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f2d4a33 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | import os
import PIL
import numpy
from numpy.lib.function_base import average
from numpy import zeros
from numpy import asarray
from mrcnn.config import Config
from mrcnn.model import MaskRCNN
from skimage.draw import polygon2mask
from skimage.io import imread
from datetime import datetime
from io import BytesIO
from mrcnn.utils import extract_bboxes
from numpy import expand_dims
from matplotlib import pyplot
from matplotlib.patches import Rectangle
from keras.backend import clear_session
import json
from flask import Flask, flash, request,jsonify, redirect, url_for
from werkzeug.utils import secure_filename
from skimage.io import imread
from mrcnn.model import mold_image
import tensorflow as tf
import sys
from PIL import Image
global _model
global _graph
global cfg
ROOT_DIR = os.path.abspath("./")
WEIGHTS_FOLDER = "./weights"
from flask_cors import CORS, cross_origin
sys.path.append(ROOT_DIR)
MODEL_NAME = "mask_rcnn_hq"
WEIGHTS_FILE_NAME = 'maskrcnn_15_epochs.h5'
application=Flask(__name__)
cors = CORS(application, resources={r"/*": {"origins": "*"}})
class PredictionConfig(Config):
# define the name of the configuration
NAME = "floorPlan_cfg"
# number of classes (background + door + wall + window)
NUM_CLASSES = 1 + 3
# simplify GPU config
GPU_COUNT = 1
IMAGES_PER_GPU = 1
@application.before_first_request
def load_model():
global cfg
global _model
model_folder_path = os.path.abspath("./") + "/mrcnn"
weights_path= os.path.join(WEIGHTS_FOLDER, WEIGHTS_FILE_NAME)
cfg=PredictionConfig()
print(cfg.IMAGE_RESIZE_MODE)
print('==============before loading model=========')
_model = MaskRCNN(mode='inference', model_dir=model_folder_path,config=cfg)
print('=================after loading model==============')
_model.load_weights(weights_path, by_name=True)
global _graph
_graph = tf.get_default_graph()
def myImageLoader(imageInput):
image = numpy.asarray(imageInput)
h,w,c=image.shape
if image.ndim != 3:
image = skimage.color.gray2rgb(image)
if image.shape[-1] == 4:
image = image[..., :3]
return image,w,h
def getClassNames(classIds):
result=list()
for classid in classIds:
data={}
if classid==1:
data['name']='wall'
if classid==2:
data['name']='window'
if classid==3:
data['name']='door'
result.append(data)
return result
def normalizePoints(bbx,classNames):
normalizingX=1
normalizingY=1
result=list()
doorCount=0
index=-1
doorDifference=0
for bb in bbx:
index=index+1
if(classNames[index]==3):
doorCount=doorCount+1
if(abs(bb[3]-bb[1])>abs(bb[2]-bb[0])):
doorDifference=doorDifference+abs(bb[3]-bb[1])
else:
doorDifference=doorDifference+abs(bb[2]-bb[0])
result.append([bb[0]*normalizingY,bb[1]*normalizingX,bb[2]*normalizingY,bb[3]*normalizingX])
return result,(doorDifference/doorCount)
def turnSubArraysToJson(objectsArr):
result=list()
for obj in objectsArr:
data={}
data['x1']=obj[1]
data['y1']=obj[0]
data['x2']=obj[3]
data['y2']=obj[2]
result.append(data)
return result
@application.route('/',methods=['POST'])
def prediction():
global cfg
imagefile = PIL.Image.open(request.files['image'].stream)
image,w,h=myImageLoader(imagefile)
print(h,w)
scaled_image = mold_image(image, cfg)
sample = expand_dims(scaled_image, 0)
global _model
global _graph
with _graph.as_default():
r = _model.detect(sample, verbose=0)[0]
#output_data = model_api(imagefile)
data={}
bbx=r['rois'].tolist()
temp,averageDoor=normalizePoints(bbx,r['class_ids'])
temp=turnSubArraysToJson(temp)
data['points']=temp
data['classes']=getClassNames(r['class_ids'])
data['Width']=w
data['Height']=h
data['averageDoor']=averageDoor
return jsonify(data)
if __name__ =='__main__':
application.debug=False
print('===========before running==========')
application.run(host='0.0.0.0', port=7860)
print('===========after running==========')
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