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import sys
import os
import flask
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import copy
import cv2
import random
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from re import I
from flask import Flask, render_template, request, redirect, url_for, flash, jsonify
from flask_cors import CORS, cross_origin
from flask import send_from_directory
import base64
from PIL import Image
from io import BytesIO
app = Flask(__name__)
cors = CORS(app)
app.config['CORS_HEADERS'] = 'Content-Type'
# Disable tensorflow compilation warnings
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
#import tensorflow as tf
def predict(image_data):
predictions = sess.run(softmax_tensor, \
{'DecodeJpeg/contents:0': image_data})
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
max_score = 0.0
res = ''
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
if score > max_score:
max_score = score
res = human_string
return res, max_score
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line
in tf.gfile.GFile("logs/trained_labels.txt")]
# Unpersists graph from file
with tf.gfile.FastGFile("logs/trained_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
sess = tf.Session()
# Feed the image_data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
def imageRead (random_name):
c = 0
global sess
global softmax_tensor
#cap = cv2.VideoCapture(0)
res, score = '', 0.0
i = 0
mem = ''
consecutive = 0
sequence = ''
while True:
img = cv2.imread('temp_img/'+random_name)
img = cv2.flip(img, 1)
#x1, y1, x2, y2 = 200, 200, 600, 600
#img_cropped = img[y1:y2, x1:x2]
c += 1
image_data = cv2.imencode('.jpg', img)[1].tostring()
a = cv2.waitKey(1) # waits to see if `esc` is pressed
res_tmp, score = predict(image_data)
res = res_tmp
print(res)
return res;
#cv2.putText(img, '%s' % (res.upper()), (100,400), cv2.FONT_HERSHEY_SIMPLEX, 4, (255,255,255), 4)
#cv2.putText(img, '(score = %.5f)' % (float(score)), (100,450), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255))
#mem = res
#cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,0), 2)
#cv2.imshow("img", img)
#img_sequence = np.zeros((200,1200,3), np.uint8)
#cv2.putText(img_sequence, '%s' % (sequence.upper()), (30,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)
#cv2.imshow('sequence', img_sequence)
#if a == 27: # when `esc` is pressed
# break
@app.route('/image', methods=['GET', 'POST'])
@cross_origin()
def image():
req = request.get_json()
random_name = "test" + '.jpg'
image_data = req['image_data'].split(',')[1]
im = Image.open(BytesIO(base64.b64decode(image_data)))
im.save('temp_img/'+random_name, 'JPEG')
imageData = imageRead(random_name)
return '{"status":1, "value": "'+imageData+'"}';
@app.route('/')
@cross_origin()
def homePage():
return render_template('index.html')
@app.route("/audio/<path:path>")
def static_dir(path):
return flask.send_file("templates/audio/" + path)
@app.route('/image-upload', methods=['GET', 'POST'])
@cross_origin()
def imageUpload():
req = request.get_json()
random_name = str( random.randint(1, 9999999) )+ '.jpg'
image_data = req['image_data'].split(',')[1]
im = Image.open(BytesIO(base64.b64decode(image_data)))
im.save('temp_img/'+random_name, 'JPEG')
imageData = imageRead(random_name)
return '{"status":1, "value": "'+imageData+'"}';
if __name__ == '__main__':
app.run(debug=True)
# Following line should... <-- This should work fine now
# cv2.destroyAllWindows()
# cv2.VideoCapture(0).release() |