告别性能瓶颈：使用 Google Coral TPU 为树莓派注入强大AI计算力！

echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.listcurl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -sudo apt-get update

sudo apt-get install libedgetpu1-std

sudo apt-get install python3-pycoral --yes

pip3 show tflite_runtime

Name: tflite-runtimeVersion: 2.5.0Summary: TensorFlow Lite is for mobile and embedded devices.Home-page: https://www.tensorflow.org/lite/Author: Google, LLCAuthor-email: packages@tensorflow.orgLicense: Apache 2.0Location: /usr/lib/python3/dist-packagesRequires:Required-by: pycoral

echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.listcurl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -sudo apt-get updatesudo apt-get install python3-tflite-runtime

mkdir google-coral && cd google-coralgit clone https://github.com/google-coral/examples-camera --depth 1

cd examples-camerash download_models.sh

cd opencvbash install_requirements.sh

python3 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 argparseimport cv2import os from pycoral.adapters.common import input_sizefrom pycoral.adapters.detect import get_objectsfrom pycoral.utils.dataset import read_label_filefrom pycoral.utils.edgetpu import make_interpreterfrom pycoral.utils.edgetpu import run_inference 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=0.1,                        help='classifier score threshold')    args = parser.parse_args()     print('Loading {} with {} labels.'.format(args.model, args.labels))    interpreter = make_interpreter(args.model)    interpreter.allocate_tensors()    labels = read_label_file(args.labels)    inference_size = input_size(interpreter)     cap = cv2.VideoCapture(args.camera_idx)     while cap.isOpened():        ret, frame = cap.read()        if not ret:            break        cv2_im = frame         cv2_im_rgb = cv2.cvtColor(cv2_im, cv2.COLOR_BGR2RGB)        cv2_im_rgb = cv2.resize(cv2_im_rgb, inference_size)        run_inference(interpreter, cv2_im_rgb.tobytes())        objs = get_objects(interpreter, args.threshold)[:args.top_k]        cv2_im = append_objs_to_img(cv2_im, inference_size, objs, labels)         cv2.imshow('frame', cv2_im)        if cv2.waitKey(1) & 0xFF == ord('q'):            break     cap.release()    cv2.destroyAllWindows() def append_objs_to_img(cv2_im, inference_size, objs, labels):    height, width, channels = cv2_im.shape    scale_x, scale_y = width / inference_size[0], height / inference_size[1]    for obj in objs:        bbox = obj.bbox.scale(scale_x, scale_y)        x0, y0 = int(bbox.xmin), int(bbox.ymin)        x1, y1 = int(bbox.xmax), int(bbox.ymax)         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() 

objs = get_objects(interpreter, args.threshold)[:args.top_k]

[Object(id=16, score=0.5, bbox=BBox(xmin=-2, ymin=102, xmax=158, ymax=296)), Object(id=0, score=0.16015625, bbox=BBox(xmin=6, ymin=114, xmax=270, ymax=300)), Object(id=61, score=0.12109375, bbox=BBox(xmin=245, ymin=166, xmax=301, ymax=302))]

{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 12: 'stop sign', 13: 'parking meter', 14: 'bench', 15: 'bird', 16: 'cat', 17: 'dog', 18: 'horse', 19: 'sheep', 20: 'cow', 21: 'elephant', 22: 'bear', 23: 'zebra',24: 'giraffe', 26: 'backpack', 27: 'umbrella', 30: 'handbag', 31: 'tie', 32: 'suitcase', 33: 'frisbee', 34: 'skis', 35: 'snowboard', 36: 'sports ball', 37: 'kite', 38: 'baseball bat', 39: 'baseball glove', 40: 'skateboard', 41: 'surfboard', 42: 'tennis racket', 43: 'bottle', 45: 'wine glass', 46: 'cup', 47: 'fork', 48: 'knife', 49: 'spoon', 50: 'bowl', 51: 'banana', 52: 'apple', 53: 'sandwich', 54: 'orange', 55: 'broccoli', 56: 'carrot', 57: 'hot dog', 58: 'pizza', 59: 'donut', 60: 'cake', 61: 'chair', 62: 'couch', 63: 'potted plant',64: 'bed', 66: 'dining table', 69: 'toilet', 71: 'tv', 72: 'laptop', 73: 'mouse', 74: 'remote', 75: 'keyboard', 76: 'cell phone', 77: 'microwave', 78: 'oven', 79: 'toaster', 80: 'sink', 81: 'refrigerator', 83: 'book', 84: 'clock', 85: 'vase', 86: 'scissors', 87: 'teddy bear', 88: 'hair drier', 89: 'toothbrush'}

found_scores = [o.score for o in objs if o.id == 5]if len(found_scores) > 0 and max(found_scores) >= 0.8:# do something