https://blog.csdn.net/yql_617540298/article/details/81123147

这篇博客中的patch中图片数量还是一，并且更改的代码较多。

原始代码中images这个变量是list。我们可以一个patch放10张图片。

这些要放到jupyter notebook里面

val2014一共5k张图片。

#设置路径
import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt

# Root directory of the project
ROOT_DIR = os.path.abspath("../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
# Import COCO config
sys.path.append(os.path.join(ROOT_DIR, "samples/coco/"))  # To find local version
import coco

%matplotlib inline 

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

# Directory of images to run detection on
IMAGE_DIR = '/home/liutian/Mask_RCNN-master/val2014'


#配置模型config
class InferenceConfig(coco.CocoConfig):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1

#建立模型
config= InferenceConfig()
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)


#coco数据集名称建立list，由于其序号有些不连续
class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
               'bus', 'train', 'truck', 'boat', 'traffic light',
               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
               'kite', 'baseball bat', 'baseball glove', 'skateboard',
               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
               'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
               'teddy bear', 'hair drier', 'toothbrush']

#开始测试
#由于一共有50000张图片，所以我这里只设置了两张

image_list = os.listdir(IMAGE_DIR)
count = len(image_list)
print(count)
for i in range(0,2):
    path = os.path.join(IMAGE_DIR, image_list[i])
    image_name = image_list[i]
    image = skimage.io.imread(path)
    result = model.detect([image],verbose=1)
    r = result[0]
    visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], 
                            class_names, r['scores'],image_name)

#改了mrcnn里面的visualize
#加入了image_name这一个变量，这样储存的时候存的就是原本的图片名
def display_instances(image, boxes, masks, class_ids, class_names,image_name,
                      scores=None, title="",
                      figsize=(16, 16), ax=None,
                      show_mask=True, show_bbox=True,
                      colors=None, captions=None):
    """
    boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
    masks: [height, width, num_instances]
    class_ids: [num_instances]
    class_names: list of class names of the dataset
    scores: (optional) confidence scores for each box
    title: (optional) Figure title
    show_mask, show_bbox: To show masks and bounding boxes or not
    figsize: (optional) the size of the image
    colors: (optional) An array or colors to use with each object
    captions: (optional) A list of strings to use as captions for each object
    """
    # Number of instances
    N = boxes.shape[0]
    if not N:
        print("\n*** No instances to display *** \n")
    else:
        assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]

    # If no axis is passed, create one and automatically call show()
    auto_show = False
    if not ax:
        _, ax = plt.subplots(1, figsize=figsize)
        auto_show = True

    # Generate random colors
    colors = colors or random_colors(N)

    # Show area outside image boundaries.
    height, width = image.shape[:2]
    ax.set_ylim(height + 10, -10)
    ax.set_xlim(-10, width + 10)
    ax.axis('off')
    ax.set_title(title)

    masked_image = image.astype(np.uint32).copy()
    for i in range(N):
        color = colors[i]

        # Bounding box
        if not np.any(boxes[i]):
            # Skip this instance. Has no bbox. Likely lost in image cropping.
            continue
        y1, x1, y2, x2 = boxes[i]
        if show_bbox:
            p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
                                alpha=0.7, linestyle="dashed",
                                edgecolor=color, facecolor='none')
            ax.add_patch(p)

        # Label
        if not captions:
            class_id = class_ids[i]
            score = scores[i] if scores is not None else None
            label = class_names[class_id]
            x = random.randint(x1, (x1 + x2) // 2)
            caption = "{} {:.3f}".format(label, score) if score else label
        else:
            caption = captions[i]
        ax.text(x1, y1 + 8, caption,
                color='w', size=11, backgroundcolor="none")

        # Mask
        mask = masks[:, :, i]
        if show_mask:
            masked_image = apply_mask(masked_image, mask, color)

        # Mask Polygon
        # Pad to ensure proper polygons for masks that touch image edges.
        padded_mask = np.zeros(
            (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
        padded_mask[1:-1, 1:-1] = mask
        contours = find_contours(padded_mask, 0.5)
        for verts in contours:
            # Subtract the padding and flip (y, x) to (x, y)
            verts = np.fliplr(verts) - 1
            p = Polygon(verts, facecolor="none", edgecolor=color)
            ax.add_patch(p)
    ax.imshow(masked_image.astype(np.uint8))
    if auto_show:
#不用展示，直接储存
        plt.savefig("/home/liutian/Mask_RCNN-master/Mask_RCNN-master/coco_result/"+image_name)
        #plt.show()