pil example picam detection TPU (snippet)

Python pil example 'picam detection TPU'

Functions in program:

• def detect_from_camera():
• def detect_from_image():
• def cv2pil(image_cv):

Modules used in program:

• import cv2
• import picamera.array
• import picamera
• import time

python picam detection TPU

Python pil example: picam detection TPU

import time
import picamera
import picamera.array
import cv2
from PIL import Image
from edgetpu.detection.engine import DetectionEngine

MODEL_NAME = "mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite"

def cv2pil(image_cv):
    image_cv = cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB)
    image_pil = Image.fromarray(image_cv)
    image_pil = image_pil.convert('RGB')
    return image_pil

label2string = \
{
    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",
}

def detect_from_image():
    # Load model and prepare TPU engine
    engine = DetectionEngine(MODEL_NAME)

    # prepara input image
    img_org = cv2.imread('input.jpg')
    #    cv2.imshow('image', img)
    pil_img = cv2pil(cv2.resize(img_org, (300, 300)))

    # Run inference
    ans = engine.DetectWithImage(pil_img, threshold=0.5, keep_aspect_ratio=True, relative_coord=True, top_k=10)

    # Retrieve results
    if ans:
        for obj in ans:
            print(('-----------------------------------------'))
            print('label = ', label2string[obj.label_id])
            print(('score = ', obj.score))
            box = obj.bounding_box.flatten().tolist()
            print(('box = ', box))

            x0 = int(box[0] * img_org.shape[1])
            y0 = int(box[1] * img_org.shape[0])
            x1 = int(box[2] * img_org.shape[1])
            y1 = int(box[3] * img_org.shape[0])
            cv2.rectangle(img_org, (x0, y0), (x1, y1), (255, 0, 0), 2)
            cv2.rectangle(img_org, (x0, y0), (x0 + 100, y0 - 30), (255, 0, 0), -1)
            cv2.putText(img_org,
                    str(label2string[obj.label_id]),
                    (x0, y0),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    1,
                    (255, 255, 255),
                    2)

        cv2.imshow('image', img_org)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

def detect_from_camera():
    # Load model and prepare TPU engine
    engine = DetectionEngine(MODEL_NAME)

    # Start camera capturing
    with picamera.PiCamera() as camera:
        with picamera.array.PiRGBArray(camera) as stream:
            camera.resolution = (640, 480)
            while True:
                start = time.time()
                # capture image
                camera.capture(stream, 'bgr', use_video_port=True)
                image_display = stream.array
                pil_img = cv2pil(cv2.resize(stream.array, (300, 300)))

                # Run inference
                ans = engine.DetectWithImage(pil_img, threshold=0.5, keep_aspect_ratio=True, relative_coord=True, top_k=10)

                # Retrieve results
                if ans:
                    for obj in ans:
                        print(('-----------------------------------------'))
                        print('label = ', label2string[obj.label_id])
                        print(('score = ', obj.score))
                        box = obj.bounding_box.flatten().tolist()
                        print(('box = ', box))

                        x0 = int(box[0] * image_display.shape[1])
                        y0 = int(box[1] * image_display.shape[0])
                        x1 = int(box[2] * image_display.shape[1])
                        y1 = int(box[3] * image_display.shape[0])
                        cv2.rectangle(image_display, (x0, y0), (x1, y1), (255, 0, 0), 2)
                        cv2.rectangle(image_display, (x0, y0), (x0 + 100, y0 - 30), (255, 0, 0), -1)
                        cv2.putText(image_display,
                                str(label2string[obj.label_id]),
                                (x0, y0),
                                cv2.FONT_HERSHEY_SIMPLEX,
                                1,
                                (255, 255, 255),
                                2)

                # Draw the result
                cv2.imshow('image', image_display)
                if cv2.waitKey(1) & 0xFF == ord("q"):
                    break

                print('inference time = ', engine.get_inference_time() , '[msec]')
                elapsed_time = time.time() - start
                print('total time = ', elapsed_time * 1000 , '[msec] (', 1 / elapsed_time, ' fps)')
                stream.seek(0)
                stream.truncate()
            cv2.destroyAllWindows()


if __name__ == '__main__':
    detect_from_camera()
    detect_from_image()



'''
for Raspberry Pi 3

cd ~/
wget https://dl.google.com/coral/edgetpu_api/edgetpu_api_latest.tar.gz -O edgetpu_api.tar.gz --trust-server-names
tar xzf edgetpu_api.tar.gz
cd edgetpu_api
bash ./install.sh

sudo apt install python3-picamera
python3 picam_capture.py 

https://dl.google.com/coral/canned_models/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite
'''

Python links

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