tensorflow - 如何使用 FasterRCNN Openimages v4?
问题描述
我似乎找不到任何有关如何使用此模型的文档。我正在尝试使用它来打印视频中出现的对象任何帮助将不胜感激我刚刚开始所以对我来说轻松一点
解决方案
经过一番挖掘和大量试验和错误后,我想出了这个
#!/home/ahmed/anaconda3/envs/TensorFlow/bin/python3.8
import tensorflow as tf
import tensorflow_hub as hub
import time,imageio,sys,pickle
# sys.argv[1] is used for taking the video path from the terminal
video = sys.argv[1]
#passing the video file to ImageIO to be read later in form of frames
video = imageio.get_reader(video)
dictionary = {}
#download and extract the model( faster_rcnn/openimages_v4/inception_resnet_v2 or
# openimages_v4/ssd/mobilenet_v2) in the same folder
module_handle = "*Path to the model folder*"
detector = hub.load(module_handle).signatures['default']
#looping over every frame in the video
for index, frames in enumerate(video):
# converting the images ( video frames ) to tf.float32 which is the only acceptable input format
image = tf.image.convert_image_dtype(frames, tf.float32)[tf.newaxis]
# passing the converted image to the model
detector_output = detector(image)
class_names = detector_output["detection_class_entities"]
scores = detector_output["detection_scores"]
# in case there are multiple objects in the frame
for i in range(len(scores)):
if scores[i] > 0.3:
#converting form bytes to string
object = class_names[i].numpy().decode("ascii")
#adding the objects that appear in the frames in a dictionary and their frame numbers
if object not in dictionary:
dictionary[object] = [index]
else:
dictionary[object].append(index)
print(dictionary)