import os  import albumentations as A import cv2 import numpy as np from matplotlib import pyplot as plt from skimage.color import label2rgb 

BOX_COLOR = (255, 0, 0) # Red TEXT_COLOR = (255, 255, 255) # White   def visualize_bbox(img, bbox, color=BOX_COLOR, thickness=2, **kwargs):  x_min, y_min, w, h = bbox  x_min, x_max, y_min, y_max = int(x_min), int(x_min + w), int(y_min), int(y_min + h)  cv2.rectangle(img, (x_min, y_min), (x_max, y_max), color=color, thickness=thickness)  return img   def visualize_titles(img, bbox, title, font_thickness=2, font_scale=0.35, **kwargs):  x_min, y_min = bbox[:2]  x_min = int(x_min)  y_min = int(y_min)  ((text_width, text_height), _) = cv2.getTextSize(title, cv2.FONT_HERSHEY_SIMPLEX, font_scale, font_thickness)  cv2.rectangle(img, (x_min, y_min - int(1.3 * text_height)), (x_min + text_width, y_min), BOX_COLOR, -1)  cv2.putText(  img,  title,  (x_min, y_min - int(0.3 * text_height)),  cv2.FONT_HERSHEY_SIMPLEX,  font_scale,  TEXT_COLOR,  font_thickness,  lineType=cv2.LINE_AA,  )  return img   def augment_and_show(  aug,  image,  mask=None,  bboxes=[],  categories=[],  category_id_to_name=[],  filename=None,  font_scale_orig=0.35,  font_scale_aug=0.35,  show_title=True,  **kwargs, ):  if mask is None:  augmented = aug(image=image, bboxes=bboxes, category_ids=categories)  else:  augmented = aug(image=image, mask=mask, bboxes=bboxes, category_ids=categories)   image_aug = augmented["image"]   for bbox in bboxes:  visualize_bbox(image, bbox, **kwargs)   for bbox in augmented["bboxes"]:  visualize_bbox(image_aug, bbox, **kwargs)   if show_title:  for bbox, cat_id in zip(bboxes, categories):  visualize_titles(image, bbox, category_id_to_name[cat_id], font_scale=font_scale_orig, **kwargs)  for bbox, cat_id in zip(augmented["bboxes"], augmented["category_ids"]):  visualize_titles(image_aug, bbox, category_id_to_name[cat_id], font_scale=font_scale_aug, **kwargs)   if mask is None:  f, ax = plt.subplots(1, 2, figsize=(16, 8))   ax[0].imshow(image)  ax[0].set_title("Original image")   ax[1].imshow(image_aug)  ax[1].set_title("Augmented image")  else:  f, ax = plt.subplots(2, 2, figsize=(16, 16))   if len(mask.shape) != 3:  mask = label2rgb(mask, bg_label=0)  mask_aug = label2rgb(augmented["mask"], bg_label=0)  else:  mask_aug = augmented["mask"]   ax[0, 0].imshow(image)  ax[0, 0].set_title("Original image")   ax[0, 1].imshow(image_aug)  ax[0, 1].set_title("Augmented image")   ax[1, 0].imshow(mask, interpolation="nearest")  ax[1, 0].set_title("Original mask")   ax[1, 1].imshow(mask_aug, interpolation="nearest")  ax[1, 1].set_title("Augmented mask")   f.tight_layout()   if filename is not None:  f.savefig(filename)   if mask is None:  return augmented["image"], None, augmented["bboxes"]   return augmented["image"], augmented["mask"], augmented["bboxes"]   def find_in_dir(dirname):  return [os.path.join(dirname, fname) for fname in sorted(os.listdir(dirname))] 

image = cv2.imread("images/parrot.jpg", cv2.IMREAD_COLOR_RGB) 

bbox_params = A.BboxParams(format="coco", label_fields=["category_ids"])  light = A.Compose(  [  A.RandomBrightnessContrast(p=1),  A.RandomGamma(p=1),  A.CLAHE(p=1),  ],  p=1,  bbox_params=bbox_params,  strict=True,  seed=137, )  medium = A.Compose(  [  A.CLAHE(p=1),  A.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=50, val_shift_limit=50, p=1),  ],  p=1,  bbox_params=bbox_params,  strict=True,  seed=137, )   strong = A.Compose(  [  A.RGBShift(p=1),  A.Blur(p=1),  A.GaussNoise(p=1),  A.ElasticTransform(p=1),  ],  p=1,  bbox_params=bbox_params,  strict=True,  seed=137, ) 

r = augment_and_show(light, image) 

r = augment_and_show(medium, image) 

r = augment_and_show(strong, image) 

image, mask = (  cv2.imread("images/inria/inria_tyrol_w4_image.jpg", cv2.IMREAD_COLOR_RGB),  cv2.imread("images/inria/inria_tyrol_w4_mask.tif", cv2.IMREAD_GRAYSCALE), ) image, mask = image[:1024, :1024], mask[:1024, :1024]  light = A.Compose(  [  A.RandomSizedCrop((760 - 100, 760 + 100), size=(512, 512)),  A.Affine(scale=1.1, shear=10, rotate=30),  A.RGBShift(),  A.Blur(),  A.GaussNoise(),  A.ElasticTransform(),  A.MaskDropout((10, 15), p=1),  ],  p=1,  bbox_params=A.BboxParams(format="coco", label_fields=["category_ids"]),  strict=True,  seed=137, )  r = augment_and_show(light, image, mask) 

image = cv2.imread(  "images/dsb2018/1a11552569160f0b1ea10bedbd628ce6c14f29edec5092034c2309c556df833e/images/1a11552569160f0b1ea10bedbd628ce6c14f29edec5092034c2309c556df833e.png", ) masks = [  cv2.imread(x, cv2.IMREAD_GRAYSCALE)  for x in find_in_dir("images/dsb2018/1a11552569160f0b1ea10bedbd628ce6c14f29edec5092034c2309c556df833e/masks") ] bboxes = [cv2.boundingRect(cv2.findNonZero(mask)) for mask in masks] label_image = np.zeros_like(masks[0]) for i, mask in enumerate(masks):  label_image += (mask > 0).astype(np.uint8) * i  light = A.Compose(  [  A.RGBShift(),  A.InvertImg(),  A.Blur(),  A.GaussNoise(),  A.RandomRotate90(),  A.RandomSizedCrop((512 - 100, 512 + 100), size=(512, 512)),  ],  bbox_params={"format": "coco", "min_area": 1, "min_visibility": 0.5, "label_fields": ["category_ids"]},  p=1,  strict=True,  seed=137, )  label_ids = [0] * len(bboxes) label_names = ["Nuclei"]  r = augment_and_show(light, image, label_image, bboxes, label_ids, label_names, show_title=False) 

from PIL import Image  image = cv2.imread("images/vistas/_HnWguqEbRCphUquTMrCCA.jpg", cv2.IMREAD_COLOR_RGB) labels = cv2.imread("images/vistas/_HnWguqEbRCphUquTMrCCA_labels.png", cv2.IMREAD_COLOR_RGB) instances = np.array(Image.open("images/vistas/_HnWguqEbRCphUquTMrCCA_instances.png"), dtype=np.uint16) IGNORED = 65 * 256  instances[(instances // 256 != 55) & (instances // 256 != 44) & (instances // 256 != 50)] = IGNORED  image = image[1000:2500, 1000:2500] labels = labels[1000:2500, 1000:2500] instances = instances[1000:2500, 1000:2500]  bboxes = [  cv2.boundingRect(cv2.findNonZero((instances == instance_id).astype(np.uint8)))  for instance_id in np.unique(instances)  if instance_id != IGNORED ] instance_labels = [instance_id // 256 for instance_id in np.unique(instances) if instance_id != IGNORED]  titles = [  "Bird",  "Ground Animal",  "Curb",  "Fence",  "Guard Rail",  "Barrier",  "Wall",  "Bike Lane",  "Crosswalk - Plain",  "Curb Cut",  "Parking",  "Pedestrian Area",  "Rail Track",  "Road",  "Service Lane",  "Sidewalk",  "Bridge",  "Building",  "Tunnel",  "Person",  "Bicyclist",  "Motorcyclist",  "Other Rider",  "Lane Marking - Crosswalk",  "Lane Marking - General",  "Mountain",  "Sand",  "Sky",  "Snow",  "Terrain",  "Vegetation",  "Water",  "Banner",  "Bench",  "Bike Rack",  "Billboard",  "Catch Basin",  "CCTV Camera",  "Fire Hydrant",  "Junction Box",  "Mailbox",  "Manhole",  "Phone Booth",  "Pothole",  "Street Light",  "Pole",  "Traffic Sign Frame",  "Utility Pole",  "Traffic Light",  "Traffic Sign (Back)",  "Traffic Sign (Front)",  "Trash Can",  "Bicycle",  "Boat",  "Bus",  "Car",  "Caravan",  "Motorcycle",  "On Rails",  "Other Vehicle",  "Trailer",  "Truck",  "Wheeled Slow",  "Car Mount",  "Ego Vehicle",  "Unlabeled", ] bbox_params = A.BboxParams(format="coco", min_area=1, min_visibility=0.5, label_fields=["category_ids"])  light = A.Compose(  [  A.HorizontalFlip(p=1),  A.RandomSizedCrop((800 - 100, 800 + 100), size=(600, 600)),  A.GaussNoise(p=1),  ],  bbox_params=bbox_params,  p=1,  strict=True,  seed=137, )  medium = A.Compose(  [  A.HorizontalFlip(p=1),  A.RandomSizedCrop((800 - 100, 800 + 100), size=(600, 600)),  A.MotionBlur(blur_limit=17, p=1),  ],  bbox_params=bbox_params,  p=1,  strict=True,  seed=137, )   strong = A.Compose(  [  A.HorizontalFlip(p=1),  A.RandomSizedCrop((800 - 100, 800 + 100), size=(600, 600)),  A.RGBShift(p=1),  A.Blur(blur_limit=11, p=1),  A.RandomBrightnessContrast(p=1),  A.CLAHE(p=1),  ],  bbox_params=bbox_params,  p=1,  strict=True,  seed=137, ) 

r = augment_and_show(  light,  image,  labels,  bboxes,  instance_labels,  titles,  thickness=2,  font_scale_orig=2,  font_scale_aug=1, ) 

r = augment_and_show(  medium,  image,  labels,  bboxes,  instance_labels,  titles,  thickness=2,  font_scale_orig=2,  font_scale_aug=1, ) 

r = augment_and_show(  strong,  image,  labels,  bboxes,  instance_labels,  titles,  thickness=2,  font_scale_orig=2,  font_scale_aug=1, )