Coral Edge TPU USB のサンプルの Object Detection を試す。

1.  common.py を Windows10 でも動くように修正します。

common.py
.... line 20 #EDGETPU_SHARED_LIB = 'libedgetpu.so.1' # changed by nishi 2020.7.18 import platform EDGETPU_SHARED_LIB = { 'Linux': 'libedgetpu.so.1', 'Darwin': 'libedgetpu.1.dylib', 'Windows': 'edgetpu.dll' }[platform.system()] ....

2. detect.py も少しいじってみました。 FPS の表示がでます。

detect.py
# Copyright 2019 Google LLC # # 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 that runs object detection on camera frames using OpenCV. TEST_DATA=../all_models Run face detection model: python3 detect.py \ --model ${TEST_DATA}/mobilenet_ssd_v2_face_quant_postprocess_edgetpu.tflite Run coco model: python3 detect.py \ --model ${TEST_DATA}/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite \ --labels ${TEST_DATA}/coco_labels.txt """ import argparse import collections import common import cv2 import numpy as np import os from PIL import Image import re import tflite_runtime.interpreter as tflite # add by nishi from timeit import default_timer as timer Object = collections.namedtuple('Object', ['id', 'score', 'bbox']) def load_labels(path): p = re.compile(r'\s*(\d+)(.+)') with open(path, 'r', encoding='utf-8') as f: lines = (p.match(line).groups() for line in f.readlines()) return {int(num): text.strip() for num, text in lines} 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 objects.""" boxes = common.output_tensor(interpreter, 0) class_ids = common.output_tensor(interpreter, 1) scores = common.output_tensor(interpreter, 2) count = int(common.output_tensor(interpreter, 3)) def make(i): ymin, xmin, ymax, xmax = boxes[i] return Object( id=int(class_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 scores[i] >= score_threshold] def main(): default_model_dir = '../all_models' default_model = 'mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite' default_labels = 'coco_labels.txt' parser = argparse.ArgumentParser() parser.add_argument('--model', help='.tflite model path', default=os.path.join(default_model_dir,default_model)) parser.add_argument('--labels', help='label file path', default=os.path.join(default_model_dir, default_labels)) parser.add_argument('--top_k', type=int, default=3, help='number of categories with highest score to display') parser.add_argument('--camera_idx', type=int, help='Index of which video source to use. ', default = 0) parser.add_argument('--threshold', type=float, default=0.5, help='classifier score threshold') args = parser.parse_args() print('Loading {} with {} labels.'.format(args.model, args.labels)) interpreter = common.make_interpreter(args.model) interpreter.allocate_tensors() if False: input_fs = interpreter.get_input_details() output_fs = interpreter.get_output_details() print('>input_fs=',input_fs) print('>output_fs=',output_fs) labels = load_labels(args.labels) cap = cv2.VideoCapture(args.camera_idx) # test by nishi for fps accum_time = 0 curr_fps = 0 fps = "FPS: ??" prev_time = timer() while cap.isOpened(): ret, frame = cap.read() if not ret: break cv2_im = frame cv2_im_rgb = cv2.cvtColor(cv2_im, cv2.COLOR_BGR2RGB) pil_im = Image.fromarray(cv2_im_rgb) common.set_input(interpreter, pil_im) interpreter.invoke() objs = get_output(interpreter, score_threshold=args.threshold, top_k=args.top_k) cv2_im = append_objs_to_img(cv2_im, objs, labels) # add by nishi for fps start curr_time = timer() exec_time = curr_time - prev_time prev_time = curr_time accum_time = accum_time + exec_time curr_fps = curr_fps + 1 if accum_time > 1: accum_time = accum_time - 1 fps = "FPS: " + str(curr_fps) curr_fps = 0 cv2.putText(cv2_im, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.50, color=(255, 0, 0), thickness=2) #print('>type(cv2_im)=',type(cv2_im)) #print('>cv2_im.shape=',cv2_im.shape) #print('>cv2_im.dtype=',cv2_im.dtype) cv2.imshow('frame', cv2_im) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() def append_objs_to_img(cv2_im, objs, labels): height, width, channels = cv2_im.shape for obj in objs: x0, y0, x1, y1 = list(obj.bbox) x0, y0, x1, y1 = int(x0*width), int(y0*height), int(x1*width), int(y1*height) percent = int(100 * obj.score) label = '{}% {}'.format(percent, labels.get(obj.id, obj.id)) cv2_im = cv2.rectangle(cv2_im, (x0, y0), (x1, y1), (0, 255, 0), 2) cv2_im = cv2.putText(cv2_im, label, (x0, y0+30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 2) return cv2_im if __name__ == '__main__': main()