请我喝杯咖啡☕
*我的帖子解释了 MS COCO。
CocoDetection() 可以使用 MS COCO 数据集，如下所示。 *这适用于带有captions_train2017.json、instances_train2017.json和person_keypoints_train2017.json的train2017，带有captions_val2017.json、instances_val2017.json和person_keypoints_val2017.json的val2017以及带有image_info_test2017.json和的test2017 image_info_test-dev2017.json:
from torchvision.datasets import CocoDetection
cap_train2017_data = CocoDetection(
root="data/coco/imgs/train2017",
annFile="data/coco/anns/trainval2017/captions_train2017.json"
)
ins_train2017_data = CocoDetection(
root="data/coco/imgs/train2017",
annFile="data/coco/anns/trainval2017/instances_train2017.json"
)
pk_train2017_data = CocoDetection(
root="data/coco/imgs/train2017",
annFile="data/coco/anns/trainval2017/person_keypoints_train2017.json"
)
len(cap_train2017_data), len(ins_train2017_data), len(pk_train2017_data)
# (118287, 118287, 118287)
cap_val2017_data = CocoDetection(
root="data/coco/imgs/val2017",
annFile="data/coco/anns/trainval2017/captions_val2017.json"
)
ins_val2017_data = CocoDetection(
root="data/coco/imgs/val2017",
annFile="data/coco/anns/trainval2017/instances_val2017.json"
)
pk_val2017_data = CocoDetection(
root="data/coco/imgs/val2017",
annFile="data/coco/anns/trainval2017/person_keypoints_val2017.json"
)
len(cap_val2017_data), len(ins_val2017_data), len(pk_val2017_data)
# (5000, 5000, 5000)
test2017_data = CocoDetection(
root="data/coco/imgs/test2017",
annFile="data/coco/anns/test2017/image_info_test2017.json"
)
testdev2017_data = CocoDetection(
root="data/coco/imgs/test2017",
annFile="data/coco/anns/test2017/image_info_test-dev2017.json"
)
len(test2017_data), len(testdev2017_data)
# (40670, 20288)
cap_train2017_data[2]
# (<pil.image.image image mode="RGB" size="640x428">,
# [{'image_id': 30, 'id': 695774,
# 'caption': 'A flower vase is sitting on a porch stand.'},
# {'image_id': 30, 'id': 696557,
# 'caption': 'White vase with different colored flowers sitting inside of it. '},
# {'image_id': 30, 'id': 699041,
# 'caption': 'a white vase with many flowers on a stage'},
# {'image_id': 30, 'id': 701216,
# 'caption': 'A white vase filled with different colored flowers.'},
# {'image_id': 30, 'id': 702428,
# 'caption': 'A vase with red and white flowers outside on a sunny day.'}])
cap_train2017_data[47]
# (<pil.image.image image mode="RGB" size="640x427">,
# [{'image_id': 294, 'id': 549895,
# 'caption': 'A man standing in front of a microwave next to pots and pans.'},
# {'image_id': 294, 'id': 556411,
# 'caption': 'A man displaying pots and utensils on a wall.'},
# {'image_id': 294, 'id': 556507,
# 'caption': 'A man stands in a kitchen and motions towards pots and pans. '},
# {'image_id': 294, 'id': 556993,
# 'caption': 'a man poses in front of some pots and pans '},
# {'image_id': 294, 'id': 560728,
# 'caption': 'A man pointing to pots hanging from a pegboard on a gray wall.'}])
cap_train2017_data[64]
# (<pil.image.image image mode="RGB" size="480x640">,
# [{'image_id': 370, 'id': 468271,
# 'caption': 'A little girl holding wet broccoli in her hand. '},
# {'image_id': 370, 'id': 471646,
# 'caption': 'The young child is happily holding a fresh vegetable. '},
# {'image_id': 370, 'id': 475471,
# 'caption': 'A little girl holds a hand full of wet broccoli. '},
# {'image_id': 370, 'id': 475663,
# 'caption': 'A little girl holds a piece of broccoli towards the camera.'},
# {'image_id': 370, 'id': 822588,
# 'caption': 'a small kid holds on to some vegetables '}])
ins_train2017_data[2]
# (<pil.image.image image mode="RGB" size="640x428">,
# [{'segmentation': [[267.38, 330.14, 281.81, ..., 269.3, 329.18]],
# 'area': 47675.66289999999, 'iscrowd': 0, 'image_id': 30,
# 'bbox': [204.86, 31.02, 254.88, 324.12], 'category_id': 64,
# 'id': 291613},
# {'segmentation': ..., 'category_id': 86, 'id': 1155486}])
ins_train2017_data[47]
# (<pil.image.image image mode="RGB" size="640x427">,
# [{'segmentation': [[27.7, 423.27, 27.7, ..., 28.66, 427.0]],
# 'area': 64624.86664999999, 'iscrowd': 0, 'image_id': 294,
# 'bbox': [27.7, 69.83, 364.91, 357.17], 'category_id': 1,
# 'id': 470246},
# {'segmentation': ..., 'category_id': 50, 'id': 708187},
# ...
# {'segmentation': ..., 'category_id': 50, 'id': 2217190}])
ins_train2017_data[67]
# (<pil.image.image image mode="RGB" size="480x640">,
# [{'segmentation': [[90.81, 155.68, 90.81, ..., 98.02, 207.57]],
# 'area': 137679.34520000007, 'iscrowd': 0, 'image_id': 370,
# 'bbox': [90.81, 24.5, 389.19, 615.5], 'category_id': 1,
# 'id': 436109},
# {'segmentation': [[257.51, 446.79, 242.45, ..., 262.02, 460.34]],
# 'area': 43818.18095, 'iscrowd': 0, 'image_id': 370,
# 'bbox': [242.45, 257.05, 237.55, 243.95], 'category_id': 56,
# 'id': 1060727}])
pk_train2017_data[2]
# (<pil.image.image image mode="RGB" size="640x428">, [])
pk_train2017_data[47]
# (<pil.image.image image mode="RGB" size="640x427">,
# [{'segmentation': [[27.7, 423.27, 27.7, ..., 28.66, 427]],
# 'num_keypoints': 11, 'area': 64624.86665, 'iscrowd': 0,
# 'keypoints': [149, 133, 2, 159, ..., 0, 0], 'image_id': 294,
# 'bbox': [27.7, 69.83, 364.91, 357.17], 'category_id': 1,
# 'id': 470246}])
pk_train2017_data[64]
# (<pil.image.image image mode="RGB" size="480x640">,
# [{'segmentation': [[90.81, 155.68, 90.81, ..., 98.02, 207.57]],
# 'num_keypoints': 12, 'area': 137679.3452, 'iscrowd': 0,
# 'keypoints': [229, 171, 2, 263, ..., 0, 0], 'image_id': 370,
# 'bbox': [90.81, 24.5, 389.19, 615.5], 'category_id': 1,
# 'id': 436109}])
cap_val2017_data[2]
# (<pil.image.image image mode="RGB" size="640x483">,
# [{'image_id': 632, 'id': 301804,
# 'caption': 'Bedroom scene with a bookcase, blue comforter and window.'},
# {'image_id': 632, 'id': 302791,
# 'caption': 'A bedroom with a bookshelf full of books.'},
# {'image_id': 632, 'id': 305425,
# 'caption': 'This room has a bed with blue sheets and a large bookcase'},
# {'image_id': 632, 'id': 305953,
# 'caption': 'A bed and a mirror in a small room.'},
# {'image_id': 632, 'id': 306511,
# 'caption': 'a bed room with a neatly made bed a window and a book shelf'}])
cap_val2017_data[47]
# (<pil.image.image image mode="RGB" size="640x480">,
# [{'image_id': 5001, 'id': 542124,
# 'caption': 'A group of people cutting a ribbon on a street.'},
# {'image_id': 5001, 'id': 545685,
# 'caption': 'A man uses a pair of big scissors to cut a pink ribbon.'},
# {'image_id': 5001, 'id': 549285,
# 'caption': 'A man cutting a ribbon at a ceremony '},
# {'image_id': 5001, 'id': 549666,
# 'caption': 'A group of people on the sidewalk watching two young children.'},
# {'image_id': 5001, 'id': 549696,
# 'caption': 'A group of people holding a large pair of scissors to a ribbon.'}])
cap_val2017_data[64]
# (<pil.image.image image mode="RGB" size="375x500">,
# [{'image_id': 6763, 'id': 708378,
# 'caption': 'A man and a women posing next to one another in front of a table.'},
# {'image_id': 6763, 'id': 709983,
# 'caption': 'A man and woman hugging in a restaurant'},
# {'image_id': 6763, 'id': 711438,
# 'caption': 'A man and woman standing next to a table.'},
# {'image_id': 6763, 'id': 711723,
# 'caption': 'A happy man and woman pose for a picture.'},
# {'image_id': 6763, 'id': 714720,
# 'caption': 'A man and woman posing for a picture in a sports bar.'}])
ins_val2017_data[2]
# (<pil.image.image image mode="RGB" size="640x483">,
# [{'segmentation': [[5.45, 269.03, 25.08, ..., 3.27, 266.85]],
# 'area': 64019.87940000001, 'iscrowd': 0, 'image_id': 632,
# 'bbox': [3.27, 266.85, 401.23, 208.25], 'category_id': 65,
# 'id': 315724},
# {'segmentation': ..., 'category_id': 64, 'id': 1610466},
# ...
# {'segmentation': {'counts': [201255, 6, 328, 6, 142, ..., 4, 34074],
# 'size': [483, 640]}, 'area': 20933, 'iscrowd': 1, 'image_id': 632,
# 'bbox': [416, 43, 153, 303], 'category_id': 84,
# 'id': 908400000632}])
ins_val2017_data[47]
# (<pil.image.image image mode="RGB" size="640x480">,
# [{'segmentation': [[210.34, 204.76, 227.6, ..., 195.24, 211.24]],
# 'area': 5645.972500000001, 'iscrowd': 0, 'image_id': 5001,
# 'bbox': [173.66, 204.76, 107.87, 238.39], 'category_id': 87,
# 'id': 1158531},
# {'segmentation': ..., 'category_id': 1, 'id': 1201627},
# ...
# {'segmentation': {'counts': [251128, 24, 451, 32, 446, ..., 43, 353],
# 'size': [480, 640]}, 'area': 10841, 'iscrowd': 1, 'image_id': 5001,
# 'bbox': [523, 26, 116, 288], 'category_id': 1, 'id': 900100005001}])
ins_val2017_data[64]
# (<pil.image.image image mode="RGB" size="375x500">,
# [{'segmentation': [[232.06, 92.6, 369.96, ..., 223.09, 93.72]],
# 'area': 11265.648799999995, 'iscrowd': 0, 'image_id': 6763
# 'bbox': [219.73, 64.57, 151.35, 126.69], 'category_id': 72,
# 'id': 30601},
# {'segmentation': ..., 'category_id': 1, 'id': 197649},
# ...
# {'segmentation': ..., 'category_id': 1, 'id': 1228674}])
pk_val2017_data[2]
# (<pil.image.image image mode="RGB" size="640x483">, [])
pk_val2017_data[47]
# (<pil.image.image image mode="RGB" size="640x480">,
# [{'segmentation': [[42.07, 190.11, 45.3, ..., 48.54, 201.98]],
# 'num_keypoints': 8, 'area': 5156.63, 'iscrowd': 0,
# 'keypoints': [58, 56, 2, 61, ..., 0, 0], 'image_id': 5001,
# 'bbox': [10.79, 32.63, 58.24, 169.35], 'category_id': 1,
# 'id': 1201627},
# {'segmentation': ..., 'category_id': 1, 'id': 1220394},
# ...
# {'segmentation': {'counts': [251128, 24, 451, 32, 446, ..., 43, 353], # 'size': [480, 640]}, 'num_keypoints': 0, 'area': 10841,
# 'iscrowd': 1, 'keypoints': [0, 0, 0, 0, ..., 0, 0],
# 'image_id': 5001, 'bbox': [523, 26, 116, 288],
# 'category_id': 1, 'id': 900100005001}])
pk_val2017_data[64]
# (<pil.image.image image mode="RGB" size="375x500">,
# [{'segmentation': [[94.38, 462.92, 141.57, ..., 100.27, 459.94]],
# 'num_keypoints': 10, 'area': 36153.48825, 'iscrowd': 0,
# 'keypoints': [228, 202, 2, 252, ..., 0, 0], 'image_id': 6763,
# 'bbox': [79.48, 131.87, 254.23, 331.05], 'category_id': 1,
# 'id': 197649},
# {'segmentation': ..., 'category_id': 1, 'id': 212640},
# ...
# {'segmentation': ..., 'category_id': 1, 'id': 1228674}])
test2017_data[2]
# (<pil.image.image image mode="RGB" size="640x427">, [])
test2017_data[47]
# (<pil.image.image image mode="RGB" size="640x406">, [])
test2017_data[64]
# (<pil.image.image image mode="RGB" size="640x427">, [])
testdev2017_data[2]
# (<pil.image.image image mode="RGB" size="640x427">, [])
testdev2017_data[47]
# (<pil.image.image image mode="RGB" size="480x640">, [])
testdev2017_data[64]
# (<pil.image.image image mode="RGB" size="640x480">, [])
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon, Rectangle
import numpy as np
from pycocotools import mask
# `show_images1()` doesn't work very well for the images with
# segmentations and keypoints so for them, use `show_images2()` which
# more uses the original coco functions.
def show_images1(data, ims, main_title=None):
file = data.root.split('/')[-1]
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))
fig.suptitle(t=main_title, y=0.9, fontsize=14)
x_crd = 0.02
for i, axis in zip(ims, axes.ravel()):
if data[i][1] and "caption" in data[i][1][0]:
im, anns = data[i]
axis.imshow(X=im)
axis.set_title(label=anns[0]["image_id"])
y_crd = 0.0
for ann in anns:
text_list = ann["caption"].split()
if len(text_list) > 9:
text = " ".join(text_list[0:10]) + " ..."
else:
text = " ".join(text_list)
plt.figtext(x=x_crd, y=y_crd, fontsize=10,
s=f'{ann["id"]}:\n{text}')
y_crd -= 0.06
x_crd += 0.325
if i == 2 and file == "val2017":
x_crd += 0.06
if data[i][1] and "segmentation" in data[i][1][0]:
im, anns = data[i]
axis.imshow(X=im)
axis.set_title(label=anns[0]["image_id"])
for ann in anns:
if "counts" in ann['segmentation']:
seg = ann['segmentation']
# rle is Run Length Encoding.
uncompressed_rle = [seg['counts']]
height, width = seg['size']
compressed_rle = mask.frPyObjects(pyobj=uncompressed_rle,
h=height, w=width)
# rld is Run Length Decoding.
compressed_rld = mask.decode(rleObjs=compressed_rle)
y_plts, x_plts = np.nonzero(a=np.squeeze(a=compressed_rld))
axis.plot(x_plts, y_plts, color='yellow')
else:
for seg in ann['segmentation']:
seg_arrs = np.split(ary=np.array(seg),
indices_or_sections=len(seg)/2)
poly = Polygon(xy=seg_arrs,
facecolor="lightgreen", alpha=0.7)
axis.add_patch(p=poly)
x_plts = [seg_arr[0] for seg_arr in seg_arrs]
y_plts = [seg_arr[1] for seg_arr in seg_arrs]
axis.plot(x_plts, y_plts, color='yellow')
x, y, w, h = ann['bbox']
rect = Rectangle(xy=(x, y), width=w, height=h,
linewidth=3, edgecolor='r',
facecolor='none', zorder=2)
axis.add_patch(p=rect)
if data[i][1] and 'keypoints' in data[i][1][0]:
kps = ann['keypoints']
kps_arrs = np.split(ary=np.array(kps),
indices_or_sections=len(kps)/3)
x_plts = [kps_arr[0] for kps_arr in kps_arrs]
y_plts = [kps_arr[1] for kps_arr in kps_arrs]
nonzeros_x_plts = []
nonzeros_y_plts = []
for x_plt, y_plt in zip(x_plts, y_plts):
if x_plt == 0 and y_plt == 0:
continue
nonzeros_x_plts.append(x_plt)
nonzeros_y_plts.append(y_plt)
axis.scatter(x=nonzeros_x_plts, y=nonzeros_y_plts,
color='yellow')
# ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ Bad result ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓
# axis.plot(nonzeros_x_plts, nonzeros_y_plts)
if not data[i][1]:
im, _ = data[i]
axis.imshow(X=im)
fig.tight_layout()
plt.show()
ims = (2, 47, 64)
show_images1(data=cap_train2017_data, ims=ims,
main_title="cap_train2017_data")
show_images1(data=ins_train2017_data, ims=ims,
main_title="ins_train2017_data")
show_images1(data=pk_train2017_data, ims=ims,
main_title="pk_train2017_data")
print()
show_images1(data=cap_val2017_data, ims=ims,
main_title="cap_val2017_data")
show_images1(data=ins_val2017_data, ims=ims,
main_title="ins_val2017_data")
show_images1(data=pk_val2017_data, ims=ims,
main_title="pk_val2017_data")
print()
show_images(data=test2017_data, ims=ims,
main_title="test2017_data")
show_images(data=testdev2017_data, ims=ims,
main_title="testdev2017_data")
# `show_images2()` works very well for the images with segmentations and
# keypoints.
def show_images2(data, index, main_title=None):
img_set = data[index]
img, img_anns = img_set
if img_anns and "segmentation" in img_anns[0]:
img_id = img_anns[0]['image_id']
coco = data.coco
def show_image(imgIds, areaRng=[],
iscrowd=None, draw_bbox=False):
plt.figure(figsize=(11, 8))
plt.imshow(X=img)
plt.suptitle(t=main_title, y=1, fontsize=14)
plt.title(label=img_id, fontsize=14)
anns_ids = coco.getAnnIds(imgIds=img_id,
areaRng=areaRng, iscrowd=iscrowd)
anns = coco.loadAnns(ids=anns_ids)
coco.showAnns(anns=anns, draw_bbox=draw_bbox)
plt.show()
show_image(imgIds=img_id, draw_bbox=True)
show_image(imgIds=img_id, draw_bbox=False)
show_image(imgIds=img_id, iscrowd=False, draw_bbox=True)
show_image(imgIds=img_id, areaRng=[0, 5000], draw_bbox=True)
elif img_anns and not "segmentation" in img_anns[0]:
plt.figure(figsize=(11, 8))
img_id = img_anns[0]['image_id']
plt.imshow(X=img)
plt.suptitle(t=main_title, y=1, fontsize=14)
plt.title(label=img_id, fontsize=14)
plt.show()
elif not img_anns:
plt.figure(figsize=(11, 8))
plt.imshow(X=img)
plt.suptitle(t=main_title, y=1, fontsize=14)
plt.show()
show_images2(data=ins_val2017_data, index=2,
main_title="ins_val2017_data")
print()
show_images2(data=pk_val2017_data, index=2,
main_title="pk_val2017_data")
print()
show_images2(data=ins_val2017_data, index=47,
main_title="ins_val2017_data")
print()
show_images2(data=pk_val2017_data, index=47,
main_title="pk_val2017_data")
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Damn Vulnerable Web App (DVWA) 是一个PHP/MySQL的Web应用程序，非常容易受到攻击。它的主要目标是成为安全专业人员在合法环境中测试自己的技能和工具的辅助工具，帮助Web开发人员更好地理解保护Web应用程序的过程，并帮助教师/学生在课堂环境中教授/学习Web应用程序安全。DVWA的目标是通过简单直接的界面练习一些最常见的Web漏洞，难度各不相同。请注意，该软件中
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