38.He took his umbrella ______ it should rain.（2分） A in case of B in case C for fear D in time 39.Not until this term （ ） to realize how important this subject is to his future career as a diplomat.（2分） A he began B did he begin C he has begun D that he has begun 40.Great changes ______ in Shanghai since the beginning of the reform and opening-up policy.（2分） A took place B has taken place C has been taken place D have taken place 41.The larger the house is, ______.（2分） A the higher rent it is B the higher rent it would have C the higher the rent is D the higher rent it would be 42.Every time Jane has trouble ______ her car started, Sean will show up to lend a hand.（2分） A get B getting C to getting D to get44.Some bookshelves have been moved out of this office to make ______ for more computers.（2分） A space B place C room D position46. Wendy: Have you been to the new bakery on the corner? Arthur: NO, how is it? Wendy: It is heaven！________! （2分） A Their cakes are to strive for B Their cakes are to pay for C Their cakes are to struggle for D Their cakes are to die for 47.Dr. Jones, many students want to see you.（ ） they wait here or outside?（2分） A Do B Will C Shall D Would 48.（ ） who would like to go on the trip should put their names on the list. （2分） A Those B These C Somebody D The ones 49. Kate: Do you mind opening the door for me? Bob: _________. （2分） A It’s nothing B That’s all right C Yes, I’ll do it D Not at all 50.They first stop at a board _____ the menu is displayed.（2分） A when B while C where D which

38. B in case
39. B did he begin
40. D have taken place
41. C the higher the rent is
42. B getting
44. A space
46. D Their cakes are to die for
47. A Do
48. A Those
49. D Not at all
50. C where

相关问题

mask R_CNN例程

Mask R-CNN 是一种基于 Faster R-CNN 的目标检测算法，不仅可以检测出物体的位置，还可以精确地分割出物体的 mask。下面是一个 Mask R-CNN 的例程：

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
from mrcnn.config import Config


# 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 = os.path.join(ROOT_DIR, "images")


class InferenceConfig(Config):
    # 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
    NUM_CLASSES = 1 + 80  # COCO has 80 classes


config = InferenceConfig()
config.display()

# Create model object in inference mode.
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 Class names
# Index of the class in the list is its ID. For example, to get ID of
# the teddy bear class, use: class_names.index('teddy bear')
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']


# Load a random image from the images folder
file_names = next(os.walk(IMAGE_DIR))[2]
image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))

# Run detection
results = model.detect([image], verbose=1)

# Visualize results
r = results[0]
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], 
                            class_names, r['scores'])

在上面的例程中，我们首先导入所需的库和模块，包括 Mask R-CNN 的配置、模型、可视化等。然后，我们设置了模型的超参数和文件路径，以及加载了预训练的 COCO 权重。接下来，我们定义了一个 InferenceConfig 类，它继承自 Config 类，并设置了模型的 GPU 数量、每个 GPU 的图片数量和类别数。然后，我们在 inference 模式下创建了模型对象，并加载了预训练的 COCO 权重。最后，我们从图片文件夹中随机加载一张图片，并对其进行目标检测和分割，最后可视化检测结果。

mask_rcnn生成物体位置图的代码

生成物体位置图的代码主要涉及到目标检测和实例分割的相关操作，以下是一个示例代码，你可以参考它进行修改和调整：

import cv2
import numpy as np
import random
import colorsys

# 加载模型和数据集
model = MaskRCNN(mode="inference", config=config, model_dir=MODEL_DIR)
model.load_weights(COCO_MODEL_PATH, by_name=True)
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']

# 加载图像并进行目标检测和实例分割
image = cv2.imread(IMAGE_PATH)
results = model.detect([image], verbose=0)
r = results[0]

# 根据目标的边界框和掩码生成物体位置图
height, width = image.shape[:2]
mask = np.zeros((height, width, 3), dtype=np.uint8)
colors = random_colors(len(r['rois']))
for i, roi in enumerate(r['rois']):
    class_id = r['class_ids'][i]
    mask = cv2.fillPoly(mask, roi.reshape(1, -1, 2), colors[i])
    mask = cv2.putText(mask, class_names[class_id], (roi[1], roi[0] + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, colors[i], 2)

# 显示原始图像和物体位置图
cv2.imshow('Original Image', image)
cv2.imshow('Object Location Image', mask)
cv2.waitKey(0)
cv2.destroyAllWindows()

其中，`random_colors` 函数用于生成不同颜色的掩码，`cv2.fillPoly` 函数用于将掩码填充到图像中，`cv2.putText` 函数用于在目标区域绘制类别名称。你可以按照自己的需求进行修改和调整。