mirror of
https://github.com/mijian1988/cameraToRtmp.git
synced 2025-09-27 01:35:52 +08:00
182 lines
9.0 KiB
Python
Executable File
182 lines
9.0 KiB
Python
Executable File
import queue
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import dlib
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import threading
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import cv2 as cv
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import subprocess as sp
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from signal import signal, SIGPIPE, SIG_DFL
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#/****************************************************************************************************/
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# import numpy as np
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# import tensorflow as tf
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# from object_detection.utils import ops as utils_ops
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# from object_detection.utils import label_map_util
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# from object_detection.utils import visualization_utils as vis_util
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#/****************************************************************************************************/
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#降低延迟操作：1.增大fps;2.减小缓存个数CAP_PROP_BUFFERSIZE;3.隔两帧进行人脸探测(failed);4.降低分辨率cv.resize;
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# 让python忽略SIGPIPE信号,并且不抛出异常
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signal(SIGPIPE,SIG_DFL)
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class Live(object):
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def __init__(self):
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self.frame_queue = queue.Queue()
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self.command = ""
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# 自行设置
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self.rtmpUrl = "rtmp://192.168.0.30:1935/live/movie"
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#self.rtmpUrl = "rtmp://192.168.126.30:1935/live/movie"
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#self.camera_path = 0
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self.camera_path = "rtsp://192.168.0.10/live/live0"
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self.detector = dlib.get_frontal_face_detector()
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# List of the strings that is used to add correct label for each box.
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#self.PATH_TO_LABELS = './object_detection/face_label_map.pbtxt'
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#self.PATH_TO_FROZEN_GRAPH = './pb/face_model.pb'
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def read_frame(self):
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print("read_frame...")
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cap = cv.VideoCapture(self.camera_path)
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# Get video information ： 30|640|480,fps改大to降低延迟效果
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#fps = int(cap.get(cv.CAP_PROP_FPS))
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fps = int(100)
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width = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
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nbuffers=int(cap.set(cv.CAP_PROP_BUFFERSIZE,2))
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print("fps:",fps,"|","width:",width,"|","height","|",height,"|","nbuffers",nbuffers)
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# ffmpeg command
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self.command = ['ffmpeg',
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'-y',
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'-f', 'rawvideo',
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'-vcodec','rawvideo',
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'-pix_fmt', 'bgr24',
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'-s', "{}x{}".format(width, height),
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'-r', str(fps),
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'-i', '-',
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'-c:v', 'libx264',
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'-pix_fmt', 'yuv420p',
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'-preset', 'ultrafast',
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'-f', 'flv',
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self.rtmpUrl]
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# read webcamera
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while(cap.isOpened()):
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ret, frame = cap.read()
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if not ret:
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print("Opening camera failed")
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break
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# resize 320*240
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cv.resize(frame,(320,240),interpolation=cv.INTER_CUBIC)
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# put frame into queue
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self.frame_queue.put(frame)
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def push_frame(self):
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print("push_frame...")
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# 防止多线程时command未被设置
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while True:
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if len(self.command) > 0:
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# 管道配置
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p = sp.Popen(self.command, stdin=sp.PIPE)
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break
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#/****************************************************************************************************/
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# # 2.process frame,探测图片中的人脸(zy他们的算法,比dlib(fps:7)慢一半fps:2.4)
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# category_index = label_map_util.create_category_index_from_labelmap(self.PATH_TO_LABELS, use_display_name = True)
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# # 重启网络
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# IMAGE_SIZE = (256, 256)
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# detection_sess = tf.compat.v1.Session()
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# with detection_sess.as_default():
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# od_graph_def = tf.compat.v1.GraphDef()
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# with tf.io.gfile.GFile(self.PATH_TO_FROZEN_GRAPH, 'rb') as fid:
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# serialized_graph = fid.read()
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# od_graph_def.ParseFromString(serialized_graph)
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# tf.import_graph_def(od_graph_def, name='')
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# ops = tf.compat.v1.get_default_graph().get_operations()
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# all_tensor_names = {output.name for op in ops for output in op.outputs}
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# tensor_dict = {}
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# for key in [
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# 'num_detections', 'detection_boxes', 'detection_scores',
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# 'detection_classes', 'detection_masks'
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# ]:
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# tensor_name = key + ':0'
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# if tensor_name in all_tensor_names:
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# tensor_dict[key] = tf.compat.v1.get_default_graph().get_tensor_by_name(tensor_name)
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# if 'detection_masks' in tensor_dict:
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# # The following processing is only for single image
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# detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
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# detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
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# # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
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# real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
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# detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
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# detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
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# detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
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# detection_masks, detection_boxes, IMAGE_SIZE[0], IMAGE_SIZE[1])
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# detection_masks_reframed = tf.cast(
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# tf.greater(detection_masks_reframed, 0.5), tf.uint8)
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# # Follow the convention by adding back the batch dimension
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# tensor_dict['detection_masks'] = tf.expand_dims(
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# detection_masks_reframed, 0)
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# image_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name('image_tensor:0')
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#/****************************************************************************************************/
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while True:
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if self.frame_queue.empty() != True:
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frame = self.frame_queue.get()
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# 1.process frame,探测图片中的人脸(dlib)
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# gray_img = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
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# dets = self.detector(gray_img, 1)
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#绘制矩形框出人脸区域
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# for e, d in enumerate(dets):
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# x1 = d.top() if d.top() > 0 else 0
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# y1 = d.bottom() if d.bottom() > 0 else 0
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# x2 = d.left() if d.left() > 0 else 0
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# y2 = d.right() if d.right() > 0 else 0
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# cv.rectangle(frame, (x2, x1), (y2, y1), (0, 255, 0), 2)
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#/****************************************************************************************************/
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# 2.process frame,探测图片中的人脸(zy他们的算法,比dlib(fps:7)慢一半fps:2.4)
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# im_data = cv.resize(frame, IMAGE_SIZE)
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# output_dict = detection_sess.run(tensor_dict,
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# feed_dict={image_tensor:
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# np.expand_dims(
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# im_data, 0)})
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# # all outputs are float32 numpy arrays, so convert types as appropriate
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# output_dict['num_detections'] = int(output_dict['num_detections'][0])
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# output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.uint8)
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# output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
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# output_dict['detection_scores'] = output_dict['detection_scores'][0]
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# if 'detection_masks' in output_dict:
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# output_dict['detection_masks'] = output_dict['detection_masks'][0]
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# vis_util.visualize_boxes_and_labels_on_image_array(
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# frame,
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# output_dict['detection_boxes'],
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# output_dict['detection_classes'],
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# output_dict['detection_scores'],
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# category_index,
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# instance_masks=output_dict.get('detection_masks'),
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# use_normalized_coordinates=True,
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# line_thickness=8)
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#/****************************************************************************************************/
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# write to pipe
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p.stdin.write(frame.tostring())
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def run(self):
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threads = [
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threading.Thread(target=Live.read_frame, args=(self,)),
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threading.Thread(target=Live.push_frame, args=(self,))
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]
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#[thread.setDaemon(True) for thread in threads]
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[thread.start() for thread in threads]
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# 测试
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if __name__ == '__main__':
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t = Live()
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t.run()
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