console.timeEnd('predict1'); const c = document.getElementById('canvas'); const context = c.getContext('2d'); context.drawImage(image, 0, 0); context.font = '10px Arial'; console.log(result); console.log('number of detections: ', result.length); for (let i = 0; i < result....
[0, 0], [real_num_detection, -1]) detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1]) detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks( detection_masks, detection_boxes, image.shape[1], ...
这个命令表示将C:\Users\lukir\Documents\Source\tensorflow\models\research\object_detection\protos\下所有文件编译成python格式 4. 下载模型文件 http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gzdownload.tensorflow.org/models/object_detection/ssd_mobilenet_v...
PATH_TO_TEST_IMAGES_DIR = '/home/daniel/tensorflow/models/research/object_detection/test_images' TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 5)] # Size, in inches, of the output images. IMAGE_SIZE = (12, 8) with dete...
TensorFlow.js Object Detection SSD Lite Mobilenet V2 SSD Mobilenet v1 SSD Mobilenet v2 Run Toggle Image
Mask RCNN on Kites Image 视频测试 对我来说,最有意思的是用 YouTube 视频来测试这个模型。我从 YouTube 上下载了好几条视频,开始了视频测试。 视频测试的主要步骤: 1. 使用 VideoFileClip 功能从视频中提取出每个帧; 2. 使用 fl_image 功能对视频中截取的每张图片进行物体检测,然后用修改后的视频图片替...
1"path_of_protobuf's bin"./bin/protoc object_detection/protos/ 要检查是否有效,可以转到models> object_detection> protos中的protos文件夹,在那里可以看到每个proto文件都创建了一个python文件。 主要代码 设置环境后,需要转到“”目录并创建一个新的python文...
create_category_index(categories) # ## Helper code # In[8]: def load_image_into_numpy_array(image): (im_width, im_height) = image.size return np.array(image.getdata()).reshape( (im_height, im_width, 3)).astype(np.uint8) # # Detection # In[9]: # For the sake of simplicity...
1.3. 运行(成功后会在image下生成两个csv文件) python xml_to_csv.py 2. 将csv文件转换为tfrecord文件 2.1. 新建generate_tfrecord.py放入上图目录(models\research\object_detection)下。 2.2. 代码编写,这里要注意class_text_to_int函数的值需要修改标注的项已经定义的映射值。
num_detections= detection_graph.get_tensor_by_name('num_detections:0')forimage_pathinTEST_IMAGE_PATHS: image=Image.open(image_path) image_np=load_image_into_numpy_array(image) image_np_expanded= np.expand_dims(image_np, axis=0)