FastDeploy部署模型
2024-10-22 14:52:45 #人工智能 #深度学习

FastDeploy部署模型

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import fastdeploy as fd
import cv2
import numpy as np

# model_url = "./ppyoloe_crn_l_300e_coco.tgz"
# image_url = "./test_det.jpg"
# fd.download_and_decompress(model_url, path=".")
# fd.download(image_url, path=".")

# 模型推理的配置信息
option = fd.RuntimeOption()
option.use_gpu(device_id=0)
model = fd.vision.detection.PPYOLOE(
    model_file = "ppyoloe_crn_l_300e_coco/model.pdmodel",
    params_file = "ppyoloe_crn_l_300e_coco/model.pdiparams",
    config_file = "ppyoloe_crn_l_300e_coco/infer_cfg.yml",
    runtime_option = option
)

# 流媒体的设备编号
camera_id = 0
capture = cv2.VideoCapture(camera_id)
capture.set(3, 1280)
capture.set(4, 720)

fps = int(capture.get(cv2.CAP_PROP_FPS))
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
print("fps: %d, frame_count: %d" % (fps, frame_count))

# 标签，可以在infer_cfg.yml去进行查看
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"
]

# index = 1
while (1):
    ret, frame = capture.read()
    if not ret:
        break
    # print('detect frame: %d' % (index))
    # index += 1
    result = model.predict(frame)
    vis_im = fd.vision.visualize.vis_detection(frame, result, labels=labels, line_size=2, score_threshold=0.5)

    im = np.array(vis_im)

    # 计算缩放后的新尺寸
    width = int(im.shape[1] * 200 / 100)
    height = int(im.shape[0] * 200 / 100)

    # 使用cv2.resize来缩放图像
    resized_image = cv2.resize(im, (width, height))

    # cv2.namedWindow("abc", cv2.WINDOW_AUTOSIZE)
    cv2.imshow('Mask Detection', resized_image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
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2024-10-22 14:52:45 #人工智能 #深度学习
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