125 lines
3.3 KiB
Python
125 lines
3.3 KiB
Python
import os
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from sklearn.cluster import KMeans
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from wand.color import Color
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from wand.image import Image
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import numpy as np
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def chroma_key(img, num_object_colors=1, key_box=((0,0),(100,100)), min_object_pixels=500):
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hsv = _hsv_points(img)
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hsv_cart = _hsv_to_cartesian(hsv)
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labels = _cluster(hsv_cart, num_object_colors+1, img)
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key_label = _choose_key(labels, key_box)
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mask = _create_mask(labels, key_label)
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while True:
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ids = _islands(mask)
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if not _filter_islands(mask, ids, min_object_pixels):
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break
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_apply_mask(img, mask)
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img.trim()
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def _hsv_points(img):
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hsv_img = img.clone()
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hsv_img.transform_colorspace('hsv')
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pixels = list(hsv_img.export_pixels(channel_map='RGB')) # this actually means HSV
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return np.array(pixels).reshape(-1, 3)
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def _hsv_to_cartesian(hsv):
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h = hsv[:, 0]
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s = hsv[:, 1]
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v = hsv[:, 2]
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x = s * np.cos(h * 2 * np.pi / 255)
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y = s * np.sin(h * 2 * np.pi / 255)
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return np.column_stack((x, y, v))
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def _cluster(points, num_clusters, img):
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kmeans = KMeans(n_clusters=num_clusters)
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kmeans.fit(points)
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return kmeans.labels_.reshape(img.height, img.width)
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def _choose_key(labels, key_box):
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box_labels = labels[key_box[0][0]:key_box[1][0], key_box[0][1]:key_box[1][1]]
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values, counts = np.unique(box_labels.flatten(), return_counts=True)
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i = np.argmax(counts)
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return values[i]
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def _islands(mask):
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ids = np.zeros_like(mask).astype(np.uint64)
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next_id = 1
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for (i, j), _ in np.ndenumerate(mask):
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_flood_fill(ids, mask, i, j, next_id)
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next_id += 1
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return ids
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def _filter_islands(mask, ids, min_object_pixels):
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values, counts = np.unique(ids.flatten(), return_counts=True)
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values_rm = values[np.where(counts < min_object_pixels)]
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mask_ix = np.isin(ids, values_rm)
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mask[mask_ix] = np.where(mask[mask_ix] == 255, 0, 255)
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return len(values_rm) > 0
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def _flood_fill(ids, mask, i, j, val):
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base_val = mask[i, j]
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queue = [(i, j)]
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while queue:
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(i, j) = queue.pop()
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if i < 0 or i >= mask.shape[0] or j < 0 or j >= mask.shape[1]:
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continue
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if mask[i, j] != base_val or ids[i, j] != 0:
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continue
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ids[i, j] = val
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queue.append((i - 1, j))
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queue.append((i + 1, j))
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queue.append((i - 1, j - 1))
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queue.append((i + 1, j - 1))
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queue.append((i - 1, j + 1))
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queue.append((i + 1, j + 1))
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queue.append((i, j - 1))
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queue.append((i, j + 1))
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def _create_mask(labels, key_label):
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return (labels != key_label).astype(np.uint8) * 255
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def _apply_mask(img, mask):
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with Image.from_array(mask) as mask_img:
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img.alpha_channel = 'activate'
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img.composite_channel('alpha', mask_img, 'copy_alpha', 0, 0)
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img.background_color = Color('transparent')
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path = 'data/RAW/SC1/BR1'
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for dirpath, dirnames, filenames in os.walk(path):
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for filename in filenames:
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if not filename.endswith('.orf'):
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continue
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full_path = os.path.join(dirpath, filename)
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print(full_path)
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with Image(filename=full_path) as img:
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img.crop(left=0, top=0, width=4000, height=3000)
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chroma_key(img)
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new_path = os.path.join(dirpath, filename.removesuffix('.orf') + '.png')
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img.save(filename=new_path) |