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import base64
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from xml.dom import minidom
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import cv2
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import numpy as np
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from czifile import CziFile
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from tifffile import imread
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def read_svg(svg_path):
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doc = minidom.parse(str(svg_path))
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img_strings = {
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path.getAttribute("id"): path.getAttribute("href")
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for path in doc.getElementsByTagName("image")
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}
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doc.unlink()
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red = img_strings["Red"]
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green = img_strings["Green"]
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red = base64.b64decode(red.split(",")[1])
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green = base64.b64decode(green.split(",")[1])
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red = cv2.imdecode(np.frombuffer(red, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
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green = cv2.imdecode(np.frombuffer(green, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
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red = cv2.cvtColor(red, cv2.COLOR_BGRA2GRAY)
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green = cv2.cvtColor(green, cv2.COLOR_BGRA2GRAY)
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mask = np.zeros_like(red)
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mask[red > 0] = 1
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mask[green > 0] = 2
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return mask
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def extract_bboxes(mask):
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mask = np.array(mask)
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mask = mask.astype(np.uint8)
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(
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mask, connectivity=8
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)
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bboxes = []
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for i in range(1, num_labels):
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x, y, w, h, area = stats[i]
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bboxes.append([x, y, x + w, y + h])
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return bboxes
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def preprocess(raw_data, reverse_channels=False):
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MAX_VALUE = 2**16 - 1
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if raw_data.ndim == 2:
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raw_data = raw_data[np.newaxis, :, :]
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h, w = raw_data.shape[1:3]
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orders = np.arange(raw_data.shape[0])[::-1]
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result = np.zeros((h, w, 3), dtype=np.uint8)
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for i, chan in enumerate(raw_data):
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hist, bins = np.histogram(chan.ravel(), MAX_VALUE + 1, (0, MAX_VALUE + 1))
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cdf = hist.cumsum()
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cdf_normalized = cdf / cdf[-1]
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bmax = np.searchsorted(cdf_normalized, 0.99, side="left")
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clip = np.clip(chan, 0, bmax).astype(np.float32)
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clip = (clip - clip.min()) / (bmax - clip.min()) * 255
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result[:, :, orders[i]] = clip
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if reverse_channels:
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result = result[:, :, [1, 0, 2]]
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return result
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def read_czi(filepath):
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data = CziFile(filepath)
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return data.asarray().squeeze()
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def read_tiff(filepath):
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data = imread(filepath).squeeze()
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return data |
