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@@ -0,0 +1,147 @@
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+import cv2
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+import uuid
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+import numpy as np
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+import typing as T
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+
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+from flask import abort
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+from flask import make_response
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+from flask import request
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+from flask.views import View
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+
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+from pycs import db
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+from pycs.database.File import File
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+from pycs.database.Result import Result
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+from pycs.frontend.notifications.NotificationManager import NotificationManager
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+from pycs.jobs.JobGroupBusyException import JobGroupBusyException
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+from pycs.jobs.JobRunner import JobRunner
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+
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+class EstimateBoundingBox(View):
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+ """
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+ create a result for a file
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+ """
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+ # pylint: disable=arguments-differ
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+ methods = ['POST']
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+
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+ def __init__(self, nm: NotificationManager, jobs: JobRunner,):
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+ # pylint: disable=invalid-name
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+ self.nm = nm
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+ self.jobs = jobs
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+
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+ def dispatch_request(self, file_id: int):
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+
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+ file = File.get_or_404(file_id)
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+ request_data = request.get_json(force=True)
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+ if 'x' not in request_data or 'y' not in request_data:
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+ abort(400, "coordinates for the estimation are missing")
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+
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+ x,y = map(request_data.get, "xy")
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+
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+ # get project
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+ project = file.project
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+ try:
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+ rnd = str(uuid.uuid4())[:10]
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+ self.jobs.run(project,
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+ "Estimation",
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+ f'{project.name} (create predictions)',
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+ f"{project.id}/estimation/{rnd}",
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+ estimate,
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+ file.id, x, y,
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+ result=self.nm.create_result
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+ )
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+
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+ except JobGroupBusyException:
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+ abort(400, "Job is already running!")
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+
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+ return make_response()
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+
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+
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+def estimate(file_id: int, x: float, y: float) -> Result:
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+ file = File.query.get(file_id)
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+
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+ im = cv2.imread(file.absolute_path, cv2.IMREAD_GRAYSCALE)
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+
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+ h, w = im.shape
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+ pos = int(x * w), int(y * h)
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+ x0, y0, x1, y1 = detect(im, pos,
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+ window_size=1000,
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+ pixel_delta=50,
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+ enlarge=1e-2,
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+ )
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+
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+ data = dict(
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+ x=x0 / w,
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+ y=y0 / h,
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+ w=(x1-x0) / w,
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+ h=(y1-y0) / h
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+ )
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+
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+ return file.create_result('pipeline', 'bounding-box', label=None, data=data)
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+
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+def detect(im: np.ndarray,
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+ pos: T.Tuple[int, int],
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+ window_size: int = 1000,
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+ pixel_delta: int = 0,
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+ enlarge: float = -1) -> T.Tuple[int, int, int, int]:
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+ # im = blur(im, 3)
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+ x, y = pos
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+ pixel = im[y, x]
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+
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+ min_pix, max_pix = pixel - pixel_delta, pixel + pixel_delta
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+
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+ mask = np.logical_and(min_pix < im, im < max_pix).astype(np.float32)
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+ # mask = open_close(mask)
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+ # mask = blur(mask)
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+
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+ pad = window_size // 2
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+ mask = np.pad(mask, pad, mode="constant")
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+ window = mask[y: y + window_size, x: x + window_size]
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+
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+ sum_x, sum_y = window.sum(axis=0), window.sum(axis=1)
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+
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+ enlarge = int(enlarge * max(im.shape))
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+ (x0, x1), (y0, y1) = get_borders(sum_x, enlarge), get_borders(sum_y, enlarge)
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+
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+ x0 = max(x + x0 - pad, 0)
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+ y0 = max(y + y0 - pad, 0)
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+
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+ x1 = min(x + x1 - pad, im.shape[1])
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+ y1 = min(y + y1 - pad, im.shape[0])
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+
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+ return x0, y0, x1, y1
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+
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+def get_borders(arr, enlarge: int, eps=5e-1):
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+ mid = len(arr) // 2
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+
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+ arr0, arr1 = arr[:mid], arr[mid:]
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+
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+ thresh = arr[mid] * eps
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+
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+ lowers = np.where(arr0 < thresh)[0]
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+ lower = 0 if len(lowers) == 0 else lowers[-1]
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+
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+ uppers = np.where(arr1 < thresh)[0]
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+ upper = arr1.argmin() if len(uppers) == 0 else uppers[0]
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+
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+ # since the second half starts after the first
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+ upper = len(arr0) + upper
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+
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+ if enlarge > 0:
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+ lower = max(lower - enlarge, 0)
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+ upper = min(upper + enlarge, len(arr)-1)
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+
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+ return int(lower), int(upper)
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+
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+
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+"""
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+def blur(im, sigma=5):
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+ from skimage import filters
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+ return filters.gaussian(im, sigma=sigma, preserve_range=True)
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+
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+def open_close(im, kernel_size=3):
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+
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+ kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)
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+
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+ im = cv2.morphologyEx(im, cv2.MORPH_OPEN, kernel)
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+ im = cv2.morphologyEx(im, cv2.MORPH_CLOSE, kernel)
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+ return im
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+"""
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