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mobilenetv2ssd-sync-usbcam.py
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mobilenetv2ssd-sync-usbcam.py
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import argparse
#import platform
import sys
import numpy as np
import cv2
import time
#from PIL import Image
from time import sleep
import multiprocessing as mp
try:
from tflite_runtime.interpreter import Interpreter
except:
import tensorflow as tf
lastresults = None
processes = []
frameBuffer = None
results = None
fps = ""
detectfps = ""
framecount = 0
detectframecount = 0
time1 = 0
time2 = 0
box_color = (255, 128, 0)
box_thickness = 1
label_background_color = (125, 175, 75)
label_text_color = (255, 255, 255)
percentage = 0.0
LABELS = [
'???','person','bicycle','car','motorcycle','airplane','bus','train','truck','boat',
'traffic light','fire hydrant','???','stop sign','parking meter','bench','bird','cat','dog','horse',
'sheep','cow','elephant','bear','zebra','giraffe','???','backpack','umbrella','???',
'???','handbag','tie','suitcase','frisbee','skis','snowboard','sports ball','kite','baseball bat',
'baseball glove','skateboard','surfboard','tennis racket','bottle','???','wine glass','cup','fork','knife',
'spoon','bowl','banana','apple','sandwich','orange','broccoli','carrot','hot dog','pizza',
'donut','cake','chair','couch','potted plant','bed','???','dining table','???','???',
'toilet','???','tv','laptop','mouse','remote','keyboard','cell phone','microwave','oven',
'toaster','sink','refrigerator','???','book','clock','vase','scissors','teddy bear','hair drier',
'toothbrush']
class ObjectDetectorLite():
def __init__(self, model_path='detect.tflite', num_threads=12):
try:
self.interpreter = Interpreter(model_path=model_path, num_threads=num_threads)
except:
self.interpreter = tf.lite.Interpreter(model_path=model_path, num_threads=num_threads)
try:
self.interpreter.allocate_tensors()
except:
pass
self.input_details = self.interpreter.get_input_details()
self.output_details = self.interpreter.get_output_details()
def _boxes_coordinates(
self,
image,
boxes,
classes,
scores,
max_boxes_to_draw=20,
min_score_thresh=.5
):
if not max_boxes_to_draw:
max_boxes_to_draw = boxes.shape[0]
number_boxes = min(max_boxes_to_draw, boxes.shape[0])
person_boxes = []
for i in range(number_boxes):
if scores is None or scores[i] > min_score_thresh:
box = tuple(boxes[i].tolist())
ymin, xmin, ymax, xmax = box
_, im_height, im_width, _ = image.shape
left, right, top, bottom = [int(z) for z in (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)]
person_boxes.append([(left, top), (right, bottom), scores[i], LABELS[classes[i]]])
return person_boxes
def detect(self, image, threshold=0.1):
# run model
self.interpreter.set_tensor(self.input_details[0]['index'], image)
start_time = time.time()
self.interpreter.invoke()
stop_time = time.time()
print("time: ", stop_time - start_time)
# get results
boxes = self.interpreter.get_tensor(self.output_details[0]['index'])
classes = self.interpreter.get_tensor(self.output_details[1]['index'])
scores = self.interpreter.get_tensor(self.output_details[2]['index'])
num = self.interpreter.get_tensor(self.output_details[3]['index'])
# Find detected boxes coordinates
return self._boxes_coordinates(
image,
np.squeeze(boxes[0]),
np.squeeze(classes[0]+1).astype(np.int32),
np.squeeze(scores[0]),
min_score_thresh=threshold,
)
def overlay_on_image(frames, object_infos, camera_width, camera_height):
color_image = frames
if isinstance(object_infos, type(None)):
return color_image
img_cp = color_image.copy()
for obj in object_infos:
box_left = int(obj[0][0])
box_top = int(obj[0][1])
box_right = int(obj[1][0])
box_bottom = int(obj[1][1])
cv2.rectangle(img_cp, (box_left, box_top), (box_right, box_bottom), box_color, box_thickness)
percentage = int(obj[2] * 100)
label_text = obj[3] + " (" + str(percentage) + "%)"
label_size = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)[0]
label_left = box_left
label_top = box_top - label_size[1]
if (label_top < 1):
label_top = 1
label_right = label_left + label_size[0]
label_bottom = label_top + label_size[1]
cv2.rectangle(img_cp, (label_left - 1, label_top - 1), (label_right + 1, label_bottom + 1), label_background_color, -1)
cv2.putText(img_cp, label_text, (label_left, label_bottom), cv2.FONT_HERSHEY_SIMPLEX, 0.5, label_text_color, 1)
cv2.putText(img_cp, fps, (camera_width-170,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38,0,255), 1, cv2.LINE_AA)
cv2.putText(img_cp, detectfps, (camera_width-170,30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38,0,255), 1, cv2.LINE_AA)
return img_cp
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="models/mobilenet_ssd_v2_coco_quant_postprocess.tflite", help="Path of the detection model.")
parser.add_argument("--usbcamno", type=int, default=0, help="USB Camera number.")
parser.add_argument("--camera_type", default="usb_cam", help="set usb_cam or raspi_cam")
parser.add_argument("--camera_width", type=int, default=640, help="width.")
parser.add_argument("--camera_height", type=int, default=480, help="height.")
parser.add_argument("--vidfps", type=int, default=150, help="Frame rate.")
parser.add_argument("--num_threads", type=int, default=4, help="Threads.")
args = parser.parse_args()
model = args.model
usbcamno = args.usbcamno
camera_type = args.camera_type
camera_width = args.camera_width
camera_height = args.camera_height
vidfps = args.vidfps
num_threads = args.num_threads
if camera_type == "usb_cam":
cam = cv2.VideoCapture(usbcamno)
cam.set(cv2.CAP_PROP_FPS, vidfps)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
window_name = "USB Camera"
elif camera_type == "raspi_cam":
from picamera.array import PiRGBArray
from picamera import PiCamera
cam = PiCamera()
cam.resolution = (camera_width, camera_height)
stream = PiRGBArray(cam)
window_name = "Raspi Camera"
else:
print('[Error] --camera_type: wrong device')
parser.print_help()
sys.exit()
print(window_name)
cv2.namedWindow(window_name, cv2.WINDOW_AUTOSIZE)
detector = ObjectDetectorLite(model, num_threads)
while True:
t1 = time.perf_counter()
if camera_type == 'raspi_cam':
cam.capture(stream, 'bgr', use_video_port=True)
color_image = stream.array
stream.truncate(0)
else:
ret, color_image = cam.read()
if not ret:
continue
prepimg = cv2.resize(color_image, (300, 300))
frames = prepimg.copy()
prepimg = prepimg[:, :, ::-1].copy()
prepimg = np.expand_dims(prepimg, axis=0)
prepimg = prepimg.astype('uint8')
res = detector.detect(prepimg, 0.4)
imdraw = overlay_on_image(frames, res, camera_width, camera_height)
imdraw = cv2.resize(imdraw, (camera_width, camera_height))
cv2.imshow(window_name, imdraw)
if cv2.waitKey(1)&0xFF == ord('q'):
break
# FPS calculation
framecount += 1
if framecount >= 15:
fps = "(Playback) {:.1f} FPS".format(time1/15)
framecount = 0
detectframecount = 0
time1 = 0
time2 = 0
t2 = time.perf_counter()
elapsedTime = t2-t1
time1 += 1/elapsedTime
time2 += elapsedTime