def open_im_by_folder(FOLDER, sufx=suffixes):
"""
FOLDER : Folder containing sample files
sufx : color suffixes for samples
"""
FOLDER = Path(FOLDER)
fnames = sorted(FOLDER.ls())
fnames = [fnames[2], fnames[1], fnames[0],
fnames[3]] # Sorting according to RGBY from BGRY
imgs = [cv2.imread(str(o), cv2.IMREAD_GRAYSCALE)
for o in fnames] # List of 4 channels of given file ID
imgs = np.stack(imgs, 2)
return Image(pil2tensor(
imgs,
np.float32).float()) # Creating a Fastai Image object from the image
def get_folder():
is_repeating = False
is_folder_valid = False
while not is_folder_valid:
prompt = "Enter the location of the folder containing the samples : " if not is_repeating \
else "Please enter a valid folder : "
try:
folder = Path(input(prompt))
if not folder.is_dir():
raise FileNotFoundError
else:
is_folder_valid = True
except FileNotFoundError:
is_repeating = True
is_folder_valid = False
return folder
def classify(filename, learn):
file = Path(UPLOAD_FOLDER) / filename
image = open_image(file)
pred = learn.predict(image)
return pred[0]
def get_folder_by_ID(ROOT=ROOT):
is_repeating = False
is_ID_valid = False
while not is_ID_valid:
prompt = "Enter the ID of the samples : " if not is_repeating \
else "Please enter a valid ID : "
try:
ID = Path(input(prompt))
folder = ROOT / ID
if not folder.is_dir():
raise FileNotFoundError
else:
is_ID_valid = True
except FileNotFoundError:
is_repeating = True
is_ID_valid = False
return folder
from starlette.applications import Starlette
from starlette.responses import HTMLResponse, JSONResponse
from starlette.staticfiles import StaticFiles
from starlette.middleware.cors import CORSMiddleware
import uvicorn, aiohttp, asyncio
from io import BytesIO
from fastai.vision import ImageDataBunch, create_cnn, open_image, get_transforms, imagenet_stats, models
from fastai import Path
model_file_url = 'https://www.dropbox.com/s/y4kl2gv1akv7y4i/stage-2.pth?raw=1'
model_file_name = 'model'
classes = ['black', 'grizzly', 'teddys']
path = Path(__file__).parent
app = Starlette()
app.add_middleware(CORSMiddleware,
allow_origins=['*'],
allow_headers=['X-Requested-With', 'Content-Type'])
app.mount('/static', StaticFiles(directory='app/static'))
async def download_file(url, dest):
if dest.exists(): return
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
data = await response.read()
with open(dest, 'wb') as f:
f.write(data)
from fastai import Path
import cv2
import numpy as np
from fastai.vision import Image, pil2tensor
import matplotlib.pyplot as plt
ROOT = Path('data/demonstration')
suffixes = ['_red.png', '_green.png', '_blue.png', '_yellow.png']
prompt = "Do you want to make predictions based on a folder or ID (1 or 2) ? "
def open_im_by_id(ID, ROOT=ROOT, sufx=suffixes):
"""
ID : Base name for each sample
ROOT : Folder containing all samples
sufx : color suffixes for samples
"""
imnames = [ROOT / ID / (ID + o)
for o in sufx] # Generating file names for given image ID
imgs = [cv2.imread(str(o), cv2.IMREAD_GRAYSCALE)
for o in imnames] # List of 4 channels of given file ID
imgs = np.stack(imgs, 2)
return Image(pil2tensor(
imgs,
np.float32).float()) # Creating a Fastai Image object from the image
def open_im_by_folder(FOLDER, sufx=suffixes):
"""
FOLDER : Folder containing sample files
sufx : color suffixes for samples
def load_classes(file, path=Path('.')):
path = path / 'models'
file = path / file
filestr = str(file)
with open(filestr + '.classes', 'r') as f:
return json.loads(f.read())
def save_classes(file, classes, path=Path('.')):
path = path / 'models'
file = path / file
filestr = str(file)
with open(filestr + '.classes', 'w') as f:
f.write(json.dumps(classes))
from fastai import Path
from fastai import Config
import json
#path = Path(Config.get_key('data_path')).expanduser()/'handwritten'
# /mnt/handwritten is a path specific to azure datascience machine. If it does not work, uncomment the line above
# and comment line below
ramdisk_path = Path('/tmp/ramdisk')
path = Path(ramdisk_path / 'handwritten')
permanent_path = Path(Config.get_key('data_path')).expanduser() / 'handwritten'
def save_classes(file, classes, path=Path('.')):
path = path / 'models'
file = path / file
filestr = str(file)
with open(filestr + '.classes', 'w') as f:
f.write(json.dumps(classes))
def load_classes(file, path=Path('.')):
path = path / 'models'
file = path / file
filestr = str(file)
with open(filestr + '.classes', 'r') as f:
return json.loads(f.read())
def create_network():
empty_data = ImageDataBunch.load_empty(Path(UPLOAD_FOLDER), 'export.pkl')
learn = create_cnn(empty_data, models.resnet18).load('stage-2')
return learn