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sdv_detection.py
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sdv_detection.py
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# Copyright 2019 Google LLC
#
# Modified by Nam Vu 06/15/2020
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A demo which runs object detection on camera frames using GStreamer.
Run default object detection:
python3 sdv_detection.py
Choose different camera or video
python3 sdv_detection.py --videosrc ./test_video.mp4
"""
import argparse
import collections
import common
import gstreamer
import numpy as np
import os
import re
import svgwrite
import time
import math
Object = collections.namedtuple('Object', ['id', 'score', 'bbox'])
class Point():
def __init__(self, x, y):
self.x = x
self.y = y
def distance(self, p):
return math.sqrt((self.x - p.x)**2 + (self.y - p.y)**2)
class Person():
def __init__(self, x, y, w, h):
self.x = x
self.y = y
self.width = w
self.height = h
self.centroid = Point(((2*x+w)/2), ((2*y+h)/2))
self.violates = False
def vs(self, p):
dist = self.centroid.distance(p.centroid)
if dist < self.height or dist < p.height:
print('Violation! p1: {:.2f} p2: {:.2f} dist: {:.2f}'.format(self.height, p.height, dist))
self.violates = True
p.violates = True
def draw(self, dwg):
color = 'red' if self.violates else 'lightgreen'
label = 'Too Close' if self.violates else 'Good Distance'
dwg.add(svgwrite.shapes.Circle(center=(self.centroid.x, self.centroid.y),
r=5, fill='none', stroke=color, stroke_width='3'))
dwg.add(dwg.rect(insert=(self.x, self.y), size=(self.width, self.height),
fill='none', stroke=color, stroke_width='5'))
dwg.add(dwg.rect(insert=(self.x, self.y - 25), size=(self.width, 25),
fill=color, stroke=color, stroke_width='5'))
shadow_text(dwg, self.x, self.y-5, label, 25)
def shadow_text(dwg, x, y, text, font_size=15):
dwg.add(dwg.text(text, insert=(x+1, y+1), fill='black', font_size=font_size))
dwg.add(dwg.text(text, insert=(x, y), fill='white', font_size=font_size))
def generate_svg(src_size, inference_size, inference_box, objs, text_lines):
dwg = svgwrite.Drawing('', size=src_size)
src_w, src_h = src_size
inf_w, inf_h = inference_size
box_x, box_y, box_w, box_h = inference_box
scale_x, scale_y = src_w / box_w, src_h / box_h
for y, line in enumerate(text_lines, start=1):
shadow_text(dwg, 10, y*20, line)
people = []
for obj in objs:
x0, y0, x1, y1 = list(obj.bbox)
# Relative coordinates.
x, y, w, h = x0, y0, x1 - x0, y1 - y0
# Absolute coordinates, input tensor space.
x, y, w, h = int(x * inf_w), int(y * inf_h), int(w * inf_w), int(h * inf_h)
# Subtract boxing offset.
x, y = x - box_x, y - box_y
# Scale to source coordinate space.
x, y, w, h = x * scale_x, y * scale_y, w * scale_x, h * scale_y
# Creates a Person instance
np = Person(x, y, w, h)
for p in people:
p.vs(np)
people.append(np)
for p in people:
p.draw(dwg)
return dwg.tostring()
class BBox(collections.namedtuple('BBox', ['xmin', 'ymin', 'xmax', 'ymax'])):
"""Bounding box.
Represents a rectangle which sides are either vertical or horizontal, parallel
to the x or y axis.
"""
__slots__ = ()
def get_output(interpreter, score_threshold, top_k, image_scale=1.0):
"""Returns list of detected human."""
category_ids = common.output_tensor(interpreter, 1)
boxes = common.output_tensor(interpreter, 0)
scores = common.output_tensor(interpreter, 2)
def make(i):
ymin, xmin, ymax, xmax = boxes[i]
return Object(
id=int(category_ids[i]),
score=scores[i],
bbox=BBox(xmin=np.maximum(0.0, xmin),
ymin=np.maximum(0.0, ymin),
xmax=np.minimum(1.0, xmax),
ymax=np.minimum(1.0, ymax)))
return [make(i) for i in range(top_k) if category_ids[i] == 0 and scores[i] >= score_threshold]
def main():
default_model_dir = 'model'
default_model = 'ssdlite_mobiledet_quant_postprocess_edgetpu.tflite'
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='.tflite model path',
default=os.path.join(default_model_dir,default_model))
parser.add_argument('--top_k', type=int, default=3,
help='number of categories with highest score to display')
parser.add_argument('--threshold', type=float, default=0.5,
help='classifier score threshold')
parser.add_argument('--videosrc', help='Which video source to use. ',
default='/dev/video0')
parser.add_argument('--videofmt', help='Input video format.',
default='raw',
choices=['raw', 'h264', 'jpeg'])
args = parser.parse_args()
interpreter = common.make_interpreter(args.model)
interpreter.allocate_tensors()
w, h, _ = common.input_image_size(interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(100)
def user_callback(input_tensor, src_size, inference_box):
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(interpreter, input_tensor)
interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
objs = get_output(interpreter, args.threshold, args.top_k)
end_time = time.monotonic()
text_lines = [
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
print(' '.join(text_lines))
return generate_svg(src_size, inference_size, inference_box, objs, text_lines)
result = gstreamer.run_pipeline(user_callback,
src_size=(640, 480),
appsink_size=inference_size,
videosrc=args.videosrc,
videofmt=args.videofmt)
if __name__ == '__main__':
main()