Ahanmr

pip3 install --upgrade tensorflow 
pip3 install numpy
pip3 install scipy 
pip3 install pillow
pip3 install h5py 
pip3 install opencv-python 
pip3 install matplotlib
pip3 install keras

Ahanmr

'''
Editor: Ahan M R
Date: 18-06-2018
Python 3.6.0
'''

#PS1 CROWD DETECTION IN THE CAFETERIA
'''
From imageAI API, we import Object detection and use it to detect the object classes in the image
'''

Ahanmr

'''
Editor: Ahan M R
Date: 18-06-2018
Python 3.6.0
'''

#PS1 CROWD DETECTION IN THE SITUATION
'''
From imageAI API, we import Object detection and use it to detect the object classes in the image
'''

Ahanmr

Kaiming H. et al, Deep Residual Learning for Image Recognition
https://arxiv.org/abs/1512.03385

Szegedy. et al, Rethinking the Inception Architecture for Computer Vision
https://arxiv.org/abs/1512.00567

Gao. et al, Densely Connected Convolutional Networks
https://arxiv.org/abs/1608.06993

Ahanmr

pip install https://github.com/OlafenwaMoses/ImageAI/releases/download/2.0.1/imageai-2.0.1-py3-none-any.whl

Ahanmr

https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/resnet50_coco_best_v2.0.1.h5

Ahanmr

car : 52.74809002876282
car : 54.43572402000427
car : 61.86940670013428
car : 64.99541997909546
car : 54.53670620918274
car : 54.111236333847046
car : 55.92341423034668
person : 54.37796711921692
person : 61.132240295410156
car : 70.4900324344635

Ahanmr

#Import libraries:
import pandas as pd
import numpy as np
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn import cross_validation, metrics   #Additional scklearn functions
from sklearn.grid_search import GridSearchCV   #Perforing grid search

import matplotlib.pylab as plt
%matplotlib inline

Ahanmr

def modelfit(alg, dtrain, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50):
    
    if useTrainCV:
        xgb_param = alg.get_xgb_params()
        xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)
        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,
            metrics='auc', early_stopping_rounds=early_stopping_rounds, show_progress=False)
        alg.set_params(n_estimators=cvresult.shape[0])
    
    #Fit the algorithm on the data

Ahanmr

    def find_tables(self, table_settings={}):
        return TableFinder(self, table_settings).tables

    def extract_tables(self, table_settings={}):
        tables = self.find_tables(table_settings)
        return [ table.extract() for table in tables ]

    def extract_table(self, table_settings={}):
        tables = self.find_tables(table_settings)
        # Return the largest table, as measured by number of cells.