CocoDetection dalam PyTorch (2)
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*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")
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