# From http://www.pyimagesearch.com/2015/12/21/increasing-webcam-fps-with-python-and-opencv/
import struct
import six
import collections
import cv2
import datetime
import subprocess as sp
import json
import numpy
import time
from threading import Thread
from matplotlib import colors
class FPS:
def __init__(self):
# store the start time, end time, and total number of frames
# that were examined between the start and end intervals
self._start = None
self._end = None
self._numFrames = 0
def start(self):
# start the timer
self._start = datetime.datetime.now()
return self
def stop(self):
# stop the timer
self._end = datetime.datetime.now()
def update(self):
# increment the total number of frames examined during the
# start and end intervals
self._numFrames += 1
def elapsed(self):
# return the total number of seconds between the start and
# end interval
return (self._end - self._start).total_seconds()
def fps(self):
# compute the (approximate) frames per second
return self._numFrames / self.elapsed()
class HLSVideoStream:
def __init__(self, src):
# initialize the video camera stream and read the first frame
# from the stream
# initialize the variable used to indicate if the thread should
# be stopped
self.stopped = False
FFMPEG_BIN = "ffmpeg"
metadata = {}
while "streams" not in metadata.keys():
print('ERROR: Could not access stream. Trying again.')
info = sp.Popen(["ffprobe",
"-v", "quiet",
"-print_format", "json",
"-show_format",
"-show_streams", src],
stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.PIPE)
out, err = info.communicate(b"ffprobe -v quiet -print_format json -show_format -show_streams http://52.91.28.88:8080/hls/live.m3u8")
metadata = json.loads(out.decode('utf-8'))
time.sleep(5)
print('SUCCESS: Retrieved stream metadata.')
self.WIDTH = metadata["streams"][0]["width"]
self.HEIGHT = metadata["streams"][0]["height"]
self.pipe = sp.Popen([ FFMPEG_BIN, "-i", src,
"-loglevel", "quiet", # no text output
"-an", # disable audio
"-f", "image2pipe",
"-pix_fmt", "bgr24",
"-vcodec", "rawvideo", "-"],
stdin = sp.PIPE, stdout = sp.PIPE)
print('WIDTH: ', self.WIDTH)
raw_image = self.pipe.stdout.read(self.WIDTH*self.HEIGHT*3) # read 432*240*3 bytes (= 1 frame)
self.frame = numpy.fromstring(raw_image, dtype='uint8').reshape((self.HEIGHT,self.WIDTH,3))
self.grabbed = self.frame is not None
def start(self):
# start the thread to read frames from the video stream
Thread(target=self.update, args=()).start()
return self
def update(self):
# keep looping infinitely until the thread is stopped
# if the thread indicator variable is set, stop the thread
while True:
if self.stopped:
return
raw_image = self.pipe.stdout.read(self.WIDTH*self.HEIGHT*3) # read 432*240*3 bytes (= 1 frame)
self.frame = numpy.fromstring(raw_image, dtype='uint8').reshape((self.HEIGHT,self.WIDTH,3))
self.grabbed = self.frame is not None
def read(self):
# return the frame most recently read
return self.frame
def stop(self):
# indicate that the thread should be stopped
self.stopped = True
class WebcamVideoStream:
def __init__(self, src, width, height):
# initialize the video camera stream and read the first frame
# from the stream
self.stream = cv2.VideoCapture(src)
self.stream.set(cv2.CAP_PROP_FRAME_WIDTH, width)
self.stream.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
(self.grabbed, self.frame) = self.stream.read()
# initialize the variable used to indicate if the thread should
# be stopped
self.stopped = False
def start(self):
# start the thread to read frames from the video stream
Thread(target=self.update, args=()).start()
return self
def update(self):
# keep looping infinitely until the thread is stopped
while True:
# if the thread indicator variable is set, stop the thread
if self.stopped:
return
# otherwise, read the next frame from the stream
(self.grabbed, self.frame) = self.stream.read()
def read(self):
# return the frame most recently read
return self.frame
def stop(self):
# indicate that the thread should be stopped
self.stopped = True
def standard_colors():
colors = [
'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque',
'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite',
'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan',
'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange',
'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet',
'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite',
'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod',
'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki',
'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue',
'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey',
'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue',
'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime',
'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid',
'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen',
'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin',
'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed',
'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed',
'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple',
'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown',
'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue',
'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow',
'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White',
'WhiteSmoke', 'Yellow', 'YellowGreen'
]
return colors
def color_name_to_rgb():
colors_rgb = []
for key, value in colors.cnames.items():
colors_rgb.append((key, struct.unpack('BBB', bytes.fromhex(value.replace('#', '')))))
return dict(colors_rgb)
def draw_boxes_and_labels(
boxes,
classes,
scores,
category_index,
instance_masks=None,
keypoints=None,
max_boxes_to_draw=20,
min_score_thresh=.5,
agnostic_mode=False):
"""Returns boxes coordinates, class names and colors
Args:
boxes: a numpy array of shape [N, 4]
classes: a numpy array of shape [N]
scores: a numpy array of shape [N] or None. If scores=None, then
this function assumes that the boxes to be plotted are groundtruth
boxes and plot all boxes as black with no classes or scores.
category_index: a dict containing category dictionaries (each holding
category index `id` and category name `name`) keyed by category indices.
instance_masks: a numpy array of shape [N, image_height, image_width], can
be None
keypoints: a numpy array of shape [N, num_keypoints, 2], can
be None
max_boxes_to_draw: maximum number of boxes to visualize. If None, draw
all boxes.
min_score_thresh: minimum score threshold for a box to be visualized
agnostic_mode: boolean (default: False) controlling whether to evaluate in
class-agnostic mode or not. This mode will display scores but ignore
classes.
"""
# Create a display string (and color) for every box location, group any boxes
# that correspond to the same location.
box_to_display_str_map = collections.defaultdict(list)
box_to_color_map = collections.defaultdict(str)
box_to_instance_masks_map = {}
box_to_keypoints_map = collections.defaultdict(list)
if not max_boxes_to_draw:
max_boxes_to_draw = boxes.shape[0]
for i in range(min(max_boxes_to_draw, boxes.shape[0])):
if scores is None or scores[i] > min_score_thresh:
box = tuple(boxes[i].tolist())
if instance_masks is not None:
box_to_instance_masks_map[box] = instance_masks[i]
if keypoints is not None:
box_to_keypoints_map[box].extend(keypoints[i])
if scores is None:
box_to_color_map[box] = 'black'
else:
if not agnostic_mode:
if classes[i] in category_index.keys():
class_name = category_index[classes[i]]['name']
else:
class_name = 'N/A'
display_str = '{}: {}%'.format(
class_name,
int(100 * scores[i]))
else:
display_str = 'score: {}%'.format(int(100 * scores[i]))
box_to_display_str_map[box].append(display_str)
if agnostic_mode:
box_to_color_map[box] = 'DarkOrange'
else:
box_to_color_map[box] = standard_colors()[
classes[i] % len(standard_colors())]
# Store all the coordinates of the boxes, class names and colors
color_rgb = color_name_to_rgb()
rect_points = []
class_names = []
class_colors = []
for box, color in six.iteritems(box_to_color_map):
ymin, xmin, ymax, xmax = box
rect_points.append(dict(ymin=ymin, xmin=xmin, ymax=ymax, xmax=xmax))
class_names.append(box_to_display_str_map[box])
class_colors.append(color_rgb[color.lower()])
return rect_points, class_names, class_colors
