どのgstreamerパイプラインを使用しますか？

Question

私はラズベリーpiからopencvを実行している私のPCにビデオをストリーミングしようとしています。

私はパイに使用されるコードは次のとおりです。

raspivid -t 999999 -h 720 -w 1080 -fps 30 -hf -vf -b 2000000 -o - | gst-launch-1.0 -v fdsrc ! h264parse ! queue ! rtph264pay config-interval=1 pt=96 ! gdppay ! tcpserversink host=192.168.0.103 port=5000 

私はビデオをストリーミングするのGStreamerを使用しています。

私はOpenCVのVideoCapture関数のパラメータがどうあるべきか

gst-launch-1.0 -v tcpclientsrc host=192.168.0.103 port=5000 ! gdpdepay ! rtph264depay ! avdec_h264 ! videoconvert ! autovideosink sync=false 

...それは働いて得るために、私のPC上で次のコマンドを使用できますか？助けのための

おかげで...

PS：私のpythonを使用していますが、OpenCVのは、GStreamerのサポート付きでコンパイルされています。

私の完全なコード（Tensorflowも使用されている）：

import numpy as np 
import os 
import six.moves.urllib as urllib 
import sys 
import tarfile 
import tensorflow as tf 
import zipfile 
from collections import defaultdict 
from io import StringIO 
from matplotlib import pyplot as plt 
from PIL import Image 
import time 
import cv2 

# Capture Video using webcam 
stream_addr = "tcpclientsrc host=192.168.0.103 port=5000 ! gdpdepay ! rtph264depay ! video/x-h264, width=1280, height=720, format=YUY2, framerate=49/1 ! ffdec_h264 ! autoconvert ! appsink sync=false" 
# Net cat pipe 
pipe = "/dev/stdin" 
cap = cv2.VideoCapture("tcpclientsrc host=192.168.0.103 port=5000 ! gdpdepay ! rtph264depay ! ffdec_h264 ! videoconvert ! video/x-raw, format=BGR ! appsink", cv2.CAP_GSTREAMER) 
# cap = cv2.VideoCapture() 

# This is needed since the notebook is stored in the object_detection folder. 
sys.path.append("..") 

# ## Object detection imports 
# Here are the imports from the object detection module. 
from utils import label_map_util 

from utils import visualization_utils as vis_util 


# # Model preparation 

# ## Variables 
# 
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file. 
# 
# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies. 
# What model to download. 
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' 
MODEL_FILE = MODEL_NAME + '.tar.gz' 
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/' 

# Path to frozen detection graph. This is the actual model that is used for the object detection. 
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb' 

# List of the strings that is used to add correct label for each box. 
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt') 

NUM_CLASSES = 90 

# ## Download Model 
if not os.path.isfile(MODEL_FILE) and not os.path.isdir(MODEL_NAME): 
    opener = urllib.request.URLopener() 
    opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE) 
    tar_file = tarfile.open(MODEL_FILE) 
    for file in tar_file.getmembers(): 
     file_name = os.path.basename(file.name) 
     if 'frozen_inference_graph.pb' in file_name: 
      tar_file.extract(file, os.getcwd()) 


# ## Load a (frozen) Tensorflow model into memory. 
detection_graph = tf.Graph() 
with detection_graph.as_default(): 
    od_graph_def = tf.GraphDef() 
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: 
    serialized_graph = fid.read() 
    od_graph_def.ParseFromString(serialized_graph) 
    tf.import_graph_def(od_graph_def, name='') 


# ## Loading label map 
# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine 
label_map = label_map_util.load_labelmap(PATH_TO_LABELS) 
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) 
category_index = label_map_util.create_category_index(categories) 
list_classname = {} 

def printClass(s): 
    leng = len(list_classname) 
    if s is not None: 
     i = s.index(':') 
     label = s[:i] 
     score = s[i + 2:len(s) - 1] 
     if label in list_classname: 
      if int(list_classname[label]) < int(score): 
       list_classname[label] = score 
     else: 
      list_classname[label] = score 
     if len(list_classname) > leng: 
      leng = len(list_classname) 
      print(s) 

with detection_graph.as_default(): 
    with tf.Session(graph=detection_graph) as sess: 
    while True: 
     ret, image_np = cap.read() 
     # Expand dimensions since the model expects images to have shape: [1, None, None, 3] 
     image_np_expanded = np.expand_dims(image_np, axis=0) 
     image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') 
     # Each box represents a part of the image where a particular object was detected. 
     boxes = detection_graph.get_tensor_by_name('detection_boxes:0') 
     # Each score represent how level of confidence for each of the objects. 
     # Score is shown on the result image, together with the class label. 
     scores = detection_graph.get_tensor_by_name('detection_scores:0') 
     classes = detection_graph.get_tensor_by_name('detection_classes:0') 
     num_detections = detection_graph.get_tensor_by_name('num_detections:0') 
     # Actual detection. 
     (boxes, scores, classes, num_detections) = sess.run(
      [boxes, scores, classes, num_detections], 
      feed_dict={image_tensor: image_np_expanded}) 
     # Visualization of the results of a detection. 
     printClass(vis_util.visualize_boxes_and_labels_on_image_array(
      image_np, 
      np.squeeze(boxes), 
      np.squeeze(classes).astype(np.int32), 
      np.squeeze(scores), 
      category_index, 
      use_normalized_coordinates=True, 
      line_thickness=8)) 

     cv2.imshow('object detection', cv2.resize(image_np, (800,600))) 
     if cv2.waitKey(25) & 0xFF == ord('q'): 
     cv2.destroyAllWindows() 
     break 

speak_string = "" 
for k in list_classname: 
    speak_string = ("Detected, " + k + " probability is " + list_classname[k]) 
    os.system("say " + speak_string) 
    time.sleep(1) 

これは私が受け取るエラーです：

Traceback (most recent call last): 
    File "oculus.py", line 112, in <module> 
    feed_dict={image_tensor: image_np_expanded}) 
    File "/Users/SMBP/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run 
    run_metadata_ptr) 
    File "/Users/SMBP/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1093, in _run 
    np_val = np.asarray(subfeed_val, dtype=subfeed_dtype) 
    File "/Users/SMBP/anaconda3/envs/tensorflow/lib/python3.6/site-packages/numpy/core/numeric.py", line 531, in asarray 
    return array(a, dtype, copy=False, order=order) 
TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType 

「

Answer 1

次のことを試してみてください。

VideoCapture cap("tcpclientsrc host=192.168.0.103 port=5000 ! gdpdepay ! rtph264depay ! avdec_h264 ! videoconvert ! video/x-raw, format=BGR ! appsink", CAP_GSTREAMER); 

編集：

cap = cv2.VideoCapture('tcpclientsrc host=192.168.0.103 port=5000 ! gdpdepay ! rtph264depay ! avdec_h264 ! videoconvert ! video/x-raw, format=BGR ! appsink', cv2.CAP_GSTREAMER)