caffe-ssd 检测视频并保存

Myth丶恋晨 2022-01-28 04:15 175阅读 0赞

caffe-ssd版本的代码中 /examples下有ssd\_detect.ipynd的图像检测实例  
/examples/ssd/下有ssd\_pascal\_video.py的视频检测实例，但没有保存视频接口

博主将图像检测的修改为为视频接口并保存，代码如下：  
github地址：[https://github.com/huangzicheng/caffe-ssd-video-detect][https_github.com_huangzicheng_caffe-ssd-video-detect]

import numpy as np
    import matplotlib.pyplot as plt
    import cv2
    #%matplotlib inline
    #from __future__ import * 
    #plt.rcParams['figure.figsize'] = (10, 10)
    #plt.rcParams['image.interpolation'] = 'nearest'
    #plt.rcParams['image.cmap'] = 'gray'
    
    # Make sure that caffe is on the python path:
    caffe_root = '/home/xxx/caffe-ssd'  # this file is expected to be in {caffe_root}/examples
    import os
    os.chdir(caffe_root)
    import sys
    sys.path.insert(0, 'python')
    
    import caffe
    caffe.set_device(0)
    caffe.set_mode_gpu()
    
    
    from google.protobuf import text_format
    from caffe.proto import caffe_pb2
    
    # load PASCAL VOC labels
    labelmap_file = '/home/xxx/caffe-ssd/VOCdevkit/labelmap_voc.prototxt'
    file = open(labelmap_file, 'r')
    labelmap = caffe_pb2.LabelMap()
    text_format.Merge(str(file.read()), labelmap)
    
    def get_labelname(labelmap, labels):
        num_labels = len(labelmap.item)
        labelnames = []
        if type(labels) is not list:
            labels = [labels]
        for label in labels:
            found = False
            for i in xrange(0, num_labels):
                if label == labelmap.item[i].label:
                    found = True
                    labelnames.append(labelmap.item[i].display_name)
                    break
            assert found == True
        return labelnames
    
    model_def = 'models/VGGNet/VOC0712/SSD_300x300/deploy.prototxt'
    model_weights = 'models/VGGNet/VOC0712/SSD_300x300/VGG_VOC0712_SSD_300x300_iter_31292.caffemodel'
    
    net = caffe.Net(model_def,      # defines the structure of the model
                    model_weights,  # contains the trained weights
                    caffe.TEST)     # use test mode (e.g., don't perform dropout)
    
    # input preprocessing: 'data' is the name of the input blob == net.inputs[0]
    transformer = caffe.io.Transformer({ 'data': net.blobs['data'].data.shape})
    transformer.set_transpose('data', (2, 0, 1))
    transformer.set_mean('data', np.array([104,117,123])) # mean pixel
    transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]
    transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB
    
    
    # set net to batch size of 1
    image_resize = 300
    net.blobs['data'].reshape(1,3,image_resize,image_resize)
    
    outputFile='/home/xxx/pic_test/out1.avi'
    
    
    #cap = cv2.VideoCapture("Minions_banana.mp4")
    import math 
    cap = cv2.VideoCapture('/xxx/pic_test/cars.mp4')
    vid_writer = cv2.VideoWriter(outputFile, cv2.VideoWriter_fourcc('M','J','P','G'), 28, (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))
    
    while cap.isOpened():
        ret, image1 = cap.read()
        if ret == True:
    
           
            #cv2.imshow('img',image)
        #image = caffe.io.load_image('/home/huanglong/002795.jpg')
        #plt.imshow(image)
           # image1=cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            
            img = image1/ 255.0
            transformed_image = transformer.preprocess('data', img)
            print transformed_image.shape
            net.blobs['data'].data[...] = transformed_image
        
        # Forward pass.
            detections = net.forward()['detection_out']
        # Parse the outputs.()['detection_out']
        
            det_label = detections[0,0,:,1]
            det_conf = detections[0,0,:,2]
            det_xmin = detections[0,0,:,3]
            det_ymin = detections[0,0,:,4]
            det_xmax = detections[0,0,:,5]
            det_ymax = detections[0,0,:,6]
        
        # Get detections with confidence higher than 0.6.
            top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]
        
            top_conf = det_conf[top_indices]
            top_label_indices = det_label[top_indices].tolist()
            top_labels = get_labelname(labelmap, top_label_indices)
            top_xmin = det_xmin[top_indices]
            top_ymin = det_ymin[top_indices]
            top_xmax = det_xmax[top_indices]
            top_ymax = det_ymax[top_indices]
        
        
        #colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()
            colors=[(0,0,0),(128,0,0),(0,128,0)]
        #fig=plt.figure()
        #currentAxis = plt.gca()
        
        
            for i in xrange(top_conf.shape[0]):
                xmin = int(round(top_xmin[i] * image1.shape[1]))
                ymin = int(round(top_ymin[i] * image1.shape[0]))
                xmax = int(round(top_xmax[i] * image1.shape[1]))
                ymax = int(round(top_ymax[i] * image1.shape[0]))
                print xmin, ymin, xmax, ymax
           
                score = top_conf[i]
                label = int(top_label_indices[i])
                label_name = top_labels[i]
                display_txt = '%s: %.2f'%(label_name, score)
                coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
                print coords,
                color = colors[label]
                cv2.rectangle(image1, (xmin,ymin), (xmax,ymax), color, 1)
            #currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
            #currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5}) 
                font = cv2.FONT_HERSHEY_COMPLEX
                cv2.putText(image1, display_txt, (xmin,ymin), font, 2, color, 2)
        
        
            #cv2.imshow('img',image1) 
            vid_writer.write(image1)
        if cv2.waitKey(1000) & 0xFF == ord('q'):
                break
           
    
        #
    cap.release()
    cv2.destroyAllWindows()

[https_github.com_huangzicheng_caffe-ssd-video-detect]: https://github.com/huangzicheng/caffe-ssd-video-detect