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Description: Patent for a pump used during the process of drilling wells. Illustrations included.Comment: Tartt didn’t play football until his senior year in high school and was passed over by top colleges. However, his best games at Samford came against high-profile programs and he excelled at the Senior Bowl all-star game. The 49ers are hoping they found an overlooked gem.
Comment: Harold is an edge pass rusher, who may be undersized to be a three-down player now, but who could function as a situational pass rusher early in his career. He mostly played with his hand on the ground at Virginia and will need to learn how to play standing up.Simon Cowell held a lavish 50th birthday party at the weekend where the biggest stars from the showbiz world conversed.
Hitting the event- held at a mansion in Hertfordshire- were Cheryl Cole, Jordan, Ant & Dec, Dermot O’ Leary, Gordon Ramsay, Dannii Minogue, Westlife, Leona Lewis, Kate Moss and Randy Jackson.
According to reports guests were greeted by images of the X Factor judge everywhere- including the bathrooms- and even waiters wearing Cowell masks.
The party- which apparently cost was £2 million- was hosted for Simon by retail billionaire Phillip Green and his wife Tina.
This weekend the X Factor final 12 were also revealed. They are…. Danyl Johnson, Lucie Jones, Stacey Soloman, Rachel Adedeji, Rikki Loney, Kandy Rain, Miss F.R.A.N.K, John and Edward, Lloyd Daniels, Jamie Archer, Olly Murs and Joseph McElderry.Suppose you were in the market for vacation property and decided to buy after the developer offered a schedule of reduced monthly assessments in the first five years of ownership. After moving in, however, you were hit with a special levy of thousands of dollars for improvements the developer could have reasonably expected. You'd be pretty angry about biting on misleading information.
A similar scenario has arisen as healthcare transformation explodes across the country, bringing fear, confusion and-yes-even greed. Too many executives are rushing into deals to expand market share and failing to be forthcoming about the effect of industry changes on communities they serve. In promising merger efficiencies they don't always deliver, they contribute to a growing crisis in credibility.
In 1991 Flagler Hospital in St. Augustine, Fla., acquired competitor St. Augustine General Hospital and achieved a monopoly over acute-care services in the city of 12,000. But Flagler promised both facilities would remain open for acute- and emergency-care services. "Patients will still have their choice of going to either healthcare campus," it said in an Oct. 30, 1991, statement.
Now Flagler wants to issue tax-exempt bonds and spend $30 million to build a 105,000-square-foot acute-care patient tower and 60,000-square-foot medical office building at its main campus. The old St. Augustine General building will be converted to an outpatient surgery facility-a fate predicted in local speculation when the deal was announced.
Closing facilities often is necessary to achieve the efficiencies sought by merged organizations. But the community must be educated about the price of such moves, including costs of closing existing facilities and additional capital expense for new construction. Otherwise executives risk alienating those whose interests they profess to serve.
In Joplin, Mo., Oak Hill Hospital failed to do its homework while pursuing a merger that would give it control over 46% of the beds in a two-county metropolitan statistical area. Oak Hill failed to consider competing offers until the federal government insisted it do so. The combination may ultimately win government approval, but executives could have done a better job of preparing the community on merger options and how they would affect costs and services.
Such lapses used to pass unnoticed. No longer. In Lorain, Ohio, the American Civil Liberties Union has filed a complaint with the state and threatened a lawsuit because it contends a merger has resulted in elimination of contraceptive services to the community.
With public activism on the increase, administrators can't afford to squander the community's goodwill.Closed back headphones for Gaming, watching youtube/movies and casual music production/listening. &lt; £200.

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import os
import cv2
import time
import argparse
import multiprocessing
import numpy as np
import tensorflow as tf

from utils.app_utils import FPS, WebcamVideoStream
from multiprocessing import Queue, Pool
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
import crnn_process

CWD_PATH = os.getcwd()

# Path to frozen detection graph. This is the actual model that is used for the object detection.
MODEL_NAME ='ssd_mobilenet_v1_coco_11_06_2017'
PATH_TO_CKPT = os.path.join(CWD_PATH, 'object_detection', MODEL_NAME, 'frozen_inference_graph.pb')

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join(CWD_PATH, 'object_detection', 'data','mscoco_label_map.pbtxt')

NUM_CLASSES = 90

# Loading label map
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
                                                            use_display_name=True)
category_index = label_map_util.create_category_index(categories)


#Crop the dedected bounding box
def process_img(image,output_q2, im_width, im_height):
    images =[]
    for box in output_q2:
        ymin, xmin, ymax, xmax = box[0]
        (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                      ymin * im_height, ymax * im_height)
        crop_img = image[int(top):int(bottom),int(left):int(right),:]
        print('---------------------------------------------------')
        print('---------------------------------------------------')
        crop_img = cv2.cvtColor(crop_img, cv2.COLOR_RGB2BGR)
        cv2.imshow('Video', crop_img)
        cv2.imwrite(os.path.join('/home/manish/Location_Detector/crop', 'out.jpg'),crop_img)
        images.append(crop_img)
    return images

def detect_objects(image_np, sess, detection_graph):
    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(image_np, axis=0)
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

    # Each box represents a part of the image where a particular object was detected.
    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    scores = detection_graph.get_tensor_by_name('detection_scores:0')
    classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_