plt.show()
# =================================
# -----
# =================================UI/
cv.putText(img_masked, "Overview", (80, 40), cv.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 0), 2)
image_size_text = "Image size: [Width :" + str(
img_original.shape[0]) + "]" + "[Height :" + str(
img_original.shape[1]) + "]"
cv.putText(img_masked, image_size_text, (80, img.shape[0] - 400),
cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
cv.putText(img_masked, "Total cells number: " + str(counter_number),
(80, img.shape[0] - 350), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0),
2)
Cells_density = ('%.2f' % (10000 * counter_number /
(img.shape[0] * img.shape[1])))
# print("Cells density : ",Cells_density," / 100*100 pixels")
cv.putText(img_masked,
"Cells density : " + str(Cells_density) + " / 100*100 pixels",
(80, img.shape[0] - 300), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0),
2)
cv.putText(img_masked, "Close window to continue",
(80, img.shape[0] - 250), cv.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 0), 1)
# =================================/UI
# cv.imshow("result", img_masked)
# cv.imshow("img_sample_location_RED_DOT", img_sample)
cv.imwrite("bin\\overview_result1.bmp", img_masked)
# print("============Step 1 End============")
# cv.waitKey()
return counter_number, Cells_density, ave_cell_nucleus_color
def step1():
print("============Step 1 Start============")
#-----read-----
img = cv.imread("G:\\2020summer\\Project\\Chromophobe_dataset1\\4.jpg")
img = cv.copyMakeBorder(img,
80,
300,
80,
80,
cv.BORDER_CONSTANT,
value=[255, 255, 255])
#img=cv.cvtColor(img,cv.COLOR_BGR2BGRA)
img_masked = img.copy()
img_nucleus_white_img = img.copy()
#-----preprocess-----
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
#cv.imshow("gray", gray)
gauss = cv.GaussianBlur(gray, (5, 5), 5)
#cv.imshow("gauss1",gauss)
ret, thresh = cv.threshold(gauss, 190, 255, 0)
#cv.imshow("thresh",thresh)
erode = cv.erode(thresh, None, iterations=2)
#cv.imshow("erode",erode)
#-----remove outlines-----
cv.imshow("erode", erode)
for i in range(0, img.shape[0]):
for j in range(0, img.shape[1]):
erode[0][j] = 255
#-----find contours-----
cnts, hierarchy = cv.findContours(erode.copy(), cv.RETR_LIST,
cv.CHAIN_APPROX_NONE)
def cnt_area(cnt):
area = cv.contourArea(cnt)
return area
counter_number = 0
location_cells_center = {}
area_of_cells_nucleus = []
for i in range(0, len(cnts)):
if 250 <= cnt_area(cnts[i]) <= 0.8 * (img.shape[0] * img.shape[1]):
counter_number += 1
#print(cnts[i])
#print("======")
cv.drawContours(img_masked, cnts[i], -1, (0, 0, 255),
2) #draw contours
cv.drawContours(img_nucleus_white_img, [cnts[i]], -1,
(255, 255, 255), -1) #masked white
M = cv.moments(cnts[i])
try:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
cv.circle(img_masked, (cX, cY), 3, (255, 255, 255), -1)
cv.putText(img_masked, str(counter_number), (cX - 20, cY - 20),
cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
area_of_cells_nucleus.append(cnt_area(cnts[i]))
location_cells_center[counter_number] = [cX, cY]
except:
pass
if counter_number == 6:
x1 = cX
y1 = cY
if counter_number == 7:
x2 = cX
y2 = cY
if counter_number == 1:
x_sample = cX
y_sample = cY
#cv.drawContours(img_masked, [cnts[i]], -1, (255, 255, 255), -1)#mask contours
#-----put Text-----
print("total cells number : ", counter_number)
#cv.line(img_masked, (x1,y1), (x2,y2), (0,0,255), 2)
list_of_two_points = pixel_between_two_points(x1, x2, y1, y2)
print(list_of_two_points)
#-----output information on the line
height_of_two_points = []
height_of_two_points_B = []
height_of_two_points_G = []
height_of_two_points_R = []
for m in range(0, len(list_of_two_points)):
height = img[list_of_two_points[m][1], list_of_two_points[m][0]]
try:
height_B = img[list_of_two_points[m][1],
list_of_two_points[m][0]][0]
height_G = img[list_of_two_points[m][1],
list_of_two_points[m][0]][1]
height_R = img[list_of_two_points[m][1],
list_of_two_points[m][0]][2]
height_of_two_points_B.append(height_B)
height_of_two_points_G.append(height_G)
height_of_two_points_R.append(height_R)
except:
pass
#print(height)
height_of_two_points.append(height)
plt.plot(height_of_two_points, color="black")
plt.show()
try:
plt.plot(height_of_two_points_B, color="blue")
plt.plot(height_of_two_points_G, color="green")
plt.plot(height_of_two_points_R, color="red")
plt.savefig(
"G:\\2020summer\\Project\\Cell_classfication_1.0.0\\Step1_output_2.jpg"
)
plt.show()
except:
pass
img_sample = img.copy()
cv.circle(img_sample, (x_sample, y_sample), 3, (0, 0, 255), -1)
font = cv.FONT_HERSHEY_SIMPLEX
cv.putText(img_sample, "Sample_Point", (x_sample - 20, y_sample - 20),
font, 0.7, (255, 255, 255), 2)
#cv.imshow("img_sample_location_RED_DOT", img_sample)
print("x_sample = : ", x_sample) #样本标注点的坐标
print("y_sample = : ", y_sample)
# save to local
f = open("G:\\2020summer\\Project\\Cell_classfication_1.0.0\\dict.txt",
'w')
f.write(str(location_cells_center))
f.close()
print("save dict successfully.")
# < list save
file1 = open('area_of_nucleus.txt', 'w')
for fp in area_of_cells_nucleus:
file1.write(str(fp))
file1.write('\n')
file1.close()
# list save >
#-----
cv.imshow('img_copy', img_masked)
cv.imwrite("G:\\2020summer\\Project\\Cell_classfication_1.0.0\\temp.bmp",
img_masked)
cv.imwrite("G:\\2020summer\\Project\\Cell_classfication_1.0.0\\temp_1.bmp",
img_nucleus_white_img)
#==================================
print("============Step 1 End============")
cv.waitKey()
def step1(findpeaks=None):
cell_area_hist_list = []
print("============Step 1 Start============")
#-----read-----
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename()
#img=cv.imread("G:\\2020summer\\Project\\Chromophobe_dataset1\\4.jpg")
img = cv.imread(file_path)
print("Img size: [Width :", img.shape[0], "]", "[Height :", img.shape[1],
"]")
img = cv.copyMakeBorder(img,
80,
450,
60,
60,
cv.BORDER_CONSTANT,
value=[255, 255, 255])
#img=cv.cvtColor(img,cv.COLOR_BGR2BGRA)
img_masked = img.copy()
img_nucleus_white_img = img.copy()
#-----preprocess-----
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
#cv.imshow("gray", gray)
gauss = cv.GaussianBlur(gray, (5, 5), 5)
#cv.imshow("gauss1",gauss)
ret, thresh = cv.threshold(gauss, 140, 255, 0) #正常图数值140，癌症图190
cv.imwrite(
"G:\\2020summer\\Project\\Chromophobe_dataset1\\figure3_left.jpg",
thresh)
#cv.imshow("thresh",thresh)
erode = cv.erode(thresh, None, iterations=2)
#cv.imshow("erode",erode)
#-----remove outlines-----
#cv.imshow("erode",erode)
for i in range(0, img.shape[0]):
for j in range(0, img.shape[1]):
erode[0][j] = 255
#-----find contours-----
cnts, hierarchy = cv.findContours(erode.copy(), cv.RETR_LIST,
cv.CHAIN_APPROX_NONE)
def cnt_area(cnt):
area = cv.contourArea(cnt)
return area
counter_number = 0
location_cells_center = {}
area_of_cells_nucleus = []
for i in range(0, len(cnts)):
if 250 <= cnt_area(cnts[i]) <= 0.2 * (img.shape[0] * img.shape[1]):
cell_area_hist_list.append(cnt_area(cnts[i]))
#print(cnts[i])
#cell_area_hist_list.append(area_calculate_from_points(cnts[i]))
counter_number += 1
#print(cnts[i])
#print("======")
cv.drawContours(img_masked, cnts[i], -1, (0, 0, 255),
2) #draw contours
cv.drawContours(img_nucleus_white_img, [cnts[i]], -1,
(255, 255, 255), -1) #masked white
M = cv.moments(cnts[i])
#找出masked细胞内点的坐标
'''
rect = cv.minAreaRect(cnts[i])
cx, cy = rect[0]
box = cv.boxPoints(rect)
box = np.int0(box)
#cv.drawContours(img_masked, [box], 0, (0, 0, 255), 2)
#cv.circle(img_masked, (np.int32(cx), np.int32(cy)), 2, (255, 0, 0), 2, 8, 0)
box_gray_color=[]
for by in range(box[2][1],box[0][1]+1):
for bx in range(box[1][0],box[3][0]+1):
#print(bx,by)
#cv.circle(img_masked,(bx, by), 1, (255, 0, 0), 2, 8, 0)
box_gray_color.append(gray[bx,by])
#plt.hist(box_gray_color)
plt.hist(box_gray_color,bins=50)
plt.title(str(counter_number))
#plt.show()
dist=cv.pointPolygonTest(cnts[i],(50,50),True)
'''
try:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
cv.circle(img_masked, (cX, cY), 3, (255, 255, 255), -1)
cv.putText(img_masked, str(counter_number), (cX - 20, cY - 20),
cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
area_of_cells_nucleus.append(cnt_area(cnts[i]))
location_cells_center[counter_number] = [cX, cY]
except:
pass
if counter_number == 176:
x1 = cX
y1 = cY
if counter_number == 181:
x2 = cX
y2 = cY
if counter_number == 1:
x_sample = cX
y_sample = cY
#cv.drawContours(img_masked, [cnts[i]], -1, (255, 255, 255), -1)#mask contours
#-----put Text-----
print("total cells number : ", counter_number)
cv.line(img_masked, (x1, y1), (x2, y2), (0, 0, 255), 2)
list_of_two_points = pixel_between_two_points(x1, x2, y1, y2)
print(list_of_two_points)
#-----output information on the line
height_of_two_points_Gray = []
height_of_two_points_B = []
height_of_two_points_G = []
height_of_two_points_R = []
for m in range(0, len(list_of_two_points)):
height_Gray = gray[list_of_two_points[m][1], list_of_two_points[m][0]]
try:
height_B = img[list_of_two_points[m][1],
list_of_two_points[m][0]][0]
height_G = img[list_of_two_points[m][1],
list_of_two_points[m][0]][1]
height_R = img[list_of_two_points[m][1],
list_of_two_points[m][0]][2]
height_of_two_points_B.append(height_B)
height_of_two_points_G.append(height_G)
height_of_two_points_R.append(height_R)
except:
pass
#print(height)
height_of_two_points_Gray.append(height_Gray)
#plt.plot(height_of_two_points_Gray,color="black")
#plt.show()
try:
plt.title("RGB color between")
plt.plot(height_of_two_points_B, color="blue")
plt.plot(height_of_two_points_G, color="green")
plt.plot(height_of_two_points_R, color="red")
plt.tick_params(labelsize=13)
plt.savefig(
"G:\\2020summer\\Project\\Cell_classfication_1.0.0\\Step1_output_2.jpg"
)
plt.show()
plt.title("gray color between")
plt.tick_params(labelsize=13)
plt.plot(height_of_two_points_Gray, color="black")
plt.show()
except:
pass
import scipy.signal
#R##################
plt.title("RGB color between")
plt.plot(height_of_two_points_B, color="blue")
plt.plot(height_of_two_points_G, color="green")
plt.plot(height_of_two_points_R, color="red")
print('Detect peaks without any filters.')
height_of_two_points_Gray_reverse = [
255 - i for i in height_of_two_points_R
]
print('Detect peaks with minimum height and distance filters.')
indexes, _ = scipy.signal.find_peaks(height_of_two_points_Gray_reverse,
height=7,
distance=2.1)
print('Peaks are: %s' % (indexes))
#clean
indexes_1 = list(indexes)
indexes_2 = []
for i in indexes_1:
if i < 0.25 * len(height_of_two_points_Gray_reverse) or i > 0.75 * len(
height_of_two_points_Gray_reverse):
indexes_2.append(i)
indexes_1.remove(i)
print(i, " is removed")
print('Peaks fixed are: %s' % (indexes_1))
if len(indexes_1) != 0:
temp_list = [
abs(i - (len(height_of_two_points_Gray) / 2)) for i in indexes_1
]
indexes_1 = [indexes_1[temp_list.index(min(temp_list))]]
#plt.title("gray color between_reverse_marked")
#plt.plot(height_of_two_points_Gray, color="black")
plt.plot(indexes_1, [height_of_two_points_R[i] for i in indexes_1],
marker="8",
markersize=10,
color='red',
ls="")
#plt.plot(indexes_2, [height_of_two_points_Gray[i] for i in indexes_2], marker="8", markersize=10, color='green',ls="")
else:
print("Warning! Cell boundary detection failed!")
# G##################
print('Detect peaks without any filters.')
height_of_two_points_Gray_reverse = [
255 - i for i in height_of_two_points_G
]
print('Detect peaks with minimum height and distance filters.')
indexes, _ = scipy.signal.find_peaks(height_of_two_points_Gray_reverse,
height=7,
distance=2.1)
print('Peaks are: %s' % (indexes))
# clean
indexes_1 = list(indexes)
indexes_2 = []
for i in indexes_1:
if i < 0.25 * len(height_of_two_points_Gray_reverse) or i > 0.75 * len(
height_of_two_points_Gray_reverse):
indexes_2.append(i)
indexes_1.remove(i)
print(i, " is removed")
print('Peaks fixed are: %s' % (indexes_1))
if len(indexes_1) != 0:
temp_list = [
abs(i - (len(height_of_two_points_Gray) / 2)) for i in indexes_1
]
indexes_1 = [indexes_1[temp_list.index(min(temp_list))]]
#plt.title("gray color between_reverse_marked")
#plt.plot(height_of_two_points_Gray, color="black")
plt.plot(indexes_1, [height_of_two_points_G[i] for i in indexes_1],
marker="8",
markersize=10,
color='green',
ls="")
else:
print("Warning! Cell boundary detection failed!")
# B##################
print('Detect peaks without any filters.')
height_of_two_points_Gray_reverse = [
255 - i for i in height_of_two_points_B
]
print('Detect peaks with minimum height and distance filters.')
indexes, _ = scipy.signal.find_peaks(height_of_two_points_Gray_reverse,
height=7,
distance=2.1)
print('Peaks are: %s' % (indexes))
# clean
indexes_1 = list(indexes)
indexes_2 = []
for i in indexes_1:
if i < 0.25 * len(height_of_two_points_Gray_reverse) or i > 0.75 * len(
height_of_two_points_Gray_reverse):
indexes_2.append(i)
indexes_1.remove(i)
print(i, " is removed")
print('Peaks fixed are: %s' % (indexes_1))
if len(indexes_1) != 0:
temp_list = [
abs(i - (len(height_of_two_points_Gray) / 2)) for i in indexes_1
]
indexes_1 = [indexes_1[temp_list.index(min(temp_list))]]
#plt.title("gray color between_reverse_marked")
#plt.plot(height_of_two_points_Gray, color="black")
plt.plot(indexes_1, [height_of_two_points_B[i] for i in indexes_1],
marker="8",
markersize=10,
color='blue',
ls="")
else:
print("Warning! Cell boundary detection failed!")
plt.tick_params(labelsize=13)
plt.show()
cv.imshow('img_copy', img_masked)
cv.waitKey()
