mirror of
https://github.com/20kaushik02/CSE546_Cloud_Computing_Projects.git
synced 2025-12-06 11:14:06 +00:00
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
import sys
|
|
import os
|
|
import time
|
|
import _thread
|
|
import argparse
|
|
import requests
|
|
import subprocess
|
|
import numpy as np
|
|
import pandas as pd
|
|
import threading
|
|
from concurrent.futures import ThreadPoolExecutor
|
|
from concurrent.futures import as_completed
|
|
|
|
parser = argparse.ArgumentParser(description="Upload images")
|
|
parser.add_argument("--num_request", type=int, help="one image per request")
|
|
parser.add_argument(
|
|
"--url",
|
|
type=str,
|
|
help="URL to the backend server, e.g. http://3.86.108.221/xxxx.php",
|
|
)
|
|
parser.add_argument(
|
|
"--image_folder",
|
|
type=str,
|
|
help="the path of the folder where images are saved on your local machine",
|
|
)
|
|
parser.add_argument(
|
|
"--prediction_file", type=str, help="the path of the classification results file"
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
num_request = args.num_request
|
|
url = args.url
|
|
image_folder = args.image_folder
|
|
prediction_file = args.prediction_file
|
|
|
|
prediction_df = pd.read_csv(prediction_file)
|
|
responses = 0
|
|
err_responses = 0
|
|
correct_predictions = 0
|
|
wrong_predictions = 0
|
|
|
|
|
|
def send_one_request(image_path):
|
|
global prediction_df, responses, err_responses, correct_predictions, wrong_predictions
|
|
# Define http payload, "myfile" is the key of the http payload
|
|
file = {"inputFile": open(image_path, "rb")}
|
|
response = requests.post(url, files=file)
|
|
|
|
# Print error message if failed
|
|
if response.status_code != 200:
|
|
print("sendErr: " + response.url)
|
|
err_responses += 1
|
|
else:
|
|
filename = image_path.split("/")[-1]
|
|
image_msg = filename + " uploaded!"
|
|
msg = image_msg + "\n" + "Classification result: " + response.text
|
|
print(msg)
|
|
responses += 1
|
|
correct_result = prediction_df.loc[
|
|
prediction_df["Image"] == filename.split(".")[0], "Results"
|
|
].iloc[0]
|
|
if correct_result == response.text.split(":")[1]:
|
|
correct_predictions += 1
|
|
else:
|
|
wrong_predictions += 1
|
|
|
|
|
|
num_max_workers = 100
|
|
image_path_list = []
|
|
test_start_time = time.time()
|
|
|
|
for i, name in enumerate(os.listdir(image_folder)):
|
|
if i == num_request:
|
|
break
|
|
image_path_list.append(image_folder + name)
|
|
|
|
with ThreadPoolExecutor(max_workers=num_max_workers) as executor:
|
|
executor.map(send_one_request, image_path_list)
|
|
|
|
print("Waiting ... ")
|
|
while True:
|
|
print(".")
|
|
test_duration = time.time() - test_start_time
|
|
if num_request == (responses + err_responses) or test_duration > 300:
|
|
print(f"+++++ Test Result Statistics +++++")
|
|
print(f"Total number of requests: {num_request}")
|
|
print(f"Total number of requests completed successfully: {responses}")
|
|
print(f"Total number of failed requests: {err_responses}")
|
|
print(f"Total number of correct predictions : {correct_predictions}")
|
|
print(f"Total number of wrong predictions: {wrong_predictions}")
|
|
print(f"Total Test Duration: {test_duration} (seconds)")
|
|
print("++++++++++++++++++++++++++++++++++++")
|
|
break
|
|
time.sleep(1)
|