You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
107 lines
3.8 KiB
107 lines
3.8 KiB
#!/usr/bin/env python3
|
|
import os
|
|
from io import BytesIO
|
|
import cv2
|
|
import time
|
|
import numpy as np
|
|
import subprocess
|
|
import logging
|
|
import json
|
|
import sys
|
|
import paramiko
|
|
import pysftp
|
|
|
|
def main():
|
|
import time as time1
|
|
start = time1.time()
|
|
cnopts = pysftp.CnOpts()
|
|
cnopts.hostkeys = None
|
|
try:
|
|
|
|
sftp = pysftp.Connection(
|
|
host="10.129.28.219",
|
|
username="faasapp",
|
|
password="1234",
|
|
cnopts=cnopts
|
|
)
|
|
logging.info("connection established successfully")
|
|
except:
|
|
logging.info('failed to establish connection to targeted server')
|
|
|
|
contour_directory = "contoured-images"
|
|
is_contour_dir = os.path.isdir(contour_directory)
|
|
if(is_contour_dir == False):
|
|
os.mkdir(contour_directory)
|
|
|
|
images_dir = "images"
|
|
is_images_dir = os.path.isdir(images_dir)
|
|
if(is_images_dir == False):
|
|
os.mkdir(images_dir)
|
|
|
|
|
|
remote_download_path = "/home/faasapp/Desktop/anubhav/sprocket-decode/"+images_dir
|
|
|
|
remote_upload_path = "/home/faasapp/Desktop/anubhav/contour-finding/"+contour_directory
|
|
|
|
try:
|
|
sftp.chdir(remote_download_path) # Test if remote_path exists
|
|
except IOError:
|
|
sftp.mkdir(remote_download_path) # Create remote_path
|
|
sftp.chdir(remote_download_path)
|
|
|
|
try:
|
|
sftp.chdir(remote_upload_path) # Test if remote_path exists
|
|
except IOError:
|
|
sftp.mkdir(remote_upload_path) # Create remote_path
|
|
sftp.chdir(remote_upload_path)
|
|
|
|
sftp.get_d(remote_download_path,preserve_mtime=True,localdir=images_dir)
|
|
|
|
activation_id = os.environ.get('__OW_ACTIVATION_ID')
|
|
params = json.loads(sys.argv[1])
|
|
|
|
decode_activation_id = params["activation_id"]
|
|
parts = params["parts"]
|
|
image_contour_mappings = {}
|
|
contour_detected_images = {}
|
|
for i in range(0,parts):
|
|
img_name = images_dir+'/Image' + str(i) + '.jpg'
|
|
img = cv2.imread(img_name)
|
|
img = cv2.resize(img,None,fx=0.9,fy=0.9)
|
|
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
|
ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
|
|
contours, hierarchy = cv2.findContours(binary, mode=cv2.RETR_TREE, method=cv2.CHAIN_APPROX_NONE)
|
|
contour_list_for_each_image=[]
|
|
cv2.drawContours(img, contours, -1, (0, 255, 0), thickness=2, lineType=cv2.LINE_AA)
|
|
for contour in contours:
|
|
approx = cv2.approxPolyDP(contour, 0.01* cv2.arcLength(contour, True), True)
|
|
contour_list_for_each_image.append(len(approx))
|
|
|
|
|
|
image_contour_mappings[img_name] = sum(contour_list_for_each_image)
|
|
filename = 'contour' + str(i) +'.jpg'
|
|
# Saving the image
|
|
cv2.imwrite(contour_directory+"/"+filename, img)
|
|
contour_img = cv2.imread(contour_directory+"/"+filename)
|
|
# contour_height, contour_width = contour_img.shape[:2]
|
|
contour_detected_size = os.stat(contour_directory+"/"+filename).st_size
|
|
contour_detected_images[contour_directory+"/"+filename] = contour_detected_size
|
|
|
|
current_path = os.getcwd()
|
|
sftp.put_d(current_path+"/"+contour_directory,preserve_mtime=True,remotepath=remote_upload_path)
|
|
contour_images = os.listdir(current_path+"/"+contour_directory)
|
|
end = time1.time()
|
|
exec_time = end-start
|
|
decode_execution_time = params["exec_time_decode"]
|
|
print(json.dumps({ "contour_images": contour_images,
|
|
"image_contour_mappings": image_contour_mappings,
|
|
"contour_detect_activation_id": str(activation_id),
|
|
"number_of_images_processed": parts,
|
|
"contour_execution_time": exec_time,
|
|
"decode_execution_time": decode_execution_time,
|
|
"contour_detected_images_size": contour_detected_images
|
|
}))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |