我正在尝试使用云计算将其用于Apache2中的烧瓶服务，我尝试这样做： 1.安装Apache2和wsgi 2.安装我需要的Python和其他库
这样的代码：
#!/usr/bin/env python
import timeit
import re
import cgitb, cgi
cgitb.enable()
from keras import backend as K
import requests
import simplejson as json
import base64
import os
import uuid
from PIL import Image
from flask import Flask, jsonify, request
from keras.preprocessing.image import img_to_array
from keras.models import load_model
import numpy as np
from scipy import ndimage, misc
import cv2
import io
from io import BytesIO
from base64 import b64encode
app = Flask(__name__)
app.secret_key = "Top Secret"
ENDPOINT_URL = 'https://vision.googleapis.com/v1/images:annotate'
def make_image_data_list(image_filenames):
img_requests = []
with io.open(image_filenames, 'rb') as f:
ctxt = b64encode(f.read()).decode()
img_requests.append({
'image': {'content': ctxt},
'features': [{
'type': 'DOCUMENT_TEXT_DETECTION',
'maxResults': 1
}],
'imageContext': {
'languageHints': ['id']
}
})
return img_requests
def make_image_data(image_filenames):
"""Returns the image data lists as bytes"""
imgdict = make_image_data_list(image_filenames)
return json.dumps({"requests": imgdict }).encode()
def request_ocr(api_key, image_filenames):
response = requests.post(ENDPOINT_URL,
data=make_image_data(image_filenames),
params={'key': api_key},
headers={'Content-Type': 'application/json'})
return response
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
@app.errorhandler(500)
def internal_error(error):
return jsonify(
classify="Something wrong while process image on matching, cause damage or etc..., make sure clearly capture image",
response=306
)
@app.errorhandler(405)
def method_not_allowed(e):
return jsonify({'response': 305, 'message': 'authentication file required'}), 405
@app.route("/", methods=['POST'])
def index():
if request.method == 'POST':
start = timeit.default_timer()
id = uuid.uuid4().hex
file = request.form['file']
ktp = request.form['ktp']
face_cascade = cv2.CascadeClassifier('/var/www/html/classifier/haarcascade_frontalface_default.xml')
if(file[:2] == "/9"):
extentionfile = ".jpg"
if(file[:2] == "iV"):
extentionfile = ".png"
if(ktp[:2] == "/9"):
extentionktp = ".jpg"
if(ktp[:2] == "iV"):
extentionktp = ".png"
imselfie = Image.open(BytesIO(base64.b64decode(file)))
imselfie.save("/var/www/html/classifier/upload/face" + id + extentionfile)
imktp = Image.open(BytesIO(base64.b64decode(ktp)))
imktp.save("/var/www/html/classifier/ktp/face-" + id + extentionktp)
nig = 1
selfiestatus = 0
ktpstatus = 0
iter = 0
status = True
print("status : checking opening selfie and ktp image")
# for counter in range (0, 4):
while status == True:
iter = iter + 1
print("checking selfie image")
image = cv2.imread("/var/www/html/classifier/upload/face" + id + extentionfile, cv2.IMREAD_COLOR)
print("checking ktp image")
ktpimage=cv2.imread("/var/www/html/classifier/ktp/face-" + id + extentionktp, cv2.IMREAD_COLOR)
faces = face_cascade.detectMultiScale(image, 1.3, 5)
ktpfaces = face_cascade.detectMultiScale(ktpimage, 1.3, 5)
if(len(faces) >= 1):
selfiestatus += 1
if(len(ktpfaces) >= 1):
ktpstatus += 1
if(len(faces) != 1):
try:
image_to_rotate = ndimage.imread("/var/www/html/classifier/upload/face" + id + extentionfile)
rotated = ndimage.rotate(image_to_rotate, 90)
misc.imsave("/var/www/html/classifier/upload/face" + id + extentionfile, rotated)
print("rotating selfie photo")
except KeyError:
return jsonify(response=302, classify="Can't open selfie foto cause file is damage or missing, make sure to capture clearly")
if(len(ktpfaces) != 1):
try:
image_to_rotate = ndimage.imread("/var/www/html/classifier/ktp/face-" + id + extentionktp)
rotated = ndimage.rotate(image_to_rotate, 90)
misc.imsave("/var/www/html/classifier/ktp/face-" + id + extentionktp, rotated)
print("rotating ktp photo")
except:
return jsonify(response=302, classify="Can't open KTP foto cause file is damage or missing, make sure to capture clearly")
if(iter >= 4 or (ktpstatus >= 1 and selfiestatus >=1)):
status = False
if (selfiestatus <= 0):
print("error image selfie")
return jsonify(
response=300,
classify="No face found on selfie, make sure you have clearly selfie and just you in selfie photo"
)
elif (ktpstatus <= 0):
print("error image ktp")
return jsonify(
response=301,
classify="No face found on KTP, make sure you have clearly capture KTP foto"
)
else:
print("run liveness function")
image = cv2.imread("/var/www/html/classifier/upload/face" + id + extentionfile, cv2.IMREAD_COLOR)
image = cv2.resize(image, (28, 28))
image = image.astype("float") / 255.0
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
model = load_model("/var/www/html/classifier/liveness.model")
(notSanta, santa) = model.predict(image)[0]
K.clear_session()
label = "Live Person" if santa > notSanta else "Fake"
proba = santa if santa > notSanta else notSanta
if (label == "Live Person" and proba*100 < 75.00):
label = "Not Live Person"
print (label)
if(label == "Live Person"):
print("read file ktp")
ktp = base64.b64encode(open("/var/www/html/classifier/ktp/face-" + id + extentionktp, "rb").read())
print("sending file selfie")
selfie = base64.b64encode(open("/var/www/html/classifier/upload/face" + id + extentionfile, "rb").read())
print("packing to img base64 to json")
img = {'img1': selfie, 'img2': {'data':[ktp]}}
jsondata = json.dumps(img)
jsondataasbytes = jsondata.encode('utf-8')
convert = 'img=' + jsondataasbytes
print("sending 2 image in base64 format for matching, waiting...")
r = requests.post("http://192.168.78.16/classifier/classifier.py", data=convert)
print(r.text)
res = r.text.replace("\n", "")
result = float(res)
if result <= 0.48:
print(result)
result = 'true'
else:
print(result)
result = 'false'
response = request_ocr("", "/var/www/html/classifier/ktp/face-" + id + extentionfile)
if response.status_code != 200 or response.json().get('error'):
print(response.text)
else:
for idx, resp in enumerate(response.json()['responses']):
try:
t = resp['textAnnotations'][0]
text = t['description']
NIK = re.sub("\D", "", text)[:16]
except KeyError:
return jsonify(response=307, classify="Error when reading text from KTP with OCR function, make sure you send KTP correctly")
stop = timeit.default_timer()
print stop - start
return jsonify(
response=200,
NIK=NIK,
fileid=id,
liveness='true',
match=result,
score=r.text.replace("\n", ""))
else:
return jsonify(
liveness='false',
response=304,
classify=label,
score=proba * 100
)
if __name__ == '__main__':
app.run(threaded=True)
日志：
[Mon Sep 17 10:22:09.706774 2018] [core:notice] [pid 12837:tid 139745726404480] AH00094: Command line: '/usr/sbin/apache2'
[Mon Sep 17 10:22:19.648680 2018] [wsgi:error] [pid 12841:tid 139745593399040] [remote 210.210.184.38:17686] mod_wsgi (pid=12841): Target WSGI script '/var/www/html/cl
assifier/class.wsgi' cannot be loaded as Python module.
[Mon Sep 17 10:22:19.648721 2018] [wsgi:error] [pid 12841:tid 139745593399040] [remote 210.210.184.38:17686] mod_wsgi (pid=12841): Exception occurred processing WSGI s
cript '/var/www/html/classifier/class.wsgi'.
[Mon Sep 17 10:22:19.648735 2018] [wsgi:error] [pid 12841:tid 139745593399040] [remote 210.210.184.38:17686] Traceback (most recent call last):
[Mon Sep 17 10:22:19.648769 2018] [wsgi:error] [pid 12841:tid 139745593399040] [remote 210.210.184.38:17686] File "/var/www/html/classifier/class.wsgi", line 5, in <
module>
[Mon Sep 17 10:22:19.648816 2018] [wsgi:error] [pid 12841:tid 139745593399040] [remote 210.210.184.38:17686] from index import app as application
[Mon Sep 17 10:22:19.648822 2018] [wsgi:error] [pid 12841:tid 139745593399040] [remote 210.210.184.38:17686] File "/var/www/html/classifier/index.py", line 4, in <mo
dule>
[Mon Sep 17 10:22:19.648909 2018] [wsgi:error] [pid 12841:tid 139745593399040] [remote 210.210.184.38:17686] from flask_api import FlaskAPI
[Mon Sep 17 10:22:19.648923 2018] [wsgi:error] [pid 12841:tid 139745593399040] [remote 210.210.184.38:17686] ImportError: No module named flask_api
但是要验证安装，请尝试以下操作：
(envface) root@nvidia-ngc-image-2-vm:/etc/apache2/sites-enabled# pip install flask_api
Requirement already satisfied: flask_api in /home/mip_mip774/cuda/envface/lib/python2.7/site-packages (1.0)
Requirement already satisfied: Flask>=0.10.1 in /home/mip_mip774/cuda/envface/lib/python2.7/site-packages (from flask_api) (1.0.2)
Requirement already satisfied: Jinja2>=2.10 in /home/mip_mip774/cuda/envface/lib/python2.7/site-packages (from Flask>=0.10.1->flask_api) (2.10)
Requirement already satisfied: itsdangerous>=0.24 in /home/mip_mip774/cuda/envface/lib/python2.7/site-packages (from Flask>=0.10.1->flask_api) (0.24)
Requirement already satisfied: Werkzeug>=0.14 in /home/mip_mip774/cuda/envface/lib/python2.7/site-packages (from Flask>=0.10.1->flask_api) (0.14.1)
Requirement already satisfied: click>=5.1 in /home/mip_mip774/cuda/envface/lib/python2.7/site-packages (from Flask>=0.10.1->flask_api) (6.7)
Requirement already satisfied: MarkupSafe>=0.23 in /home/mip_mip774/cuda/envface/lib/python2.7/site-packages (from Jinja2>=2.10->Flask>=0.10.1->flask_api) (1.0)
但是当我在运行中使用开发烧瓶时：python main.py，其中main是烧瓶文件（不在apache2上），则可以成功运行而没有错误。
出什么问题了？有任何线索吗？
答案 0 :(得分：0)
通常，系统中安装了许多python解释器，而当您运行Function<Flux<String>, Flux<Integer>> letterPerWordCount = f -> f
.flatMap(sentence -> Flux.fromArray(sentence.split(" "))
.map(String::length);
Flux<String> mySentenceSource;
Flux<Integer> letterCount = mySentenceCount.transform(letterPerWordCount);
时，它不是在系统范围内安装的，而是针对单个python解释器，这通常与Apache使用的解释器不同。 Python的最佳实践是使用虚拟环境，因此我建议您学习what it is and how to create it和how to use it with Apache+mod_wsgi。