detection.py 文件源码

def detection(img, net, transformer, labels_file):
    im = caffe.io.load_image(img)
    net.blobs['data'].data[...] = transformer.preprocess('data', im)

    start = time.clock()
    # ????
    net.forward()
    end = time.clock()
    print('detection time: %f s' % (end - start))

    # ????????
    file = open(labels_file, 'r')
    labelmap = caffe_pb2.LabelMap()
    text_format.Merge(str(file.read()), labelmap)

    loc = net.blobs['detection_out'].data[0][0]
    confidence_threshold = 0.5
    for l in range(len(loc)):
        if loc[l][2] >= confidence_threshold:
            xmin = int(loc[l][3] * im.shape[1])
            ymin = int(loc[l][4] * im.shape[0])
            xmax = int(loc[l][5] * im.shape[1])
            ymax = int(loc[l][6] * im.shape[0])
            img = np.zeros((512, 512, 3), np.uint8)  # ?????????
            cv2.rectangle(im, (xmin, ymin), (xmax, ymax), (55 / 255.0, 255 / 255.0, 155 / 255.0), 2)

            # ??????
            class_name = labelmap.item[int(loc[l][1])].display_name
            # text_font = cv2.cv.InitFont(cv2.cv.CV_FONT_HERSHEY_SCRIPT_SIMPLEX, 1, 1, 0, 3, 8)
            cv2.putText(im, class_name, (xmin, ymax), cv2.cv.CV_FONT_HERSHEY_SIMPLEX, 1, (55, 255, 155), 2)

    # ????
    plt.imshow(im, 'brg')
    plt.show()

#CPU?GPU????