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 .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.
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