Mobiledet.py

# based on https://github.com/google-coral/pycoral/blob/master/examples/detect_image.py
from imutils.video import VideoStream, FPS
import argparse
import time
import cv2
from PIL import Image, ImageDraw
import numpy as np
import os 


from pycoral.adapters import common
from pycoral.adapters import detect
from pycoral.utils.dataset import read_label_file
from pycoral.utils.edgetpu import make_interpreter


def draw_objects(image, objs, labels):
    global displayImage
    global fps 
    global jarak
    global a 
    fps = FPS().start()
    draw = ImageDraw.Draw(image)
    bbox_y = 0
    
    for obj in objs:
        bbox = obj.bbox
        if obj.id == 0 :
            bbox_y = bbox.ymax
        else :
            bbox_y = 0
        draw.rectangle(
            [(bbox.xmin, bbox.ymin), (bbox.xmax, bbox.ymax)], outline='blue')
        draw.text((bbox.xmin + 10, bbox.ymin + 10), '%s\n%.2f' %
                  (labels.get(obj.id, obj.id), obj.score), fill='blue')
    displayImage = np.asarray(image)
    cv2.imshow('CORAL', cv2.resize(displayImage, (900, 650)))
    
def send_obj_data(objs):
    bbox_y = 0
    if not objs :
        bbox_y = 0
    else :
        for obj in objs:
            bbox = obj.bbox
            #print(obj.id)
            if obj.id == 0 :
                bbox_y = bbox.ymax
            else :
                bbox_y = 0           
    print("Frame Y :",bbox_y)
     

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--model', help='File path of Tflite model.', required=True)
    parser.add_argument(
        '--labels', help='File path of label file.', required=True)
    parser.add_argument('--picamera',
                        action='store_true',
                        help="Use PiCamera for image capture",
                        default=False)
    parser.add_argument(
        '-t', '--threshold', type=float, default=0.5,
        help='Classification score threshold')
    args = parser.parse_args()

    print('Loading {} with {} labels.'.format(args.model, args.labels))
    labels = read_label_file(args.labels) if args.labels else {}
    interpreter = make_interpreter(args.model)
    interpreter.allocate_tensors()

    # Inisialisasi aliran video
    #vs = VideoStream(usePiCamera=args.picamera, resolution=(640, 480)).start()
    #vs = VideoStream(2, resolution=(640, 480)).start() 
    vs = VideoStream(2).start()
    time.sleep(1)
    fps = FPS().start()
    while (True):
        try:
            # Membaca frame dari video
            screenshot = vs.read()
            image = Image.fromarray(screenshot)
            _, scale = common.set_resized_input(
            interpreter, image.size, lambda size: image.resize(size, Image.ANTIALIAS))
            interpreter.invoke()
            objs = detect.get_objects(interpreter, args.threshold, scale)
            draw_objects(image, objs, labels)
            send_obj_data(objs)
            
            if(cv2.waitKey(5) & 0xFF == ord('q')):
                fps.stop()
                break
            
            fps.update()
            fps.stop()
            print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
            print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
        except KeyboardInterrupt:
            fps.stop()
            break

    
    #print("Elapsed time:" + str(fps.elapsed()))
    #print("Approx FPS: :" + str(fps.fps()))
    fps.stop()
    print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
    print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
    cv2.destroyAllWindows()
    vs.stop()
    time.sleep(2)


if __name__ == '__main__':
    print('Edge TPU')
    main()