222 lines
7.4 KiB
Python
222 lines
7.4 KiB
Python
import os
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import time
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import json
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import requests
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import traceback
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import cv2
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import pika
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import boto3
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import paramiko
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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
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###############################################
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# Config #
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###############################################
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with open('/home/sdt/Workspace/gseps_inference/config/inference_config.json', 'r') as f:
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info = json.load(f)
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def send_message_to_slack():
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data = {"text": info['slack_message']}
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req = requests.post(
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url=info['slack_url'],
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data=json.dumps(data)
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)
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def image_upload_to_s3(path):
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try:
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s3 = boto3.client('s3')
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folder_name = path.split('/')[-1]
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for root, dirs, files in os.walk(path):
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for f in files:
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local_file_path = os.path.join(root, f)
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s3.upload_file(local_file_path, 'gseps-data', f'{folder_name}/{f}')
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except Exception as e:
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print(traceback.format_exc())
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def image_copy_using_SCP(remote_path):
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try:
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# ssh connect
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ssh = paramiko.SSHClient()
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ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
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ssh.connect(info['remote_server_ip'], username=info['remote_server_id'], password=info['remote_server_pw'])
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# copy image from edge
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sftp = ssh.open_sftp()
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image_name = remote_path.split('/')[-1]
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local_path = info['copied_image_path_from_remote_server']
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if not os.path.exists(local_path):
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os.makedirs(local_path)
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sftp.get(remote_path, os.path.join(local_path, image_name))
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sftp.remove(remote_path)
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sftp.close()
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return os.path.join(local_path, image_name)
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except Exception as e:
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print(e)
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return ""
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def get_info(body):
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payload = json.loads(body)
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image_path = payload['image_path']
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return image_path
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class Consumer:
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def __init__(self):
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# with open("./config.json", "r") as f:
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# info = json.load(f.read())
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# change hard coding to info_dictionary from config.json
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self.__url = info['amqp_url']
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self.__port = info['amqp_port']
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self.__vhost = info['amqp_vhost']
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self.__cred = pika.PlainCredentials(info['amqp_id'], info['amqp_pw'])
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self.__TaskQ = info['amqp_taskq']
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self.__ResultQ = info['amqp_resultq']
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self.cfg = info['model_config']
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self.mask_generator = None
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# self.cloud_vender = info['cloud_vender']
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self.cloud_vender = 'S3'
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def initialize(self):
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start = time.time()
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sam = sam_model_registry['vit_h'](
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checkpoint=info['model_checkpoints'])
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sam = sam.to(self.cfg['device'])
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self.mask_generator = SamAutomaticMaskGenerator(model=sam,
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points_per_side=self.cfg['points_per_side'],
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pred_iou_thresh=self.cfg['pred_iou_thresh'],
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stability_score_thresh=self.cfg['stability_score_thresh'],
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crop_n_layers=self.cfg['crop_n_layers'],
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crop_n_points_downscale_factor=self.cfg[
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'crop_n_points_downscale_factor'],
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box_nms_thresh=self.cfg['box_nms_thresh'],
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min_mask_region_area=self.cfg['min_mask_region_area'])
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end = time.time()
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print(f'Initialize time: {(end - start) // 60}m {(end - start) % 60:4f}s')
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def image_upload_to_ncp(self, path):
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pass
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def image_upload_to_cloud(self, path):
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if self.cloud_vender == 'S3':
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image_upload_to_s3(path)
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elif self.cloud_vender == 'NCP':
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self.image_upload_to_ncp(path)
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def inference(self, image_path):
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image = cv2.imread(image_path)
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image_name = image_path.split('/')[-1].split('.')[0]
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result = self.mask_generator.generate(image)
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shape = result[0]['segmentation'].shape
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cumulated = np.zeros(shape)
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count = 0
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sizes = []
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for n, r in enumerate(result):
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if r['area'] < self.cfg['area_thresh'] * shape[0] * shape[1] and r['stability_score'] > self.cfg['stability_score_thresh']:
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if np.amax(cumulated + r['segmentation'].astype(int)) < 2:
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cumulated = cumulated + r['segmentation'].astype(int)
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count += 1
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x, y, w, h = r['bbox']
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sizes.append(np.mean([w, h]))
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save_path = os.path.join(info['inference_result_path'], image_name)
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if not os.path.exists(save_path):
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os.makedirs(save_path)
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cv2.imwrite(os.path.join(save_path, f'{image_name}.jpg'), image)
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plt.imsave(os.path.join(save_path, f'result_{image_name}.jpg'), cumulated)
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result_dict = {'image': os.path.join(save_path, f'{image_name}.jpg'), # 수정필요
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'count': count,
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'sizes': sizes}
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with open(os.path.join(save_path, f'result_{image_name}.json'), 'w') as f:
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json.dump(result_dict, f, indent=4)
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return result_dict, save_path
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def result_publish(self, channel, result):
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channel.basic_publish(exchange='',
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routing_key=self.__ResultQ,
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body=json.dumps(result))
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print(f"Done!")
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def upload_to_database(self, results):
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"""
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Insert inference result data into DB(postgreSQL).
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Columns consist of file_name(or image_name), count.
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"""
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pass
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def main(self):
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try:
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conn = pika.BlockingConnection(pika.ConnectionParameters(self.__url,
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self.__port,
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self.__vhost,
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self.__cred))
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chan = conn.channel()
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chan.queue_declare(queue=self.__TaskQ, durable=True)
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chan.queue_declare(queue=self.__ResultQ, durable=True)
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while True:
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method, properties, body = chan.basic_get(queue=self.__TaskQ,
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auto_ack=True)
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if not method:
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send_message_to_slack()
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break
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if method:
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print(f" [x] Received {body}", end=' | ', flush=True)
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edge_image_path = get_info(body)
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image_path = image_copy_using_SCP(edge_image_path)
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if image_path == "":
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continue
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result_dict, save_path = self.inference(image_path)
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self.result_publish(chan, result_dict)
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self.upload_to_database(result_dict)
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self.image_upload_to_cloud(save_path)
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time.sleep(1)
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else:
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time.sleep(0.5)
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except Exception as e:
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print(traceback.format_exc())
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conn.close()
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if __name__ == "__main__":
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consumer = Consumer()
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consumer.initialize()
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consumer.main()
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