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  • matplotlib中plt用法实例

    import torch
    from models.models import Model
    import cv2
    from PIL import Image
    import numpy as np
    
    from matplotlib.animation import FFMpegWriter
    import time
    import matplotlib.pyplot as plt
    
    
    from torchvision.transforms import functional
    
    
    exp_name = './xxxx_results'
    dataRoot = 'xxxx.mp4'
    model_path = './checkpoint_best.pth'
    
    
    def pre_image(image):
        image = Image.fromarray(cv2.cvtColor(image,cv2.COLOR_BGR2RGB))
        input_image = image.copy()
        # image.show()
        height, width = image.size[1], image.size[0]
        height = round(height / 16) * 16
        width = round(width / 16) * 16
        image = image.resize((width, height), Image.BILINEAR)
    
        image = functional.to_tensor(image)
        image = functional.normalize(image, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        return input_image,torch.unsqueeze(image,0)
    
    
    if __name__ == '__main__':
    
        device = torch.device('cuda:0')
    
        # load model
        model=Model()
        checkpoint = torch.load(model_path)
        model.load_state_dict(checkpoint['model'])
    
        model.cuda()
        model.eval()
    
        # input video
        video = cv2.VideoCapture(dataRoot)
        fps = video.get(cv2.CAP_PROP_FPS)
        print(fps)
        frameCount = video.get(cv2.CAP_PROP_FRAME_COUNT)
        print(frameCount)
        size = (int(video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)))
    
    
        # metadata = dict(title='Video Test', artist='Matplotlib', comment='Movie support!')
        # writer = FFMpegWriter(fps=25, metadata=metadata)
    
            # videoWriter = cv2.VideoWriter('trans.mp4', cv2.VideoWriter_fourcc(*'MP4V'), fps, size)
        success, frame = video.read()
        index = 1
    
        figure = plt.figure()
        while success:
            # time1=time.time()
            src_image,frame = pre_image(frame)
            images = frame.to(device)
    
            # time1 = time.time()
    
    
            # ground truth
            # gt_path = dataRoot + '/den/' + filename_no_ext + '.csv'
    
            # predict
            dense_map,atten_map = model(images)
            # test = time.time() - time1
    
            dense_map = dense_map.cpu().data.numpy()[0,0,:,:]
            # test=time.time()-time1
    
            dense_pred_count = np.sum(dense_map)
            dense_map = dense_map/np.max(dense_map+1e-20)
    
            # cv2.imshow("image", dense_map)
            # cv2.waitKey(0)
    
    
            plt.subplot(121)
            plt.imshow(src_image)
            # plt.title('original image')
            plt.axis('off')
    
            plt.subplot(122)
            plt.imshow(dense_map)
            # plt.title('dense map')
            plt.text(25, 25, 'pred crowd count:%.4f ' % dense_pred_count, fontdict={'size': 10, 'color': 'red'})
            plt.axis('off')
    
            plt.tight_layout(pad=0.3, w_pad=0, h_pad=1)
    
            # anni=animation.FuncAnimation(fig, animate, init_func=init,frames=200, interval=20, blit=True)
            # anim.save('sin.gif', fps=75, writer='imagemagick')
            plt.savefig(exp_name + '/'+ str('%05d' % index) + '_' + str(int(dense_pred_count)) + '.png', bbox_inches='tight', pad_inches=0, dpi=150)
    
            # plt.show()
            plt.clf()
    
            success, frame = video.read()
            index += 1
    
        video.release()
    
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  • 原文地址:https://www.cnblogs.com/wangyarui/p/11201110.html
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