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find_meteor.py
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# Run python find_meteor.py [input_path] [output_path]
# Run example: python find_meteor.py ./2020-11-11 ./2020-11-11-find
def method1(img_path, mask, show_all_step=False, minimal_lenth=300):
import cv2
import numpy as np
from scipy.spatial import distance as dist
# (1) Loading image
img = cv2.imread(img_path)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# (2) Edge detection : Canny
img_gray_canny = cv2.Canny(img_gray.astype(np.uint8),50,100,apertureSize=3)
# (3) Apply the mask to the result of edge detection
img_gray_canny_crop = img_gray_canny*(mask/255)
img_gray_canny_crop = img_gray_canny_crop.astype(np.uint8)
# (4) Line dilatation
kernel = np.ones((3,3), np.uint8)
dilation = cv2.dilate(img_gray_canny_crop, kernel, iterations = 2)
# (5) Line erosion
dilation = cv2.erode(dilation, kernel, iterations = 1)
# (6) Cloudy detection and mask producing
cloud_mask = np.zeros(dilation.shape,np.uint8)
contour,hier = cv2.findContours(dilation,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)
contour_able = 0
try:
for cnt in contour:
area = cv2.contourArea(cnt)
if area > 1550:
#print(area)
contour_able = 1
cv2.drawContours(cloud_mask,[cnt],0,255,-1)
except:
contour_able = 0
if contour_able==1:
#print('It\'s Cloudy')
kernel_dilate = np.ones((7,7), np.uint8)
#contour_maks = cv2.dilate(cv2.bitwise_not(aa), kernel_dilate, iterations = 1)
#dilation_mask = dilation * contour_maks
dilation_mask = dilation * cv2.dilate(cv2.bitwise_not(cloud_mask), kernel_dilate, iterations = 1)
dilation_mask = 255*dilation_mask
else:
#print('Nice Weather')
dilation_mask = dilation
# (7) Line detection
lines = cv2.HoughLinesP(dilation_mask,3,np.pi/180,100,minimal_lenth,20)
try:
# Draw the detected line on the image
counter = 0
for i in range(lines.shape[0]):
for x1,y1,x2,y2 in lines[i]:
if dist.euclidean((x1, y1), (x2, y2)) > 50:
counter +=1
cv2.line(img,(x1,y1),(x2,y2),(0,0,255),10)
# Show result
if counter != 0:
#print(lines.shape,' After threshold: ',counter)
if show_all_step:
#result = cv2.hconcat((cv2.cvtColor(img_gray_canny_crop, cv2.COLOR_GRAY2BGR), cv2.cvtColor(dilation, cv2.COLOR_GRAY2BGR), cv2.cvtColor(aa, cv2.COLOR_GRAY2BGR), cv2.cvtColor(dilation_mask, cv2.COLOR_GRAY2BGR), img))
result1 = cv2.hconcat((cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR), cv2.cvtColor(img_gray_canny, cv2.COLOR_GRAY2BGR), cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR), cv2.cvtColor(img_gray_canny_crop, cv2.COLOR_GRAY2BGR)))
result2 = cv2.hconcat((cv2.cvtColor(dilation, cv2.COLOR_GRAY2BGR), cv2.cvtColor(Cloud_mask, cv2.COLOR_GRAY2BGR), cv2.cvtColor(dilation_mask, cv2.COLOR_GRAY2BGR),img))
result = cv2.vconcat([result1, result2])
cv2_imshow(result)
return 1
else:
#print('No lines was detected')
return 0
except:
# The error comes from the data type of lines when No lines was detected
#print('No lines was detected')
return 0
def find_meteor(file_list, show_result=False, save_image=False, save_csv=False, save_path=None):
import time
start_time = time.perf_counter()
find_list = []
print(file_list[0])
mask = mask_generation()
for img_path in file_list:
#print(img_path)
if method1(img_path,mask,show_result): find_list.append(img_path)
print('Finished')
finished_time = time.perf_counter()
spend = finished_time - start_time
print("Time Consumed：{}s".format(spend))
#print(find_list)
print('Find : Total = ',len(find_list),'/',len(file_list),' = ',round(100*len(find_list)/len(file_list),2),'%')
if save_image:
import os
from shutil import copyfile
os.makedirs(save_path,exist_ok=True)
# save image to folder
for filename in find_list:
save_file_path= save_path+'/'+os.path.basename(filename)
copyfile(filename, save_file_path)
print('Saving the meteor images at the path of : '+save_path)
if save_csv:
import pandas as pd
name=['File Name']
csv=pd.DataFrame(columns=name,data=find_list)
#print(test)
csv.to_csv(save_path+'/result.csv',encoding='gbk')
print('Saving the find_list at the path of : '+save_path+'/result.csv')
return find_list
def mask_generation(high=2080, width=3096):
print('-------mask generation-----')
import cv2
import numpy as np
mask=np.zeros((high,width),np.uint8)
cv2.circle(mask,(int(width/2),int(high/2)),1000,(255),-1)
cv2.circle(mask,(int(width/2),int(high/2)),50,(0),-1)
mask[0:100,:] = 0
mask[mask.shape[0]-100:mask.shape[0],:] = 0
mask[1800:mask.shape[0], 750:850] = 0
print('-------mask generation Finished-----')
return mask
if __name__ == '__main__':
import sys
import glob
file_list = glob.glob(sys.argv[1] + '/*__1[7-9]*.jpg')
file_list.extend(glob.glob(sys.argv[1] + '/*__2[0-3]*.jpg'))
file_list.extend(glob.glob(sys.argv[1] + '/*__0[0-5]*.jpg'))
find_list = []
find_list = find_meteor(file_list, save_path=sys.argv[2],save_image=True,save_csv=True)