abhaymise

from concurrent.futures import ProcessPoolExecutor, as_completed
import cv2
import multiprocessing
import os
import sys


def print_progress(iteration, total, prefix='', suffix='', decimals=3, bar_length=100):
    """
    Call in a loop to create standard out progress bar

abhaymise

# For Windows users# Note: <> denotes changes to be made

#Create a conda environment
conda create --name <environment-name> python=<version:2.7/3.5>

#Install dependency in existing environment
conda install -c conda-forge  --name <env_name> scikit-plot  ipython notebook numpy pandas scipy matplotlib seaborn scikit-learn scikit-plot Pillow scikit-image opencv joblib mlflow requests tensorflow keras dlib 

#To create a requirements.txt file:
conda list #Gives you list of packages used for the environment

abhaymise

from matplotlib.pyplot import imshow
import numpy as np
import math
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
from PIL import Image
import requests
from io import BytesIO

def show_images(images, cols = 1, titles = None):

abhaymise

#!/usr/bin/python

from PIL import Image
import math

test_image_list = '/home/tianwei/Data/sfm_data/test_image_list' # path to the image list
with open(test_image_list, 'r') as f:
    test_images = f.readlines()
    test_images = map(str.strip, test_images)

abhaymise

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    for image_path in TEST_IMAGE_PATHS:
      image = Image.open(image_path)
      image_np = load_image_into_numpy_array(image)
      image_np_expanded = np.expand_dims(image_np, axis=0)
      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
      scores = detection_graph.get_tensor_by_name('detection_scores:0')
      classes = detection_graph.get_tensor_by_name('detection_classes:0')