def get_prediction(self,
type,
img_full_path=None,
img_path=None,
showbboxUI=None):
error_message = ''
config = Common_config()
predictMode = PredictMode.classifyOnly
#if(classify_detect):
#predictMode = PredictMode.classifyAndDetect
data = None
if (type == PredictType.sampleImages):
data, img_path, has_error = self.get_prediction_sampleImg(
img_path, img_full_path, predictMode)
if (type == PredictType.uploadedFile):
data, img_path, has_error, error_message = self.get_prediction_uploadedFile(
img_path, img_full_path, predictMode)
#if(type == PredictType.fromURL):
#data, img_path, has_error = self.get_prediction_url(predictMode)
return data, img_path, has_error, error_message
import sys
import uuid
import common
import requests
import traceback
import numpy as np
from PIL import Image
from search import Search
from urllib.parse import urlparse
from io import StringIO
from enums import *
from common import *
from common_config import Common_config
from flask import Flask, render_template, request, jsonify, make_response, url_for, g
config = Common_config()
root_direc = config.get_root_path()
sample_direc = config.get_sample_img_path()
upload_direc = config.get_upload_path()
model_path = config.get_model_path()
max_file_size = 3750000
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
def delete_all_uploaded():
directory = "." + upload_direc
import os
import future
import random
import pandas as pd
from common_config import Common_config
config = Common_config()
root_direc = config.get_root_path()
class Sample_images:
def __init__(self):
self.list = self.shuffle_animal_list()
self.start_range = 0
def shuffle_animal_list(self):
d = pd.read_csv(root_direc + "/static/data/updated_animal_list.csv")
print(d)
d = d.sample(frac=1)
return d
def get_images_data(self, add_more):
end_range = self.start_range + 8
if(not add_more):
end_range = self.start_range + 12
img_list = []
from PIL import Image
from search import Search
from flask import json
from enums import *
from common import *
from io import StringIO
from flask import send_file
from predict import Predict
from common_config import Common_config
from sample_images import Sample_images
s = Search()
predict = Predict()
sample_images = Sample_images()
config = Common_config()
root_direc = config.get_root_path()
show_bbox = config.show_bbox()
from flask import Flask, render_template, request, jsonify, make_response, url_for, g
app = Flask(__name__, static_url_path="/static", static_folder="static")
@app.route('/')
@app.route('/index.html')
def index():
try:
return render_template('/explore.html')
except Exception as e:
var = traceback.format_exc()
import numpy as np
from PIL import Image
from bbox import Bbox
from flask import request
from search import Search
from io import StringIO
from flask import send_file
from enums import *
from common import *
from urllib.parse import urlparse
from common_config import Common_config
from common import str2bool, delete_all_uploaded
import Predict_Trees
from keras.preprocessing.image import load_img
config = Common_config()
root_direc = config.get_root_path()
sample_direc = config.get_sample_img_path()
upload_direc = config.get_upload_path()
bbox = Bbox()
'''Move this to config file '''
CONTENT_TYPE_KEY = "Content-Type"
CONTENT_TYPE = 'application/octet-stream'
SUBSCRIPTION_KEY = '<add subscription key here> '
AUTHORIZATION_HEADER = 'Ocp-Apim-Subscription-Key'
base_url = '<add AI for earth API base URL here>'
classify_format = '{0}/species-recognition/v{1}/predict?topK={2}&predictMode={3}'