Tensorflow2 对象检测计数 API 教程 [英] Tensorflow2 Object Detection Counting API for tutorial

问题描述

我绞尽脑汁使用网络摄像头教程自定义 TensorFlow 对象检测，以计算从每个分类中检测到的对象数量.我使用efficientdet_d0_coco17_tpu-32 模型训练了我的自定义检测模型.我也在使用detect_from_webcam.py"教程脚本.我能够使检测工作并在屏幕上显示分类.现在我想显示检测到的每个分类的数量.

I've racked my brain at customizing the TensorFlow object detection using webcam tutorial to count how many objects are detected from each classification. I trained my custom detection model using the efficientdet_d0_coco17_tpu-32 model. I am also using the 'detect_from_webcam.py' tutorial script. I was able to get the detection working and displaying classifications on the screen. Now I would like to display how many of each classification is detected.

我查看并尝试了 TensorFlow 对象计数 API，但似乎无法理解如何将其与我的自定义训练模型集成.Counting_API

I have looked at and attempted the TensorFlow object counting API and just can't seem to understand how to integrate it with my custom trained model. Counting_API

如果这是一个愚蠢的问题，请原谅我，因为我通常从 Python 编码和机器学习开始.预先感谢您的帮助！

Forgive me if this is a silly question as I am starting out with Python coding and machine learning in general. Thanks in advance for your help!

我使用的是 Tensorflow 2.4.1 和 Python 3.7.0

I am using Tensorflow 2.4.1 and Python 3.7.0

谁能帮助我或指出我需要添加什么来计算检测到的对象?

Can anyone help me or point me to what I would need to add to count the objects detected?

这是我使用 CMD 传递给脚本的命令:

This is the command I pass to the script using CMD:

python detect_from_webcam.py -m research\object_detection\inference_graph\saved_model -l research\object_detection\Training\labelmap.pbtxt

这是脚本:

import numpy as np
import argparse
import tensorflow as tf
import cv2
import pathlib

from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
from api import object_counting_api
from utils import backbone
# patch tf1 into `utils.ops`
utils_ops.tf = tf.compat.v1

# Patch the location of gfile
tf.gfile = tf.io.gfile


def load_model(model_path):
    model = tf.saved_model.load(model_path)
    return model


def run_inference_for_single_image(model, image):
    image = np.asarray(image)
    # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
    input_tensor = tf.convert_to_tensor(image)
    # The model expects a batch of images, so add an axis with `tf.newaxis`.
    input_tensor = input_tensor[tf.newaxis,...]
    
    # Run inference
    output_dict = model(input_tensor)

    # All outputs are batches tensors.
    # Convert to numpy arrays, and take index [0] to remove the batch dimension.
    # We're only interested in the first num_detections.
    num_detections = int(output_dict.pop('num_detections'))
    output_dict = {key: value[0, :num_detections].numpy()
                   for key, value in output_dict.items()}
    output_dict['num_detections'] = num_detections
    #print(num_detections)
    # detection_classes should be ints.
    output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)
    
    # Handle models with masks:
    if 'detection_masks' in output_dict:
        # Reframe the the bbox mask to the image size.
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                                    output_dict['detection_masks'], output_dict['detection_boxes'],
                                    image.shape[0], image.shape[1])      
        detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5, tf.uint8)
        output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()
    
    return output_dict


def run_inference(model, category_index, cap):
    
    while True:
        ret, image_np = cap.read()
        
        # Actual detection.
        output_dict = run_inference_for_single_image(model, image_np)
        # Visualization of the results of a detection.
        vis_util.visualize_boxes_and_labels_on_image_array(
            image_np,
            output_dict['detection_boxes'],
            output_dict['detection_classes'],
            output_dict['detection_scores'],
            category_index,
            instance_masks=output_dict.get('detection_masks_reframed', None),
            use_normalized_coordinates=True,
            line_thickness=8)
           
        cv2.imshow('object_detection', cv2.resize(image_np, (1920, 1080)))
        if cv2.waitKey(25) & 0xFF == ord('q'):
            cap.release()
            cv2.destroyAllWindows()
            break


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Detect objects inside webcam videostream')
    parser.add_argument('-m', '--model', type=str, required=True, help='Model Path')
    parser.add_argument('-l', '--labelmap', type=str, required=True, help='Path to Labelmap')
    args = parser.parse_args()

    detection_model = load_model(args.model)
    category_index = label_map_util.create_category_index_from_labelmap(args.labelmap, use_display_name=True)
    
    cap = cv2.VideoCapture(0)
    run_inference(detection_model, category_index, cap)

推荐答案

您可以使用 tensorflow 对象计数 api.您只需将 ssd_mobilenet_v1_coco_2018_01_28 替换为您自己的包含推理图的模型.

You can count objects in an image using single_image_object_counting.py of tensorflow object counting api. You just replace ssd_mobilenet_v1_coco_2018_01_28 with your own model containing inference graph.

您可以参考如下所示的代码

You can refer code as shown below

input_video = "image.jpg"
detection_graph, category_index = backbone.set_model(MODEL_DIR)

is_color_recognition_enabled = False # set it to true for enabling the color prediction for the detected objects

# targeted objects counting
result = object_counting_api.single_image_object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled) 

print (result)

有关详细信息，您可以参考此处.

For more details you can refer here.

这篇关于Tensorflow2 对象检测计数 API 教程的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！