Rumah pembangunan bahagian belakang Tutorial Python CocoDetection dalam PyTorch (2)

CocoDetection dalam PyTorch (2)

Jan 07, 2025 am 07:45 AM

Beli Saya Kopi☕

*Siaran saya menerangkan MS COCO.

CocoDetection() boleh menggunakan set data MS COCO seperti yang ditunjukkan di bawah. *Ini adalah untuk train2017 dengan captions_train2017.json, instances_train2017.json dan person_keypoints_train2017.json, val2017 dengan captions_val2017.json, instances_val2017.json dan person_keypoints_val2020.json dan image_testinfo.json1. 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")
Salin selepas log masuk

CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)


CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)


CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)


CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)


CocoDetection in PyTorch (2)


CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)


CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)

CocoDetection in PyTorch (2)

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