Я работаю над приложением Flask, в котором я хочу, чтобы пользователь загрузил фотографию, и классифицирую ее, используя свою модель Tensorflow. Поскольку в Heroku возникли проблемы с локальным хранением файлов, я подключился к базе данных PostgreSQL и пытаюсь вставить изображение в базу данных, а затем получить его из базы данных и использовать это изображение для Image.open(<img_file_from_my_database>) PIL. Ниже мой app.py
import flask
from flask import Flask, render_template, url_for
from flask import request
from flask import jsonify
from flask import send_from_directory
from flask import redirect
from flask import flash
import os.path
import datetime
import json
import pandas as pd
import numpy as np
from PIL import Image
import requests
from io import BytesIO
import time
import os
# Tensorflow imports
import tensorflow as tf
# **** Place functions here from predict.py to avoid Heroku Error: ****
# from predict import process_image, prediction, get_label_names
def process_image(image):
# from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
'''
The process_image function should take in an image (in the form of a NumPy array) and return an image in the form of a NumPy array with shape (224, 224, 3).
'''
# First, convert the image into a TensorFlow Tensor then resize it to the appropriate size using tf.image.resize
image = tf.convert_to_tensor(image, dtype=tf.float32)
image = tf.image.resize(image, (224,224))
# #Second, the pixel values of the input images are typically encoded as integers in the range 0-255, but the model expects the pixel values to be floats in the range 0-1.
# # #Therefore, you'll also need to normalize the pixel values
image /= 255
image = image.numpy()
return image
def prediction(image_path,top_k):
import os
model = 'my_model2'
model_path = os.path.join(app.root_path, model)
reloaded_keras_model = tf.saved_model.load(model_path)
processed_test_image = process_image(image_path)
#The image returned by the process_image function is a NumPy array with shape (224, 224, 3) but the model expects the input images to be of shape (1, 224, 224, 3). This extra dimension represents the batch size.
# Use np.expand_dims() function to add the extra dimension.
processed_test_image = np.expand_dims(processed_test_image, axis=0)
# Get the predictions by using the probability model to predict the input image
predictions = reloaded_keras_model(processed_test_image)
predictions = np.array(predictions)
# Get the index of the top 10 probabilities
top_idxs = predictions[0].argsort()[-top_k:][::-1]
# Get the top 10 probabilities
top_probabilities = predictions[0][top_idxs]
probs = top_probabilities
# Get the labels (the index of the probabilities)
labels_nums = [str(idx) for idx in top_idxs]
classes = labels_nums
return probs, classes
def get_label_names(json_file, labels):
'''
Given json_file that contains the label names for the label numbers, return the correct label names from array 'label'
'''
class_names = {"21": "fire lily", "3": "canterbury bells", "45": "bolero deep blue", "1": "pink primrose", "34": "mexican aster", "27": "prince of wales feathers", "7": "moon orchid", "16": "globe-flower", "25": "grape hyacinth", "26": "corn poppy", "79": "toad lily", "39": "siam tulip", "24": "red ginger", "67": "spring crocus", "35": "alpine sea holly", "32": "garden phlox", "10": "globe thistle", "6": "tiger lily", "93": "ball moss", "33": "love in the mist", "9": "monkshood", "102": "blackberry lily", "14": "spear thistle", "19": "balloon flower", "100": "blanket flower", "13": "king protea", "49": "oxeye daisy", "15": "yellow iris", "61": "cautleya spicata", "31": "carnation", "64": "silverbush", "68": "bearded iris", "63": "black-eyed susan", "69": "windflower", "62": "japanese anemone", "20": "giant white arum lily", "38": "great masterwort", "4": "sweet pea", "86": "tree mallow", "101": "trumpet creeper", "42": "daffodil", "22": "pincushion flower", "2": "hard-leaved pocket orchid", "54": "sunflower", "66": "osteospermum", "70": "tree poppy", "85": "desert-rose", "99": "bromelia", "87": "magnolia", "5": "english marigold", "92": "bee balm", "28": "stemless gentian", "97": "mallow", "57": "gaura", "40": "lenten rose", "47": "marigold", "59": "orange dahlia", "48": "buttercup", "55": "pelargonium", "36": "ruby-lipped cattleya", "91": "hippeastrum", "29": "artichoke", "71": "gazania", "90": "canna lily", "18": "peruvian lily", "98": "mexican petunia", "8": "bird of paradise", "30": "sweet william", "17": "purple coneflower", "52": "wild pansy", "84": "columbine", "12": "colt's foot", "11": "snapdragon", "96": "camellia", "23": "fritillary", "50": "common dandelion", "44": "poinsettia", "53": "primula", "72": "azalea", "65": "californian poppy", "80": "anthurium", "76": "morning glory", "37": "cape flower", "56": "bishop of llandaff", "60": "pink-yellow dahlia", "82": "clematis", "58": "geranium", "75": "thorn apple", "41": "barbeton daisy", "95": "bougainvillea", "43": "sword lily", "83": "hibiscus", "78": "lotus lotus", "88": "cyclamen", "94": "foxglove", "81": "frangipani", "74": "rose", "89": "watercress", "73": "water lily", "46": "wallflower", "77": "passion flower", "51": "petunia"}
new_class_names = {}
for key in class_names:
new_class_names[int(key)-1]=class_names[key]
new_class_names
label_names = [new_class_names[int(i)] for i in labels]
return label_names
# **** End functions from predict.py ****
from werkzeug.utils import secure_filename
UPLOAD_FOLDER = 'tmp/uploads'
ALLOWED_EXTENSIONS = {'jpg','jpeg'}
app = Flask(__name__)
from flask_sqlalchemy import SQLAlchemy
app.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('DATABASE_URL', '')
# # Remove tracking modifications
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
import psycopg2
# import databaseconfig
from .databaseconfig import user_name, password, local_host
# ---------------------
# PostgreSQL connection
# ---------------------
t_host = local_host
t_port = '5432'
t_dbname = "flower-image-db"
t_name_user = user_name
t_password = password
data_conn = psycopg2.connect(host=t_host, port=t_port, dbname=t_dbname, user=t_name_user, password=t_password)
db_cursor = data_conn.cursor()
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
@app.route('/', methods=['GET', 'POST'])
def upload_file():
if request.method == 'GET':
print(request.url_root)
return flask.render_template('index.html')
if request.method == 'POST':
print(request.url_root)
# check if the post request has the file part
if 'file' not in request.files:
return flask.render_template('index.html',
noFile = True)
file = request.files['file']
# if user does not select file, browser also
# submit an empty part without filename
if file.filename == '':
return flask.render_template('index.html',
noSelectedFile = True)
if allowed_file(file.filename) == False:
return flask.render_template('index.html',
noFile = True)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
print(filename)
id_image = 2
SaveFileToPG(id_image, filename)
top_k = str(request.form['month_day'])
if top_k.isdigit() == False:
return flask.render_template('index.html',
topKError = True)
else:
temp = 'temp'
top_k = int(top_k)
my_file_path = str(request.url_root)+ (str(my_file_path_ext)[1:])
postgreSQL_select_Query = "select * from tbl_files_images where id_image = %s"
db_cursor.execute(postgreSQL_select_Query, (id_image,))
records = db_cursor.fetchall()
for row in records:
myID = row[0]
myImage = row[1]
print(myImage)
# BytesIO(myImage)
new_bin_data = bytes(myImage)
print(new_bin_data)
print(bytearray(myImage))
im = Image.open(new_bin_data)
test_image = np.asarray(im)
print(test_image)
probs, classes = prediction(test_image, 'my_model2', top_k)
# # # # Print the probabilities and class numbers to the console
print(f'Proabilities: {probs}')
print(probs[0])
print(f'Label Numbers: {classes}')
# # Call the get_label_names function with the JSON file with class names and the classes returned from prediction
label_names = get_label_names('label_map.json', classes)
# # Print the class names to the console
print(f'Label Names: {label_names}')
print(probs[0])
print(classes[0])
print(label_names[0])
top_flower_name = label_names[0].title()
top_prob = round(probs[0]*100,2)
if top_k == 1:
return flask.render_template('index.html',
probs = probs,
classes=classes,
labelNames=label_names,
topFlowerName = top_flower_name,
onlyOneK = True,
topProb = top_prob)
else:
temp = 'temp'
return flask.render_template('index.html',
probs = probs,
classes=classes,
labelNames=label_names,
temp=temp,
topFlowerName = top_flower_name,
topProb = top_prob,
filename=filename,
top_k = top_k)
def SaveFileToPG(id_image, fileData):
insert_stmt = (
"INSERT INTO tbl_files_images (id_image, blob_image_data) "
"VALUES (%s, %s)"
)
data = (id_image, fileData)
db_cursor.execute(insert_stmt, data)
if __name__ == "__main__":
app.run(debug=True)
Однако он продолжает бросать FileNotFoundError: [Errno 2] No such file or directory: b'orange_dahlia.jpg'. Он работает, когда я запускаю приложение локально или через блокнот Jupyter. Это вызывает эту ошибку, когда я запускаю Heroku local через свой терминал. Я что-то не так делаю с форматом файла при получении изображения из базы данных? Как я могу это исправить? Заранее спасибо.