flask post JSON 数据获取方式总结

import base64
import io
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
import urllib

import json
from flask import Flask
from flask import request
app = Flask(__name__)

model = ResNet50(weights='imagenet')

def picture_recognition_base64(img_base64):
ret = []
img = io.BytesIO(base64.b64decode(img_base64))
img = image.load_img(img, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
result = decode_predictions(preds, top=5)[0]
for item in result:
ret.append({'name': item[1], 'sim': str(item[2])})
return ret

def picture_recognition(url):
ret = []
img = io.BytesIO(urllib.request.urlopen(url).read())
img = image.load_img(img, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
result = decode_predictions(preds, top=5)[0]
for item in result:
ret.append({'name': item[1], 'sim': str(item[2])})
return ret

@app.route('/url', methods=['GET', 'POST'])
def route_url():
if request.method == 'POST':
data = request.get_data()
print(data)
json_data = json.loads(data.decode("utf-8"))
print(json_data)
img_url = json_data.get("img_url")
result = picture_recognition(img_url)
print(json.dumps(result))
return json.dumps(result)

@app.route('/base64', methods=['GET', 'POST'])
def route_base64():
if request.method == 'POST':
data = request.get_data()
print(data)
json_data = json.loads(data.decode("utf-8"))
print(json_data)

img_base64 = json_data.get("img_base64")
result = picture_recognition_base64(img_base64)
print(json.dumps(result))
return json.dumps(result)

if __name__ == '__main__':
app.run(host='0.0.0.0',port=22222, debug=False)

window cmd:  curl -X post --data {\"img_url\":\"https://ss1.bdstatic.com/70cFvXSh_Q1YnxGkpoWK1HF6hhy/it/u=68222350,3999791223&fm=27&gp=0.jpg\"} http://172.0.0.1:22222/url 
linux shell: curl -X post --data '{"img_url":"https://ss1.bdstatic.com/70cFvXSh_Q1YnxGkpoWK1HF6hhy/it/u=68222350,3999791223&fm=27&gp=0.jpg"}' http://172.0.0.1:22222/url