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How to use offset_left method in hypothesis
Best Python code snippet using hypothesis
cv2_aug_transforms.py
Source:cv2_aug_transforms.py 
1#!/usr/bin/env python2# -*- coding:utf-8 -*-3# Author: Donny You (youansheng@gmail.com)4import collections5import random6import math7import cv28import numpy as np9from utils.tools.logger import Logger as Log10class RandomPad(object):11 """ Padding the Image to proper size.12 Args:13 stride: the stride of the network.14 pad_value: the value that pad to the image border.15 img: Image object as input.16 Returns::17 img: Image object.18 """19 def __init__(self, up_scale_range=None, ratio=0.5, mean=(104, 117, 123)):20 # do something21 assert isinstance(up_scale_range, (list, tuple))22 self.up_scale_range = up_scale_range23 self.ratio = ratio24 self.mean = mean25 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):26 assert isinstance(img, (np.ndarray, list))27 assert labelmap is None or isinstance(labelmap, np.ndarray)28 assert maskmap is None or isinstance(maskmap, np.ndarray)29 if random.random() > self.ratio:30 return img, labelmap, maskmap, kpts, bboxes, labels, polygons31 height, width, channels = img.shape32 ws = random.uniform(self.up_scale_range[0], self.up_scale_range[1])33 hs = ws34 for _ in range(50):35 scale = random.uniform(self.up_scale_range[0], self.up_scale_range[1])36 min_ratio = max(0.5, 1. / scale / scale)37 max_ratio = min(2, scale * scale)38 ratio = math.sqrt(random.uniform(min_ratio, max_ratio))39 ws = scale * ratio40 hs = scale / ratio41 if ws >= 1 and hs >= 1:42 break43 w = int(ws * width)44 h = int(hs * height)45 pad_width = random.randint(0, w - width)46 pad_height = random.randint(0, h - height)47 left_pad = random.randint(0, pad_width) # pad_left48 up_pad = random.randint(0, pad_height) # pad_up49 if not isinstance(img, list):50 img = cv2.copyMakeBorder(img, up_pad, pad_height-up_pad, left_pad, pad_width-left_pad,51 cv2.BORDER_CONSTANT, value=self.mean)52 else:53 img = [cv2.copyMakeBorder(item, up_pad, pad_height-up_pad, left_pad, pad_width-left_pad,54 cv2.BORDER_CONSTANT, value=self.mean) for item in img]55 if labelmap is not None:56 labelmap = cv2.copyMakeBorder(labelmap, up_pad, pad_height - up_pad, left_pad, pad_width - left_pad,57 cv2.BORDER_CONSTANT, value=255)58 if maskmap is not None:59 maskmap = cv2.copyMakeBorder(maskmap, up_pad, pad_height - up_pad, left_pad, pad_width - left_pad,60 cv2.BORDER_CONSTANT, value=1)61 if polygons is not None:62 for object_id in range(len(polygons)):63 for polygon_id in range(len(polygons[object_id])):64 polygons[object_id][polygon_id][0::2] += left_pad65 polygons[object_id][polygon_id][1::2] += up_pad66 if kpts is not None and kpts.size > 0:67 kpts[:, :, 0] += left_pad68 kpts[:, :, 1] += up_pad69 if bboxes is not None and bboxes.size > 0:70 bboxes[:, 0::2] += left_pad71 bboxes[:, 1::2] += up_pad72 return img, labelmap, maskmap, kpts, bboxes, labels, polygons73class Padding(object):74 """ Padding the Image to proper size.75 Args:76 stride: the stride of the network.77 pad_value: the value that pad to the image border.78 img: Image object as input.79 Returns::80 img: Image object.81 """82 def __init__(self, pad=None, ratio=0.5, mean=(104, 117, 123), allow_outside_center=True):83 self.pad = pad84 self.ratio = ratio85 self.mean = mean86 self.allow_outside_center = allow_outside_center87 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):88 assert isinstance(img, (np.ndarray, list))89 assert labelmap is None or isinstance(labelmap, np.ndarray)90 assert maskmap is None or isinstance(maskmap, np.ndarray)91 if random.random() > self.ratio:92 return img, labelmap, maskmap, kpts, bboxes, labels, polygons93 height, width, channels = img.shape94 left_pad, up_pad, right_pad, down_pad = self.pad95 target_size = [width + left_pad + right_pad, height + up_pad + down_pad]96 offset_left = -left_pad97 offset_up = -up_pad98 if kpts is not None and kpts.size > 0:99 kpts[:, :, 0] -= offset_left100 kpts[:, :, 1] -= offset_up101 mask = np.logical_or.reduce((kpts[:, :, 0] >= target_size[0], kpts[:, :, 0] < 0,102 kpts[:, :, 1] >= target_size[1], kpts[:, :, 1] < 0))103 kpts[mask == 1, 2] = -1104 if bboxes is not None and bboxes.size > 0:105 if self.allow_outside_center:106 mask = np.ones(bboxes.shape[0], dtype=bool)107 else:108 crop_bb = np.array([offset_left, offset_up, offset_left + target_size[0], offset_up + target_size[1]])109 center = (bboxes[:, :2] + bboxes[:, 2:]) / 2110 mask = np.logical_and(crop_bb[:2] <= center, center < crop_bb[2:]).all(axis=1)111 bboxes[:, 0::2] -= offset_left112 bboxes[:, 1::2] -= offset_up113 bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, target_size[0] - 1)114 bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, target_size[1] - 1)115 mask = np.logical_and(mask, (bboxes[:, :2] < bboxes[:, 2:]).all(axis=1))116 bboxes = bboxes[mask]117 if labels is not None:118 labels = labels[mask]119 if polygons is not None:120 new_polygons = list()121 for object_id in range(len(polygons)):122 if mask[object_id] == 1:123 for polygon_id in range(len(polygons[object_id])):124 polygons[object_id][polygon_id][0::2] -= offset_left125 polygons[object_id][polygon_id][1::2] -= offset_up126 polygons[object_id][polygon_id][0::2] = np.clip(polygons[object_id][polygon_id][0::2],127 0, target_size[0] - 1)128 polygons[object_id][polygon_id][1::2] = np.clip(polygons[object_id][polygon_id][1::2],129 0, target_size[1] - 1)130 new_polygons.append(polygons[object_id])131 polygons = new_polygons132 if not isinstance(img, list):133 expand_image = np.zeros((max(height, target_size[1]) + abs(offset_up),134 max(width, target_size[0]) + abs(offset_left), channels), dtype=img.dtype)135 expand_image[:, :, :] = self.mean136 expand_image[abs(min(offset_up, 0)):abs(min(offset_up, 0)) + height,137 abs(min(offset_left, 0)):abs(min(offset_left, 0)) + width] = img138 img = expand_image[max(offset_up, 0):max(offset_up, 0) + target_size[1],139 max(offset_left, 0):max(offset_left, 0) + target_size[0]]140 else:141 for i in range(len(img)):142 expand_image = np.zeros((max(height, target_size[1]) + abs(offset_up),143 max(width, target_size[0]) + abs(offset_left), channels), dtype=img[i].dtype)144 expand_image[:, :, :] = self.mean145 expand_image[abs(min(offset_up, 0)):abs(min(offset_up, 0)) + height,146 abs(min(offset_left, 0)):abs(min(offset_left, 0)) + width] = img[i]147 img[i] = expand_image[max(offset_up, 0):max(offset_up, 0) + target_size[1],148 max(offset_left, 0):max(offset_left, 0) + target_size[0]]149 if maskmap is not None:150 expand_maskmap = np.zeros((max(height, target_size[1]) + abs(offset_up),151 max(width, target_size[0]) + abs(offset_left)), dtype=maskmap.dtype)152 expand_maskmap[:, :] = 1153 expand_maskmap[abs(min(offset_up, 0)):abs(min(offset_up, 0)) + height,154 abs(min(offset_left, 0)):abs(min(offset_left, 0)) + width] = maskmap155 maskmap = expand_maskmap[max(offset_up, 0):max(offset_up, 0) + target_size[1],156 max(offset_left, 0):max(offset_left, 0) + target_size[0]]157 if labelmap is not None:158 expand_labelmap = np.zeros((max(height, target_size[1]) + abs(offset_up),159 max(width, target_size[0]) + abs(offset_left)), dtype=labelmap.dtype)160 expand_labelmap[:, :] = 255161 expand_labelmap[abs(min(offset_up, 0)):abs(min(offset_up, 0)) + height,162 abs(min(offset_left, 0)):abs(min(offset_left, 0)) + width] = labelmap163 labelmap = expand_labelmap[max(offset_up, 0):max(offset_up, 0) + target_size[1],164 max(offset_left, 0):max(offset_left, 0) + target_size[0]]165 return img, labelmap, maskmap, kpts, bboxes, labels, polygons166class RandomHFlip(object):167 def __init__(self, swap_pair=None, ratio=0.5):168 self.swap_pair = swap_pair169 self.ratio = ratio170 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):171 assert isinstance(img, (np.ndarray, list))172 assert labelmap is None or isinstance(labelmap, np.ndarray)173 assert maskmap is None or isinstance(maskmap, np.ndarray)174 if random.random() > self.ratio:175 return img, labelmap, maskmap, kpts, bboxes, labels, polygons176 height, width, _ = img.shape177 if not isinstance(img, list):178 img = cv2.flip(img, 1)179 else:180 img = [cv2.flip(item, 1) for item in img]181 if labelmap is not None:182 labelmap = cv2.flip(labelmap, 1)183 if maskmap is not None:184 maskmap = cv2.flip(maskmap, 1)185 if polygons is not None:186 for object_id in range(len(polygons)):187 for polygon_id in range(len(polygons[object_id])):188 polygons[object_id][polygon_id][0::2] = width - 1 - polygons[object_id][polygon_id][0::2]189 if bboxes is not None and bboxes.size > 0:190 xmin = width - 1 - bboxes[:, 2]191 xmax = width - 1 - bboxes[:, 0]192 bboxes[:, 0] = xmin193 bboxes[:, 2] = xmax194 if kpts is not None and kpts.size > 0:195 kpts[:, :, 0] = width - 1 - kpts[:, :, 0]196 for pair in self.swap_pair:197 temp_point = np.copy(kpts[:, pair[0] - 1])198 kpts[:, pair[0] - 1] = kpts[:, pair[1] - 1]199 kpts[:, pair[1] - 1] = temp_point200 return img, labelmap, maskmap, kpts, bboxes, labels, polygons201class RandomSaturation(object):202 def __init__(self, lower=0.5, upper=1.5, ratio=0.5):203 self.lower = lower204 self.upper = upper205 self.ratio = ratio206 assert self.upper >= self.lower, "saturation upper must be >= lower."207 assert self.lower >= 0, "saturation lower must be non-negative."208 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):209 assert isinstance(img, np.ndarray)210 assert labelmap is None or isinstance(labelmap, np.ndarray)211 assert maskmap is None or isinstance(maskmap, np.ndarray)212 if random.random() > self.ratio:213 return img, labelmap, maskmap, kpts, bboxes, labels, polygons214 img = img.astype(np.float32)215 img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)216 img[:, :, 1] *= random.uniform(self.lower, self.upper)217 img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)218 img = np.clip(img, 0, 255).astype(np.uint8)219 return img, labelmap, maskmap, kpts, bboxes, labels, polygons220class RandomHue(object):221 def __init__(self, delta=18, ratio=0.5):222 assert 0 <= delta <= 360223 self.delta = delta224 self.ratio = ratio225 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):226 assert isinstance(img, np.ndarray)227 assert labelmap is None or isinstance(labelmap, np.ndarray)228 assert maskmap is None or isinstance(maskmap, np.ndarray)229 if random.random() > self.ratio:230 return img, labelmap, maskmap, kpts, bboxes, labels, polygons231 img = img.astype(np.float32)232 img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)233 img[:, :, 0] += random.uniform(-self.delta, self.delta)234 img[:, :, 0][img[:, :, 0] > 360] -= 360235 img[:, :, 0][img[:, :, 0] < 0] += 360236 img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)237 img = np.clip(img, 0, 255).astype(np.uint8)238 return img, labelmap, maskmap, kpts, bboxes, labels, polygons239class RandomPerm(object):240 def __init__(self, ratio=0.5):241 self.ratio = ratio242 self.perms = ((0, 1, 2), (0, 2, 1),243 (1, 0, 2), (1, 2, 0),244 (2, 0, 1), (2, 1, 0))245 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):246 assert isinstance(img, np.ndarray)247 assert labelmap is None or isinstance(labelmap, np.ndarray)248 assert maskmap is None or isinstance(maskmap, np.ndarray)249 if random.random() > self.ratio:250 return img, labelmap, maskmap, kpts, bboxes, labels, polygons251 swap = self.perms[random.randint(0, len(self.perms) - 1)]252 img = img[:, :, swap].astype(np.uint8)253 return img, labelmap, maskmap, kpts, bboxes, labels, polygons254class RandomContrast(object):255 def __init__(self, lower=0.5, upper=1.5, ratio=0.5):256 self.lower = lower257 self.upper = upper258 self.ratio = ratio259 assert self.upper >= self.lower, "contrast upper must be >= lower."260 assert self.lower >= 0, "contrast lower must be non-negative."261 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):262 assert isinstance(img, np.ndarray)263 assert labelmap is None or isinstance(labelmap, np.ndarray)264 assert maskmap is None or isinstance(maskmap, np.ndarray)265 if random.random() > self.ratio:266 return img, labelmap, maskmap, kpts, bboxes, labels, polygons267 img = img.astype(np.float32)268 img *= random.uniform(self.lower, self.upper)269 img = np.clip(img, 0, 255).astype(np.uint8)270 return img, labelmap, maskmap, kpts, bboxes, labels, polygons271class RandomBrightness(object):272 def __init__(self, shift_value=30, ratio=0.5):273 self.shift_value = shift_value274 self.ratio = ratio275 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):276 assert isinstance(img, np.ndarray)277 assert labelmap is None or isinstance(labelmap, np.ndarray)278 assert maskmap is None or isinstance(maskmap, np.ndarray)279 if random.random() > self.ratio:280 return img, labelmap, maskmap, kpts, bboxes, labels, polygons281 img = img.astype(np.float32)282 shift = random.randint(-self.shift_value, self.shift_value)283 img[:, :, :] += shift284 img = np.around(img)285 img = np.clip(img, 0, 255).astype(np.uint8)286 return img, labelmap, maskmap, kpts, bboxes, labels, polygons287class RandomResizedCrop(object):288 """Crop the given PIL Image to random size and aspect ratio.289 A crop of random size (default: of 0.08 to 1.0) of the original size and a random290 aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop291 is finally resized to given size.292 This is popularly used to train the Inception networks.293 Args:294 size: expected output size of each edge295 scale: range of size of the origin size cropped296 ratio: range of aspect ratio of the origin aspect ratio cropped297 interpolation: Default: PIL.Image.BILINEAR298 """299 def __init__(self, size, scale_range=(0.08, 1.0), aspect_range=(3. / 4., 4. / 3.)):300 self.size = tuple(size)301 self.scale = scale_range302 self.ratio = aspect_range303 @staticmethod304 def get_params(img, scale, ratio):305 """Get parameters for ``crop`` for a random sized crop.306 Args:307 img (PIL Image): Image to be cropped.308 scale (tuple): range of size of the origin size cropped309 ratio (tuple): range of aspect ratio of the origin aspect ratio cropped310 Returns:311 tuple: params (i, j, h, w) to be passed to ``crop`` for a random312 sized crop.313 """314 height, width, _ = img.shape315 for attempt in range(10):316 area = width * height317 target_area = random.uniform(*scale) * area318 aspect_ratio = random.uniform(*ratio)319 w = int(round(math.sqrt(target_area * aspect_ratio)))320 h = int(round(math.sqrt(target_area / aspect_ratio)))321 if random.random() < 0.5:322 w, h = h, w323 if w <= width and h <= height:324 i = random.randint(0, height - h)325 j = random.randint(0, width - w)326 return i, j, h, w327 # Fallback328 w = min(height, width)329 i = (height - w) // 2330 j = (width - w) // 2331 return i, j, w, w332 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):333 """334 Args:335 img (Numpy Image): Image to be cropped and resized.336 Returns:337 Numpy Image: Randomly cropped and resized image.338 """339 assert labelmap is None and maskmap is None and kpts is None and bboxes is None and labels is None340 i, j, h, w = self.get_params(img, self.scale, self.ratio)341 img = img[i:i+h, j:j+w]342 img = cv2.resize(img, self.size, interpolation=cv2.INTER_LINEAR)343 return img, labelmap, maskmap, kpts, bboxes, labels, polygons344class RandomResize(object):345 """Resize the given numpy.ndarray to random size and aspect ratio.346 Args:347 scale_min: the min scale to resize.348 scale_max: the max scale to resize.349 """350 def __init__(self, scale_range=(0.75, 1.25), aspect_range=(0.9, 1.1), target_size=None,351 resize_bound=None, method='random', ratio=0.5):352 self.scale_range = scale_range353 self.aspect_range = aspect_range354 self.resize_bound = resize_bound355 self.method = method356 self.ratio = ratio357 if target_size is not None:358 if isinstance(target_size, int):359 self.input_size = (target_size, target_size)360 elif isinstance(target_size, (list, tuple)) and len(target_size) == 2:361 self.input_size = target_size362 else:363 raise TypeError('Got inappropriate size arg: {}'.format(target_size))364 else:365 self.input_size = None366 def get_scale(self, img_size, bboxes):367 if self.method == 'random':368 scale_ratio = random.uniform(self.scale_range[0], self.scale_range[1])369 return scale_ratio370 elif self.method == 'focus':371 if self.input_size is not None and bboxes is not None and len(bboxes) > 0:372 bboxes = np.array(bboxes)373 border = bboxes[:, 2:] - bboxes[:, 0:2]374 scale = 0.6 / max(max(border[:, 0]) / self.input_size[0], max(border[:, 1]) / self.input_size[1])375 scale_ratio = random.uniform(self.scale_range[0], self.scale_range[1]) * scale376 return scale_ratio377 else:378 scale_ratio = random.uniform(self.scale_range[0], self.scale_range[1])379 return scale_ratio380 elif self.method == 'bound':381 scale1 = self.resize_bound[0] / min(img_size)382 scale2 = self.resize_bound[1] / max(img_size)383 scale = min(scale1, scale2)384 return scale385 else:386 Log.error('Resize method {} is invalid.'.format(self.method))387 exit(1)388 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):389 """390 Args:391 img (Image): Image to be resized.392 maskmap (Image): Mask to be resized.393 kpt (list): keypoints to be resized.394 center: (list): center points to be resized.395 Returns:396 Image: Randomly resize image.397 Image: Randomly resize maskmap.398 list: Randomly resize keypoints.399 list: Randomly resize center points.400 """401 assert isinstance(img, (np.ndarray, list))402 assert labelmap is None or isinstance(labelmap, np.ndarray)403 assert maskmap is None or isinstance(maskmap, np.ndarray)404 height, width, _ = img.shape405 if random.random() < self.ratio:406 scale_ratio = self.get_scale([width, height], bboxes)407 aspect_ratio = random.uniform(*self.aspect_range)408 w_scale_ratio = math.sqrt(aspect_ratio) * scale_ratio409 h_scale_ratio = math.sqrt(1.0 / aspect_ratio) * scale_ratio410 else:411 w_scale_ratio, h_scale_ratio = 1.0, 1.0412 if kpts is not None and kpts.size > 0:413 kpts[:, :, 0] *= w_scale_ratio414 kpts[:, :, 1] *= h_scale_ratio415 if bboxes is not None and bboxes.size > 0:416 bboxes[:, 0::2] *= w_scale_ratio417 bboxes[:, 1::2] *= h_scale_ratio418 if polygons is not None:419 for object_id in range(len(polygons)):420 for polygon_id in range(len(polygons[object_id])):421 polygons[object_id][polygon_id][0::2] *= w_scale_ratio422 polygons[object_id][polygon_id][1::2] *= h_scale_ratio423 converted_size = (int(width * w_scale_ratio), int(height * h_scale_ratio))424 if not isinstance(img, list):425 img = cv2.resize(img, converted_size, interpolation=cv2.INTER_LINEAR).astype(np.uint8)426 else:427 img = [cv2.resize(item, converted_size, interpolation=cv2.INTER_LINEAR).astype(np.uint8) for item in img]428 if labelmap is not None:429 labelmap = cv2.resize(labelmap, converted_size, interpolation=cv2.INTER_NEAREST)430 if maskmap is not None:431 maskmap = cv2.resize(maskmap, converted_size, interpolation=cv2.INTER_NEAREST)432 return img, labelmap, maskmap, kpts, bboxes, labels, polygons433class RandomRotate(object):434 """Rotate the input numpy.ndarray and points to the given degree.435 Args:436 degree (number): Desired rotate degree.437 """438 def __init__(self, max_degree, ratio=0.5, mean=(104, 117, 123)):439 assert isinstance(max_degree, int)440 self.max_degree = max_degree441 self.ratio = ratio442 self.mean = mean443 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):444 """445 Args:446 img (Image): Image to be rotated.447 maskmap (Image): Mask to be rotated.448 kpt (list): Keypoints to be rotated.449 center (list): Center points to be rotated.450 Returns:451 Image: Rotated image.452 list: Rotated key points.453 """454 assert isinstance(img, (np.ndarray, list))455 assert labelmap is None or isinstance(labelmap, np.ndarray)456 assert maskmap is None or isinstance(maskmap, np.ndarray)457 if random.random() < self.ratio:458 rotate_degree = random.uniform(-self.max_degree, self.max_degree)459 else:460 return img, labelmap, maskmap, kpts, bboxes, labels, polygons461 height, width, _ = img.shape462 img_center = (width / 2.0, height / 2.0)463 rotate_mat = cv2.getRotationMatrix2D(img_center, rotate_degree, 1.0)464 cos_val = np.abs(rotate_mat[0, 0])465 sin_val = np.abs(rotate_mat[0, 1])466 new_width = int(height * sin_val + width * cos_val)467 new_height = int(height * cos_val + width * sin_val)468 rotate_mat[0, 2] += (new_width / 2.) - img_center[0]469 rotate_mat[1, 2] += (new_height / 2.) - img_center[1]470 if not isinstance(img, list):471 img = cv2.warpAffine(img, rotate_mat, (new_width, new_height), borderValue=self.mean).astype(np.uint8)472 else:473 img = [cv2.warpAffine(item, rotate_mat, (new_width, new_height),474 borderValue=self.mean).astype(np.uint8) for item in img]475 if labelmap is not None:476 labelmap = cv2.warpAffine(labelmap, rotate_mat, (new_width, new_height),477 borderValue=(255, 255, 255), flags=cv2.INTER_NEAREST)478 labelmap = labelmap.astype(np.uint8)479 if maskmap is not None:480 maskmap = cv2.warpAffine(maskmap, rotate_mat, (new_width, new_height),481 borderValue=(1, 1, 1), flags=cv2.INTER_NEAREST)482 maskmap = maskmap.astype(np.uint8)483 if polygons is not None:484 for object_id in range(len(polygons)):485 for polygon_id in range(len(polygons[object_id])):486 for i in range(len(polygons[object_id][polygon_id]) // 2):487 x = polygons[object_id][polygon_id][i * 2]488 y = polygons[object_id][polygon_id][i * 2 + 1]489 p = np.array([x, y, 1])490 p = rotate_mat.dot(p)491 polygons[object_id][polygon_id][i * 2] = p[0]492 polygons[object_id][polygon_id][i * 2 + 1] = p[1]493 if kpts is not None and kpts.size > 0:494 num_objects = len(kpts)495 num_keypoints = len(kpts[0])496 for i in range(num_objects):497 for j in range(num_keypoints):498 x = kpts[i][j][0]499 y = kpts[i][j][1]500 p = np.array([x, y, 1])501 p = rotate_mat.dot(p)502 kpts[i][j][0] = p[0]503 kpts[i][j][1] = p[1]504 # It is not right for object detection tasks.505 if bboxes is not None and bboxes.size > 0:506 for i in range(len(bboxes)):507 bbox_temp = [bboxes[i][0], bboxes[i][1], bboxes[i][2], bboxes[i][1],508 bboxes[i][0], bboxes[i][3], bboxes[i][2], bboxes[i][3]]509 for node in range(4):510 x = bbox_temp[node * 2]511 y = bbox_temp[node * 2 + 1]512 p = np.array([x, y, 1])513 p = rotate_mat.dot(p)514 bbox_temp[node * 2] = p[0]515 bbox_temp[node * 2 + 1] = p[1]516 bboxes[i] = [min(bbox_temp[0], bbox_temp[2], bbox_temp[4], bbox_temp[6]),517 min(bbox_temp[1], bbox_temp[3], bbox_temp[5], bbox_temp[7]),518 max(bbox_temp[0], bbox_temp[2], bbox_temp[4], bbox_temp[6]),519 max(bbox_temp[1], bbox_temp[3], bbox_temp[5], bbox_temp[7])]520 return img, labelmap, maskmap, kpts, bboxes, labels, polygons521class RandomCrop(object):522 """Crop the given numpy.ndarray and at a random location.523 Args:524 size (int or tuple): Desired output size of the crop.(w, h)525 """526 def __init__(self, crop_size, ratio=0.5, method='random', grid=None, allow_outside_center=True):527 self.ratio = ratio528 self.method = method529 self.grid = grid530 self.allow_outside_center = allow_outside_center531 if isinstance(crop_size, float):532 self.size = (crop_size, crop_size)533 elif isinstance(crop_size, collections.Iterable) and len(crop_size) == 2:534 self.size = crop_size535 else:536 raise TypeError('Got inappropriate size arg: {}'.format(crop_size))537 def get_lefttop(self, crop_size, img_size):538 if self.method == 'center':539 return [(img_size[0] - crop_size[0]) // 2, (img_size[1] - crop_size[1]) // 2]540 elif self.method == 'random':541 x = random.randint(0, img_size[0] - crop_size[0])542 y = random.randint(0, img_size[1] - crop_size[1])543 return [x, y]544 elif self.method == 'grid':545 grid_x = random.randint(0, self.grid[0] - 1)546 grid_y = random.randint(0, self.grid[1] - 1)547 x = grid_x * ((img_size[0] - crop_size[0]) // (self.grid[0] - 1))548 y = grid_y * ((img_size[1] - crop_size[1]) // (self.grid[1] - 1))549 return [x, y]550 else:551 Log.error('Crop method {} is invalid.'.format(self.method))552 exit(1)553 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):554 """555 Args:556 img (Image): Image to be cropped.557 maskmap (Image): Mask to be cropped.558 Returns:559 Image: Cropped image.560 Image: Cropped maskmap.561 list: Cropped keypoints.562 list: Cropped center points.563 """564 assert isinstance(img, (np.ndarray, list))565 assert labelmap is None or isinstance(labelmap, np.ndarray)566 assert maskmap is None or isinstance(maskmap, np.ndarray)567 if random.random() > self.ratio:568 return img, labelmap, maskmap, kpts, bboxes, labels, polygons569 height, width, _ = img.shape570 target_size = [min(self.size[0], width), min(self.size[1], height)]571 offset_left, offset_up = self.get_lefttop(target_size, [width, height])572 # img = ImageHelper.draw_box(img, bboxes[index])573 if kpts is not None and kpts.size > 0:574 kpts[:, :, 0] -= offset_left575 kpts[:, :, 1] -= offset_up576 if bboxes is not None and bboxes.size > 0:577 if self.allow_outside_center:578 mask = np.ones(bboxes.shape[0], dtype=bool)579 else:580 crop_bb = np.array([offset_left, offset_up, offset_left + target_size[0], offset_up + target_size[1]])581 center = (bboxes[:, :2] + bboxes[:, 2:]) / 2582 mask = np.logical_and(crop_bb[:2] <= center, center < crop_bb[2:]).all(axis=1)583 bboxes[:, 0::2] -= offset_left584 bboxes[:, 1::2] -= offset_up585 bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, target_size[0] - 1)586 bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, target_size[1] - 1)587 mask = np.logical_and(mask, (bboxes[:, :2] < bboxes[:, 2:]).all(axis=1))588 bboxes = bboxes[mask]589 if labels is not None:590 labels = labels[mask]591 if polygons is not None:592 new_polygons = list()593 for object_id in range(len(polygons)):594 if mask[object_id] == 1:595 for polygon_id in range(len(polygons[object_id])):596 polygons[object_id][polygon_id][0::2] -= offset_left597 polygons[object_id][polygon_id][1::2] -= offset_up598 polygons[object_id][polygon_id][0::2] = np.clip(polygons[object_id][polygon_id][0::2],599 0, target_size[0] - 1)600 polygons[object_id][polygon_id][1::2] = np.clip(polygons[object_id][polygon_id][1::2],601 0, target_size[1] - 1)602 new_polygons.append(polygons[object_id])603 polygons = new_polygons604 if not isinstance(img, list):605 img = img[offset_up:offset_up + target_size[1], offset_left:offset_left + target_size[0]]606 else:607 img = [item[offset_up:offset_up + target_size[1], offset_left:offset_left + target_size[0]] for item in img]608 if maskmap is not None:609 maskmap = maskmap[offset_up:offset_up + target_size[1], offset_left:offset_left + target_size[0]]610 if labelmap is not None:611 labelmap = labelmap[offset_up:offset_up + target_size[1], offset_left:offset_left + target_size[0]]612 return img, labelmap, maskmap, kpts, bboxes, labels, polygons613class RandomFocusCrop(object):614 """Crop the given numpy.ndarray and at a random location.615 Args:616 size (int or tuple): Desired output size of the crop.(w, h)617 """618 def __init__(self, crop_size, ratio=0.5, center_jitter=None, mean=(104, 117, 123), allow_outside_center=True):619 self.ratio = ratio620 self.center_jitter = center_jitter621 self.mean = mean622 self.allow_outside_center = allow_outside_center623 if isinstance(crop_size, float):624 self.size = (crop_size, crop_size)625 elif isinstance(crop_size, collections.Iterable) and len(crop_size) == 2:626 self.size = crop_size627 else:628 raise TypeError('Got inappropriate size arg: {}'.format(crop_size))629 def get_center(self, img_size, bboxes):630 if bboxes is None or bboxes.size == 0:631 if img_size[0] > self.size[0]:632 x = random.randint(self.size[0] // 2, img_size[0] - self.size[0] // 2)633 else:634 x = img_size[0] // 2635 if img_size[1] > self.size[1]:636 y = random.randint(self.size[1] // 2, img_size[1] - self.size[1] // 2)637 else:638 y = img_size[1] // 2639 return [x, y], -1640 else:641 border = bboxes[:, 2:] - bboxes[:, 0:2]642 area = border[:, 0] * border[:, 1]643 max_index = np.argmax(area)644 max_center = [(bboxes[max_index][0] + bboxes[max_index][2]) / 2,645 (bboxes[max_index][1] + bboxes[max_index][3]) / 2]646 if self.center_jitter is not None:647 jitter = random.randint(-self.center_jitter, self.center_jitter)648 max_center[0] += jitter649 jitter = random.randint(-self.center_jitter, self.center_jitter)650 max_center[1] += jitter651 return max_center, max_index652 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):653 """654 Args:655 img (Image): Image to be cropped.656 maskmap (Image): Mask to be cropped.657 Returns:658 Image: Cropped image.659 Image: Cropped maskmap.660 list: Cropped keypoints.661 list: Cropped center points.662 """663 assert isinstance(img, np.ndarray)664 assert labelmap is None or isinstance(labelmap, np.ndarray)665 assert maskmap is None or isinstance(maskmap, np.ndarray)666 if random.random() > self.ratio:667 return img, labelmap, maskmap, kpts, bboxes, labels, polygons668 height, width, channels = img.shape669 center, index = self.get_center([width, height], bboxes)670 # img = ImageHelper.draw_box(img, bboxes[index])671 offset_left = int(center[0] - self.size[0] // 2)672 offset_up = int(center[1] - self.size[1] // 2)673 if kpts is not None and kpts.size > 0:674 kpts[:, :, 0] -= offset_left675 kpts[:, :, 1] -= offset_up676 mask = np.logical_or.reduce((kpts[:, :, 0] >= self.size[0], kpts[:, :, 0] < 0,677 kpts[:, :, 1] >= self.size[1], kpts[:, :, 1] < 0))678 kpts[mask == 1, 2] = -1679 if bboxes is not None and bboxes.size > 0:680 if self.allow_outside_center:681 mask = np.ones(bboxes.shape[0], dtype=bool)682 else:683 crop_bb = np.array([offset_left, offset_up, offset_left + self.size[0], offset_up + self.size[1]])684 center = (bboxes[:, :2] + bboxes[:, 2:]) / 2685 mask = np.logical_and(crop_bb[:2] <= center, center < crop_bb[2:]).all(axis=1)686 bboxes[:, 0::2] -= offset_left687 bboxes[:, 1::2] -= offset_up688 bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, self.size[0] - 1)689 bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, self.size[1] - 1)690 mask = np.logical_and(mask, (bboxes[:, :2] < bboxes[:, 2:]).all(axis=1))691 bboxes = bboxes[mask]692 if labels is not None:693 labels = labels[mask]694 if polygons is not None:695 new_polygons = list()696 for object_id in range(len(polygons)):697 if mask[object_id] == 1:698 for polygon_id in range(len(polygons[object_id])):699 polygons[object_id][polygon_id][0::2] -= offset_left700 polygons[object_id][polygon_id][1::2] -= offset_up701 polygons[object_id][polygon_id][0::2] = np.clip(polygons[object_id][polygon_id][0::2],702 0, self.size[0] - 1)703 polygons[object_id][polygon_id][1::2] = np.clip(polygons[object_id][polygon_id][1::2],704 0, self.size[1] - 1)705 new_polygons.append(polygons[object_id])706 polygons = new_polygons707 expand_image = np.zeros((max(height, self.size[1]) + abs(offset_up),708 max(width, self.size[0]) + abs(offset_left), channels), dtype=img.dtype)709 expand_image[:, :, :] = self.mean710 expand_image[abs(min(offset_up, 0)):abs(min(offset_up, 0)) + height,711 abs(min(offset_left, 0)):abs(min(offset_left, 0)) + width] = img712 img = expand_image[max(offset_up, 0):max(offset_up, 0) + self.size[1],713 max(offset_left, 0):max(offset_left, 0) + self.size[0]]714 if maskmap is not None:715 expand_maskmap = np.zeros((max(height, self.size[1]) + abs(offset_up),716 max(width, self.size[0]) + abs(offset_left)), dtype=maskmap.dtype)717 expand_maskmap[:, :] = 1718 expand_maskmap[abs(min(offset_up, 0)):abs(min(offset_up, 0)) + height,719 abs(min(offset_left, 0)):abs(min(offset_left, 0)) + width] = maskmap720 maskmap = expand_maskmap[max(offset_up, 0):max(offset_up, 0) + self.size[1],721 max(offset_left, 0):max(offset_left, 0) + self.size[0]]722 if labelmap is not None:723 expand_labelmap = np.zeros((max(height, self.size[1]) + abs(offset_up),724 max(width, self.size[0]) + abs(offset_left)), dtype=labelmap.dtype)725 expand_labelmap[:, :] = 255726 expand_labelmap[abs(min(offset_up, 0)):abs(min(offset_up, 0)) + height,727 abs(min(offset_left, 0)):abs(min(offset_left, 0)) + width] = labelmap728 labelmap = expand_labelmap[max(offset_up, 0):max(offset_up, 0) + self.size[1],729 max(offset_left, 0):max(offset_left, 0) + self.size[0]]730 return img, labelmap, maskmap, kpts, bboxes, labels, polygons731class RandomDetCrop(object):732 """Crop733 Arguments:734 img (Image): the image being input during training735 boxes (Tensor): the original bounding boxes in pt form736 labels (Tensor): the class labels for each bbox737 mode (float tuple): the min and max jaccard overlaps738 Return:739 (img, boxes, classes)740 img (Image): the cropped image741 boxes (Tensor): the adjusted bounding boxes in pt form742 labels (Tensor): the class labels for each bbox743 """744 def __init__(self, ratio=0.5, allow_outside_center=True):745 self.ratio = ratio746 self.allow_outside_center = allow_outside_center747 self.sample_options = (748 # using entire original input image749 None,750 # sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9751 (0.1, None),752 (0.3, None),753 (0.5, None),754 (0.7, None),755 (0.9, None),756 # randomly sample a patch757 (None, None),758 )759 @staticmethod760 def intersect(box_a, box_b):761 max_xy = np.minimum(box_a[:, 2:], box_b[2:])762 min_xy = np.maximum(box_a[:, :2], box_b[:2])763 inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf)764 return inter[:, 0] * inter[:, 1]765 @staticmethod766 def jaccard_numpy(box_a, box_b):767 """Compute the jaccard overlap of two sets of boxes. The jaccard overlap768 is simply the intersection over union of two boxes.769 E.g.:770 A ⩠B / A ⪠B = A ⩠B / (area(A) + area(B) - A ⩠B)771 Args:772 box_a: Multiple bounding boxes, Shape: [num_boxes,4]773 box_b: Single bounding box, Shape: [4]774 Return:775 jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]]776 """777 inter = RandomDetCrop.intersect(box_a, box_b)778 area_a = ((box_a[:, 2] - box_a[:, 0]) *779 (box_a[:, 3] - box_a[:, 1])) # [A,B]780 area_b = ((box_b[2] - box_b[0]) *781 (box_b[3] - box_b[1])) # [A,B]782 union = area_a + area_b - inter783 return inter / union # [A,B]784 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):785 assert labelmap is None and maskmap is None and kpts is None and polygons is None786 assert bboxes is not None and labels is not None787 if random.random() > self.ratio:788 return img, labelmap, maskmap, kpts, bboxes, labels, polygons789 height, width, _ = img.shape790 while True:791 # randomly choose a mode792 mode = random.choice(self.sample_options)793 if mode is None or bboxes.size == 0:794 return img, labelmap, maskmap, kpts, bboxes, labels, polygons795 min_iou, max_iou = mode796 if min_iou is None:797 min_iou = float('-inf')798 if max_iou is None:799 max_iou = float('inf')800 # max trails (50)801 for _ in range(50):802 scale = random.uniform(0.3, 1.)803 min_ratio = max(0.5, scale * scale)804 max_ratio = min(2.0, 1. / scale / scale)805 ratio = math.sqrt(random.uniform(min_ratio, max_ratio))806 w = int(scale * ratio * width)807 h = int((scale / ratio) * height)808 left = random.randint(0, width - w)809 top = random.randint(0, height - h)810 # convert to integer rect x1,y1,x2,y2811 rect = np.array([int(left), int(top), int(left + w), int(top + h)])812 # calculate IoU (jaccard overlap) b/t the cropped and gt boxes813 overlap = self.jaccard_numpy(bboxes, rect)814 # is min and max overlap constraint satisfied? if not try again815 if overlap.min() < min_iou or max_iou < overlap.max():816 continue817 # keep overlap with gt box IF center in sampled patch818 centers = (bboxes[:, :2] + bboxes[:, 2:]) / 2.0819 # mask in all gt boxes that above and to the left of centers820 m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])821 # mask in all gt boxes that under and to the right of centers822 m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])823 # mask in that both m1 and m2 are true824 mask = m1 * m2825 # have any valid boxes? try again if not826 if not mask.any():827 continue828 # take only matching gt boxes829 current_boxes = bboxes[mask, :].copy()830 # cut the crop from the image831 current_img = img[rect[1]:rect[3], rect[0]:rect[2], :]832 # take only matching gt labels833 current_labels = labels[mask]834 # should we use the box left and top corner or the crop's835 current_boxes[:, :2] = np.maximum(current_boxes[:, :2], rect[:2])836 # adjust to crop (by substracting crop's left,top)837 current_boxes[:, :2] -= rect[:2]838 current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:], rect[2:])839 # adjust to crop (by substracting crop's left,top)840 current_boxes[:, 2:] -= rect[:2]841 return current_img, labelmap, maskmap, kpts, current_boxes, current_labels, polygons842class Resize(object):843 """Resize the given numpy.ndarray to random size and aspect ratio.844 Args:845 scale_min: the min scale to resize.846 scale_max: the max scale to resize.847 """848 def __init__(self, target_size=None, min_side_length=None, max_side_length=None):849 self.target_size = target_size850 self.min_side_length = min_side_length851 self.max_side_length = max_side_length852 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):853 assert isinstance(img, (np.ndarray, list))854 assert labelmap is None or isinstance(labelmap, np.ndarray)855 assert maskmap is None or isinstance(maskmap, np.ndarray)856 height, width, _ = img.shape857 if self.target_size is not None:858 target_size = self.target_size859 w_scale_ratio = self.target_size[0] / width860 h_scale_ratio = self.target_size[1] / height861 elif self.min_side_length is not None:862 scale_ratio = self.min_side_length / min(width, height)863 w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio864 target_size = [int(round(width * w_scale_ratio)), int(round(height * h_scale_ratio))]865 else:866 scale_ratio = self.max_side_length / max(width, height)867 w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio868 target_size = [int(round(width * w_scale_ratio)), int(round(height * h_scale_ratio))]869 if kpts is not None and kpts.size > 0:870 kpts[:, :, 0] *= w_scale_ratio871 kpts[:, :, 1] *= h_scale_ratio872 if bboxes is not None and bboxes.size > 0:873 bboxes[:, 0::2] *= w_scale_ratio874 bboxes[:, 1::2] *= h_scale_ratio875 if polygons is not None:876 for object_id in range(len(polygons)):877 for polygon_id in range(len(polygons[object_id])):878 polygons[object_id][polygon_id][0::2] *= w_scale_ratio879 polygons[object_id][polygon_id][1::2] *= h_scale_ratio880 target_size = tuple(target_size)881 if not isinstance(img, list):882 img = cv2.resize(img, target_size, interpolation=cv2.INTER_LINEAR)883 else:884 img = [cv2.resize(item, target_size, interpolation=cv2.INTER_LINEAR) for item in img]885 if labelmap is not None:886 labelmap = cv2.resize(labelmap, target_size, interpolation=cv2.INTER_NEAREST)887 if maskmap is not None:888 maskmap = cv2.resize(maskmap, target_size, interpolation=cv2.INTER_NEAREST)889 return img, labelmap, maskmap, kpts, bboxes, labels, polygons890CV2_AUGMENTATIONS_DICT = {891 'random_saturation': RandomSaturation,892 'random_hue': RandomHue,893 'random_perm': RandomPerm,894 'random_contrast': RandomContrast,895 'random_brightness': RandomBrightness,896 'random_pad': RandomPad,897 'padding': Padding,898 'random_hflip': RandomHFlip,899 'random_resize': RandomResize,900 'random_crop': RandomCrop,901 'random_focus_crop': RandomFocusCrop,902 'random_det_crop': RandomDetCrop,903 'random_resized_crop': RandomResizedCrop,904 'random_rotate': RandomRotate,905 'resize': Resize906}907class CV2AugCompose(object):908 """Composes several transforms together.909 Args:910 transforms (list of ``Transform`` objects): list of transforms to compose.911 Example:912 >>> CV2AugCompose([913 >>> RandomCrop(),914 >>> ])915 """916 def __init__(self, configer, split='train'):917 self.configer = configer918 self.transforms = dict()919 self.split = split920 aug_trans = self.configer.get(split, 'aug_trans')921 shuffle_train_trans = []922 if 'shuffle_trans_seq' in aug_trans:923 if isinstance(aug_trans['shuffle_trans_seq'][0], list):924 train_trans_seq_list = aug_trans['shuffle_trans_seq']925 for train_trans_seq in train_trans_seq_list:926 shuffle_train_trans += train_trans_seq927 else:928 shuffle_train_trans = aug_trans['shuffle_trans_seq']929 for trans in aug_trans['trans_seq'] + shuffle_train_trans:930 self.transforms[trans] = CV2_AUGMENTATIONS_DICT[trans](**aug_trans[trans])931 def __call__(self, img, labelmap=None, maskmap=None, kpts=None, bboxes=None, labels=None, polygons=None):932 if self.configer.get('data', 'input_mode') == 'RGB':933 img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)934 aug_trans = self.configer.get(self.split, 'aug_trans')935 shuffle_trans_seq = []936 if 'shuffle_trans_seq' in aug_trans:937 if isinstance(aug_trans['shuffle_trans_seq'][0], list):938 shuffle_trans_seq_list = aug_trans['shuffle_trans_seq']939 shuffle_trans_seq = shuffle_trans_seq_list[random.randint(0, len(shuffle_trans_seq_list))]940 else:941 shuffle_trans_seq = aug_trans['shuffle_trans_seq']942 random.shuffle(shuffle_trans_seq)943 for trans_key in (shuffle_trans_seq + aug_trans['trans_seq']):944 (img, labelmap, maskmap, kpts,945 bboxes, labels, polygons) = self.transforms[trans_key](img, labelmap, maskmap,946 kpts, bboxes, labels, polygons)947 if self.configer.get('data', 'input_mode') == 'RGB':948 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)949 out_list = [img]950 for elem in [labelmap, maskmap, kpts, bboxes, labels, polygons]:951 if elem is not None:952 out_list.append(elem)...
dtmViewer.py
Source:dtmViewer.py
1from pysvg.filter import *2from pysvg.gradient import *3from pysvg.linking import *4from pysvg.script import *5from pysvg.shape import *6from pysvg.structure import *7from pysvg.style import *8from pysvg.text import *9from pysvg.builders import *10from lxml import etree11import sys12import matplotlib13matplotlib.use('Agg')14import matplotlib.pyplot as plt15import os16import os.path17PIX_BY_SEC = 22018HEIGHT_BY_WORKER = 11019RESOLUTION_TASK_DISCOVER = 0.00520OFFSET_LEFT = 4021colorsList = ['#ee0000', '#00bb00', '#0000ff', '#aaaa00', '#00aaaa', '#000077', '#cc00cc']22funcAssignDict = {}23workersTimeDelta = []24taskDict = {}25class badDispatchFinder(object):26 def __init__(self, maxTime, tasksDict):27 self.timeBlocks = [0 for i in xrange(int(maxTime / RESOLUTION_TASK_DISCOVER))]28 for task in tasksDict.values():29 self.addTask(task)30 return31 def addTask(self, taskInfo):32 for i in xrange(int(taskInfo[0] / RESOLUTION_TASK_DISCOVER), int(taskInfo[1][0] / RESOLUTION_TASK_DISCOVER)):33 self.timeBlocks[i] += 134 def drawParts(self, svgObj, nbrWorkers):35 i = 036 oh = ShapeBuilder()37 myStyle = StyleBuilder()38 myStyle.setFontFamily(fontfamily="Verdana")39 myStyle.setFontWeight('bold')40 myStyle.setFontSize('14pt')41 txt = text("Parallelisable?", 5, 20)42 txt.set_style(myStyle.getStyle())43 svgObj.addElement(txt)44 while i < len(self.timeBlocks):45 if self.timeBlocks[i] > nbrWorkers:46 beginRect = i47 while i < len(self.timeBlocks) and self.timeBlocks[i] > nbrWorkers:48 i += 149 endRect = i50 svgObj.addElement(oh.createRect(OFFSET_LEFT + beginRect * RESOLUTION_TASK_DISCOVER * PIX_BY_SEC + 2, 5, (endRect - beginRect) * RESOLUTION_TASK_DISCOVER * PIX_BY_SEC + 2, 18, strokewidth=2, fill='orange', stroke='white'))51 else:52 i += 153def synchroWorkersTime(workerFileBeginPath):54 global workersTimeDelta55 indexF = 056 msgTreeList = []57 while os.path.exists(workerFileBeginPath + str(indexF) + ".xml"):58 xmlTree = etree.parse(workerFileBeginPath + str(indexF) + ".xml")59 msgTreeList.append(xmlTree.getroot()[3]) #60 assert msgTreeList[-1].tag == 'commLog'61 indexF += 162 workersTimeDelta.append(0.) # Par definition, la reference est le premier worker63 # On va chercher les premiers messages qu'il a envoye a chaque worker64 for i in xrange(1, len(msgTreeList)):65 j = 066 while int(msgTreeList[0][j].get('otherWorker')) != i or msgTreeList[0][j].get('direc') != "out":67 #print("[0]",msgTreeList[0][j].get('otherWorker'), msgTreeList[0][j].get('msgtag'), msgTreeList[0][j].get('type'))68 j += 169 msgTagRootWorker, timeRootWorker = int(msgTreeList[0][j].get('msgtag')), float(msgTreeList[0][j].get('time'))70 k = 071 while int(msgTreeList[i][k].get('otherWorker')) != 0 or msgTreeList[i][k].get('direc') != "in":72 #print("[" +str(i)+"]", msgTreeList[i][k].get('otherWorker'), msgTreeList[i][k].get('msgtag'), msgTreeList[i][k].get('type'))73 k += 174 msgTagOtherWorker, timeOtherWorker = int(msgTreeList[i][k].get('msgtag')), float(msgTreeList[i][k].get('time'))75 assert msgTagRootWorker == msgTagOtherWorker76 workersTimeDelta.append(timeOtherWorker - timeRootWorker)77 return78def traceWorker(svgO, workerFile, offset, decalageTemps=0.):79 global funcAssignDict80 global taskDict81 oh = ShapeBuilder()82 xmlTree = etree.parse(workerFile)83 print("ADD WORKER ", workerFile)84 assert xmlTree.getroot().tag == 'dtm'85 wbt = float(xmlTree.getroot().get('timeBegin'))86 myStyle = StyleBuilder()87 myStyle.setFontFamily(fontfamily="Verdana")88 myStyle.setFontWeight('bold')89 myStyle.setFontSize('14pt')90 myStyle.setFilling('blue')91 txt = text("Worker " + str(xmlTree.getroot().get('workerId')), 5, offset + HEIGHT_BY_WORKER / 2)92 txt.set_style(myStyle.getStyle())93 svgO.addElement(txt)94 maxEndTime = 0.95 dataTask = ""96 taskTarget = ""97 for taskTag in xmlTree.getroot()[0]:98 beginTimes = []99 endTimes = []100 for taskInfo in taskTag:101 if taskInfo.tag == 'event' and taskInfo.get('type') == "begin" or taskInfo.get('type') == 'wakeUp':102 #print(taskInfo.get('time'))103 beginTimes.append(float(taskInfo.get('time')) - wbt + decalageTemps)104 elif taskInfo.tag == 'event' and taskInfo.get('type') == "end" or taskInfo.get('type') == 'sleep':105 assert len(endTimes) < len(beginTimes)106 endTimes.append(float(taskInfo.get('time')) - wbt + decalageTemps)107 if maxEndTime < endTimes[-1]:108 maxEndTime = endTimes[-1]109 elif taskInfo.tag == 'target':110 if not taskInfo.get('name') in funcAssignDict:111 print("Choose color " + colorsList[len(funcAssignDict)])112 funcAssignDict[taskInfo.get('name')] = colorsList[len(funcAssignDict)]113 dataTask = taskInfo.get('name') + " : "114 taskTarget = taskInfo.get('name')115 indexArg = 0116 while not (taskInfo.get('arg' + str(indexArg)) is None):117 dataTask += taskInfo.get('arg' + str(indexArg)) + ", "118 indexArg += 1119 taskDict[taskTag.get('id')] = (float(taskTag.get('creationTime')) - wbt + decalageTemps, beginTimes, endTimes)120 #print(beginTimes, endTimes)121 for i in xrange(len(beginTimes)):122 if endTimes[i] - beginTimes[i] < 5. / PIX_BY_SEC:123 #endTimes[i] += 4. / PIX_BY_SEC124 svgO.addElement(oh.createLine(OFFSET_LEFT + decalageTemps * PIX_BY_SEC + beginTimes[i] * PIX_BY_SEC + 1, offset, OFFSET_LEFT + decalageTemps * PIX_BY_SEC + beginTimes[i] * PIX_BY_SEC + 1, offset + HEIGHT_BY_WORKER, strokewidth=3, stroke=funcAssignDict[taskTarget]))125 else:126 svgO.addElement(oh.createRect(OFFSET_LEFT + decalageTemps * PIX_BY_SEC + beginTimes[i] * PIX_BY_SEC + 2, offset, endTimes[i] * PIX_BY_SEC - 2 - (beginTimes[i] * PIX_BY_SEC + 2), HEIGHT_BY_WORKER, strokewidth=3, stroke=funcAssignDict[taskTarget]))127 #svgO.addElement(text(dataTask, beginTime * PIX_BY_SEC + 25, offset + HEIGHT_BY_WORKER / 2))128 loadsList = []129 timesList = []130 for loadTag in xmlTree.getroot()[1]:131 tmpLoad = []132 for workerState in loadTag:133 if workerState.tag != "workerKnownState":134 continue135 loadWorker = [float(i) for i in workerState.get('load').split(",")]136 tmpLoad.append(loadWorker[0]+loadWorker[1]+loadWorker[2]) # En cours, en attente de demarrage et en attente de redemarrage137 loadsList.append(tmpLoad)138 timesList.append(float(loadTag.get('time')))139 timesList = [t-wbt+decalageTemps for t in timesList]140 plt.figure()141 if len(sys.argv) > 3 and sys.argv[3] == "log":142 plt.yscale('log', nonposy='clip')143 count = 0144 for line in zip(*loadsList):145 plt.plot(timesList, line, label='Worker '+str(count))146 count += 1147 plt.legend()148 plt.xlabel('Temps normalise')149 plt.ylabel('Load estime')150 plt.savefig(sys.argv[2] + 'loads_'+str(xmlTree.getroot().get('workerId'))+'.png')151 return maxEndTime152def traceTimeline(svgO, offset, upTo):153 oh = ShapeBuilder()154 svgO.addElement(oh.createRect(OFFSET_LEFT - 10, offset + 5, OFFSET_LEFT + (upTo + 1) * PIX_BY_SEC + 10, HEIGHT_BY_WORKER / 2))155 for i in range(int(upTo) + 2):156 svgO.addElement(text(str(i), OFFSET_LEFT + i * PIX_BY_SEC - 5 * len(str(i)), offset + 40))157 for i in range((int(upTo) + 2) * 10):158 if i % 10 == 0:159 svgO.addElement(oh.createLine(OFFSET_LEFT + i * PIX_BY_SEC / 10, offset + 5, OFFSET_LEFT + i * PIX_BY_SEC / 10, offset + 20, stroke='red', strokewidth=2))160 else:161 svgO.addElement(oh.createLine(OFFSET_LEFT + i * PIX_BY_SEC / 10, offset + 5, OFFSET_LEFT + i * PIX_BY_SEC / 10, offset + 20, stroke='black', strokewidth=2))162 myStyle = StyleBuilder()163 myStyle.setFontFamily(fontfamily="Verdana")164 myStyle.setFontStyle('italic')165 myStyle.setFontSize('12pt') #no need for the keywords all the time166 txt = text("Time (seconds)", OFFSET_LEFT + 10, offset + 55)167 txt.set_style(myStyle.getStyle())168 svgO.addElement(txt)169 return170def printFuncNames(svgO, offset):171 global funcAssignDict172 myStyle = StyleBuilder()173 myStyle.setFontFamily(fontfamily="Verdana")174 myStyle.setFontWeight('bold')175 myStyle.setFontSize('20pt') #no need for the keywords all the time176 tmpOff = offset177 txt = text("Functions color map :", 10, tmpOff)178 txt.set_style(myStyle.getStyle())179 svgO.addElement(txt)180 tmpOff += 35181 for key in funcAssignDict:182 myStyle.setFilling(funcAssignDict[key])183 txt = text(str(key), 30, tmpOff)184 txt.set_style(myStyle.getStyle())185 svgO.addElement(txt)186 tmpOff += 30187def main():188 global taskDict189 global workersTimeDelta190 objSvg = svg()191 indexF = 0192 maxEndTime = 0.193 tmpEndTime = 0.194 synchroWorkersTime(sys.argv[1]+"/log")195 print(workersTimeDelta)196 while os.path.exists(sys.argv[1]+"/log" + str(indexF) + ".xml"):197 tmpEndTime = traceWorker(objSvg, sys.argv[1]+"/log" + str(indexF) + ".xml", indexF * (HEIGHT_BY_WORKER + 10) + 30, workersTimeDelta[indexF])198 if tmpEndTime > maxEndTime:199 maxEndTime = tmpEndTime200 indexF += 1201 traceTimeline(objSvg, indexF * (HEIGHT_BY_WORKER + 10) + 30, maxEndTime)202 printFuncNames(objSvg, (indexF + 1) * (HEIGHT_BY_WORKER + 10) + 30)203 cb = badDispatchFinder(maxEndTime, taskDict)204 cb.drawParts(objSvg, indexF + 1)205 print("FINISHED...")206 objSvg.save(sys.argv[2])207if __name__ == '__main__':208 print("HOP!")...
test.py
Source:test.py
1#!/usr/bin/env python2from PIL import ImageFont3import inkyphat4font_file = inkyphat.fonts.FredokaOne5inkyphat.arc((0, 0, 212, 104), 0, 180, 2)6top = 07left = 08offset_left = 09for font_size in (10, 12, 14, 16, 18, 20):10 text = "Test {}".format(font_size)11 font = inkyphat.ImageFont.truetype(font_file, font_size)12 width, height = font.getsize(text)13 inkyphat.text((0, top), text, 1, font=font)14 top += height + 115 left = max(left, offset_left + width)16offset_left = left + 517top = 018for font_size in (22, 24, 26, 28):19 text = "Test {}".format(font_size)20 font = inkyphat.ImageFont.truetype(font_file, font_size)21 width, height = font.getsize(text)22 inkyphat.text((offset_left, top), text, 1, font=font)23 top += height + 124 left = max(left, offset_left + width)25offset_left = left + 526top = 027for font_size in (30, 32, 34):28 text = "Test {}".format(font_size)29 font = inkyphat.ImageFont.truetype(font_file, font_size)30 width, height = font.getsize(text)31 inkyphat.text((offset_left, top), text, 1, font=font)32 top += height + 133 left = max(left, offset_left + width)...
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