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How to use export_image method in localstack
Best Python code snippet using localstack_python
analysis_figure_histogram_logpx_bins.py
Source:analysis_figure_histogram_logpx_bins.py 
...157def tensor_to_image(obs: torch.Tensor) -> Image:158 return Image.fromarray(159 np.uint8(255 * obs.permute(1, 2, 0).squeeze().numpy())160 )161def export_image(sd: SplitDataset, idx: int, output_dir, relpath=False) -> str:162 obs = sd.get_item_by_index(idx)[0]163 img = tensor_to_image(obs)164 filename = f"{sd.name}_{idx}.png"165 pth = os.path.join(output_dir, filename)166 img.save(pth)167 return filename if relpath else pth168def random_satisfying(*args, size=1):169 first = args[0]170 assert isinstance(first, np.ndarray)171 init = np.ones_like(first, dtype=bool)172 cond = reduce(np.logical_and, args, init)173 indices = np.where(cond)[0]174 return np.random.choice(indices, size=size, replace=False)175def main():176 args = parse_args()177 np.random.seed(args.seed if args.seed else np.random.randint(10000))178 results = np.load(args.input, allow_pickle=True)179 metadata = results["metadata"][()]180 dataset = metadata["dataset"]181 root_seed = metadata["seed"]182 sd = SplitDataset(dataset, root_seed, params={"resize_64": True})183 outdir = args.outdir if args.outdir else os.path.dirname(args.output)184 relpath = args.outdir is None185 U = results["U"][:, -1]186 M = results["M"][:, -1]187 q = 0.95188 if dataset == "CelebA":189 binwidth = 500190 U_min = -17_000191 U_max = -11_000192 y_max = 60_000193 xticks = list(range(U_min, U_max + 1000, 1000))194 yticks = ["0", "20k", "40k", "60k"]195 legend_pos = "north east"196 elif dataset == "BinarizedMNIST" and metadata["learning_rate"] == 1e-3:197 binwidth = 10198 U_min = -150199 U_max = -30200 y_max = 16000201 xticks = list(range(U_min, U_max + 10, 20))202 yticks = ["0", "4k", "8k", "12k", "16k"]203 legend_pos = "north east"204 elif dataset == "BinarizedMNIST" and metadata["learning_rate"] == 1e-4:205 binwidth = 10206 U_min = -160207 U_max = -30208 y_max = 15000209 xticks = list(range(U_min, U_max + 10, 20))210 yticks = ["0", "5k", "10k", "15k"]211 legend_pos = "north east"212 else:213 raise NotImplementedError214 n_bins = (U_max - U_min) // binwidth + 1215 bins = (216 [-float("inf")]217 + [U_min + binwidth * i for i in range(n_bins)]218 + [float("inf")]219 )220 heights_reg = []221 heights_high = []222 midpoints = []223 for lb, ub in zip(bins[:-1], bins[1:]):224 midpoints.append(ub - binwidth / 2)225 in_bin = np.logical_and(U > lb, U <= ub)226 count_reg = np.sum(np.logical_and(in_bin, M <= np.quantile(M, q)))227 count_high = np.sum(np.logical_and(in_bin, M > np.quantile(M, q)))228 heights_reg.append(count_reg)229 heights_high.append(count_high)230 midpoints[-1] = midpoints[-2] + binwidth231 heights_reg = np.array(heights_reg)232 heights_high = np.array(heights_high)233 midpoints = np.array(midpoints)234 images = {}235 bottom_lines = []236 top_lines = []237 if dataset == "CelebA":238 xpos = U_min239 ypos = 19_000240 # Low logprob, low mem241 idx1, idx2 = random_satisfying(242 U < U_min, M < np.quantile(M, 0.5), size=2243 )244 pth1 = export_image(sd, idx1, outdir, relpath=relpath)245 pth2 = export_image(sd, idx2, outdir, relpath=relpath)246 images[pth1] = dict(247 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]248 )249 images[pth2] = dict(250 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]251 )252 # Low logprob, high mem253 idx1, idx2 = random_satisfying(254 U < U_min, M > np.quantile(M, 0.999), size=2255 )256 pth1 = export_image(sd, idx1, outdir, relpath=relpath)257 pth2 = export_image(sd, idx2, outdir, relpath=relpath)258 images[pth1] = dict(259 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]260 )261 images[pth2] = dict(262 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]263 )264 bottom_lines.append([(U_min - binwidth / 2, 4000), (U_min, 18_500)])265 ###266 ###267 xpos = -16_250268 # Med low log prob, low mem (1)269 idx1, idx2 = random_satisfying(270 U < -15_000, U > -15_500, M < np.quantile(M, 0.1), size=2271 )272 pth1 = export_image(sd, idx1, outdir, relpath=relpath)273 pth2 = export_image(sd, idx2, outdir, relpath=relpath)274 images[pth1] = dict(275 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]276 )277 images[pth2] = dict(278 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]279 )280 # Med low log prob, high mem (1)281 idx1, idx2 = random_satisfying(282 U < -15_000, U > -15_500, M > np.quantile(M, 0.999), size=2283 )284 pth1 = export_image(sd, idx1, outdir, relpath=relpath)285 pth2 = export_image(sd, idx2, outdir, relpath=relpath)286 images[pth1] = dict(287 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]288 )289 images[pth2] = dict(290 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]291 )292 bottom_lines.append([(-15_000 - binwidth / 2, 5000), (xpos, 18_500)])293 ###294 ###295 xpos = -15_500296 # Central log prob, low mem (1)297 idx1, idx2 = random_satisfying(298 U < -14_000, U > -14_500, M < np.quantile(M, 0.1), size=2299 )300 pth1 = export_image(sd, idx1, outdir, relpath=relpath)301 pth2 = export_image(sd, idx2, outdir, relpath=relpath)302 images[pth1] = dict(303 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]304 )305 images[pth2] = dict(306 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]307 )308 # Central log prob, high mem (1)309 idx1, idx2 = random_satisfying(310 U < -14_000, U > -14_500, M > np.quantile(M, 0.999), size=2311 )312 pth1 = export_image(sd, idx1, outdir, relpath=relpath)313 pth2 = export_image(sd, idx2, outdir, relpath=relpath)314 images[pth1] = dict(315 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]316 )317 images[pth2] = dict(318 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]319 )320 bottom_lines.append([(-14_250, 14_000), (xpos, 18_500)])321 ###322 ###323 xpos = -14_750324 # Central log prob, low mem (1)325 idx1, idx2 = random_satisfying(326 U < -13_000, U > -13_500, M < np.quantile(M, 0.1), size=2327 )328 pth1 = export_image(sd, idx1, outdir, relpath=relpath)329 pth2 = export_image(sd, idx2, outdir, relpath=relpath)330 images[pth1] = dict(331 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]332 )333 images[pth2] = dict(334 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]335 )336 # Central log prob, high mem (1)337 idx1, idx2 = random_satisfying(338 U < -13_000, U > -13_500, M > np.quantile(M, 0.995), size=2339 )340 pth1 = export_image(sd, idx1, outdir, relpath=relpath)341 pth2 = export_image(sd, idx2, outdir, relpath=relpath)342 images[pth1] = dict(343 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]344 )345 images[pth2] = dict(346 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]347 )348 top_lines.append([(-13250, 53_000), (-14_000, 59_000), (xpos, 58000)])349 elif dataset == "BinarizedMNIST" and metadata["learning_rate"] == 1e-3:350 xpos = -151351 ypos = 5_200352 # Low logprob, low mem353 idx1, idx2 = random_satisfying(354 U < U_min, M < np.quantile(M, 0.5), size=2355 )356 pth1 = export_image(sd, idx1, outdir, relpath=relpath)357 pth2 = export_image(sd, idx2, outdir, relpath=relpath)358 images[pth1] = dict(359 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]360 )361 images[pth2] = dict(362 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]363 )364 # Low logprob, high mem365 idx1, idx2 = random_satisfying(366 U < U_min, M > np.quantile(M, 0.999), size=2367 )368 pth1 = export_image(sd, idx1, outdir, relpath=relpath)369 pth2 = export_image(sd, idx2, outdir, relpath=relpath)370 images[pth1] = dict(371 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]372 )373 images[pth2] = dict(374 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]375 )376 bottom_lines.append([(U_min - binwidth / 2, 1200), (U_min, 4_900)])377 ####378 ####379 xpos = -138380 # Med low log prob, low mem (1)381 idx1, idx2 = random_satisfying(382 U < -110, U > -120, M < np.quantile(M, 0.1), size=2383 )384 pth1 = export_image(sd, idx1, outdir, relpath=relpath)385 pth2 = export_image(sd, idx2, outdir, relpath=relpath)386 images[pth1] = dict(387 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]388 )389 images[pth2] = dict(390 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]391 )392 # Med low log prob, high mem (1)393 idx1, idx2 = random_satisfying(394 U < -110, U > -120, M > np.quantile(M, 0.999), size=2395 )396 pth1 = export_image(sd, idx1, outdir, relpath=relpath)397 pth2 = export_image(sd, idx2, outdir, relpath=relpath)398 images[pth1] = dict(399 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]400 )401 images[pth2] = dict(402 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]403 )404 bottom_lines.append([(-110 - binwidth / 2, 3000), (xpos, 4_900)])405 ###406 ###407 xpos = -125408 # Central log prob, low mem (1)409 idx1, idx2 = random_satisfying(410 U < -80, U > -90, M < np.quantile(M, 0.1), size=2411 )412 pth1 = export_image(sd, idx1, outdir, relpath=relpath)413 pth2 = export_image(sd, idx2, outdir, relpath=relpath)414 images[pth1] = dict(415 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]416 )417 images[pth2] = dict(418 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]419 )420 # Central log prob, high mem (1)421 idx1, idx2 = random_satisfying(422 U < -80, U > -90, M > np.quantile(M, 0.995), size=2423 )424 pth1 = export_image(sd, idx1, outdir, relpath=relpath)425 pth2 = export_image(sd, idx2, outdir, relpath=relpath)426 images[pth1] = dict(427 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]428 )429 images[pth2] = dict(430 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]431 )432 top_lines.append([(-85, 14_000), (-100, 15_700), (xpos, 15_300)])433 elif dataset == "BinarizedMNIST" and metadata["learning_rate"] == 1e-4:434 xpos = -160435 ypos = 4_900436 # Low logprob, low mem437 idx1, idx2 = random_satisfying(438 U < U_min, M < np.quantile(M, 0.5), size=2439 )440 pth1 = export_image(sd, idx1, outdir, relpath=relpath)441 pth2 = export_image(sd, idx2, outdir, relpath=relpath)442 images[pth1] = dict(443 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]444 )445 images[pth2] = dict(446 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]447 )448 # Low logprob, high mem449 idx1, idx2 = random_satisfying(450 U < U_min, M > np.quantile(M, 0.999), size=2451 )452 pth1 = export_image(sd, idx1, outdir, relpath=relpath)453 pth2 = export_image(sd, idx2, outdir, relpath=relpath)454 images[pth1] = dict(455 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]456 )457 images[pth2] = dict(458 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]459 )460 bottom_lines.append([(U_min - binwidth / 2, 1200), (U_min, 4_800)])461 ####462 ####463 xpos = -146464 # Med low log prob, low mem (1)465 idx1, idx2 = random_satisfying(466 U < -110, U > -120, M < np.quantile(M, 0.1), size=2467 )468 pth1 = export_image(sd, idx1, outdir, relpath=relpath)469 pth2 = export_image(sd, idx2, outdir, relpath=relpath)470 images[pth1] = dict(471 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]472 )473 images[pth2] = dict(474 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]475 )476 # Med low log prob, high mem (1)477 idx1, idx2 = random_satisfying(478 U < -110, U > -120, M > np.quantile(M, 0.999), size=2479 )480 pth1 = export_image(sd, idx1, outdir, relpath=relpath)481 pth2 = export_image(sd, idx2, outdir, relpath=relpath)482 images[pth1] = dict(483 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]484 )485 images[pth2] = dict(486 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]487 )488 bottom_lines.append([(-110 - binwidth / 2, 3800), (xpos, 4_800)])489 ###490 ###491 xpos = -132492 # Central log prob, low mem (1)493 idx1, idx2 = random_satisfying(494 U < -80, U > -90, M < np.quantile(M, 0.1), size=2495 )496 pth1 = export_image(sd, idx1, outdir, relpath=relpath)497 pth2 = export_image(sd, idx2, outdir, relpath=relpath)498 images[pth1] = dict(499 mem=False, x=xpos, y=ypos, yshift=0, U=U[idx1], M=M[idx1]500 )501 images[pth2] = dict(502 mem=False, x=xpos, y=ypos, yshift=1, U=U[idx2], M=M[idx2]503 )504 # Central log prob, high mem (1)505 idx1, idx2 = random_satisfying(506 U < -80, U > -90, M > np.quantile(M, 0.995), size=2507 )508 pth1 = export_image(sd, idx1, outdir, relpath=relpath)509 pth2 = export_image(sd, idx2, outdir, relpath=relpath)510 images[pth1] = dict(511 mem=True, x=xpos, y=ypos, yshift=2, U=U[idx1], M=M[idx1]512 )513 images[pth2] = dict(514 mem=True, x=xpos, y=ypos, yshift=3, U=U[idx2], M=M[idx2]515 )516 top_lines.append([(-85, 13_800), (-110, 14_700), (xpos, 14_500)])517 else:518 raise NotImplementedError519 tex = make_tex(520 midpoints,521 heights_reg,522 heights_high,523 binwidth,...
main-modified-for-bulk.py
Source:main-modified-for-bulk.py
...7import image_effects.hsv_color_space as effects_hsv8import image_effects.luv_color_space as effects_luv9import image_effects.rgb_color_space as effects_rgb10count = 011def export_image(Image_Raw_Data) :12 print("[ ! ] Exporting as an Image..")13 img_export_name = f"exported_image_files/mona_lisa-{count}.jpg"14 open_cv.imwrite(img_export_name, Image_Raw_Data)15# img_raw_data = open_cv.imread("./c.jpg")16 17count=count+118export_image(effects_rgb.bgr_2_rgb(open_cv.imread("./c.jpg")))19count=count+120export_image(effects_rgb.bgr_2_rgba(open_cv.imread("./c.jpg")))21count=count+122export_image(effects_rgb.grayscale_image(open_cv.imread("./c.jpg")))23count=count+124export_image(effects_rgb.red_channel_only(open_cv.imread("./c.jpg")))25count=count+126export_image(effects_rgb.green_channel_only(open_cv.imread("./c.jpg")))27count=count+128export_image(effects_rgb.blue_channel_only(open_cv.imread("./c.jpg")))29threshold = int(input("\n[ ? ] Threshold Value : "))30count=count+131export_image(effects_rgb.custom_rgb_threshold(open_cv.imread("./c.jpg"),threshold))32r_threshold = int(input("\n[ ? ] Red Threshold Value : "))33g_threshold = int(input("[ ? ] Green Threshold Value : "))34b_threshold = int(input("[ ? ] Blue Threshold Value : "))35count=count+136export_image(effects_rgb.custom_rgb_gain_or_loss(open_cv.imread("./c.jpg"),r_threshold,37 g_threshold,b_threshold))38count=count+139export_image(effects_hsv.bgr_2_hsv(open_cv.imread("./c.jpg")))40count=count+141export_image(effects_hsv.hue_channel_only(open_cv.imread("./c.jpg")))42count=count+143export_image(effects_hsv.saturation_channel_only(open_cv.imread("./c.jpg")))44count=count+145export_image(effects_hsv.value_channel_only(open_cv.imread("./c.jpg")))46count=count+147export_image(effects_luv.bgr_2_luv(open_cv.imread("./c.jpg")))48count=count+149export_image(effects_luv.luminance_component_only(open_cv.imread("./c.jpg")))50count=count+151export_image(effects_luv.u_chroma_component_only(open_cv.imread("./c.jpg")))52count=count+153export_image(effects_luv.v_chroma_component_only(open_cv.imread("./c.jpg")))54count=count+155export_image(edge_detect.sobel_horizontal_edge_detect_algo(open_cv.imread("./c.jpg")))56count=count+157export_image(edge_detect.sobel_vertical_edge_detect_algo(open_cv.imread("./c.jpg")))58count=count+159export_image(edge_detect.sobel_bothaxis_bitws_or_edge_detect_algo(open_cv.imread("./c.jpg")))60count=count+161export_image(edge_detect.sobel_bothaxis_bitws_and_edge_detect_algo(open_cv.imread("./c.jpg")))62count=count+163export_image(edge_detect.sobel_bothaxis_bitws_xor_edge_detect_algo(open_cv.imread("./c.jpg")))64count=count+165export_image(edge_detect.sobel_bothaxis_bitws_not_edge_detect_algo(open_cv.imread("./c.jpg")))66count=count+167export_image(edge_detect.laplacian_edge_detect_algo(open_cv.imread("./c.jpg")))68threshold_a = int(input("\n[ ? ] Threshold A Value : "))69threshold_b = int(input("[ ? ] Threshold B Value : "))70count=count+1...
export_to_image_editor.py
Source:export_to_image_editor.py
1import bpy2import os3from bpy.types import Operator4from bpy_extras.io_utils import ImportHelper5class COATER_OT_image_editor_export(Operator):6 '''Exports the selected image paint canvas to the image editor defined in Blender's preferences'''7 bl_idname = "coater.image_editor_export"8 bl_label = "Export to External Image Editor"9 bl_description = "Exports the select image layer to the image editor defined in Blender's preferences"10 @ classmethod11 def poll(cls, context):12 return context.scene.coater_layers13 False14 def execute(self, context):15 export_image = context.scene.tool_settings.image_paint.canvas16 if export_image != None:17 if export_image.packed_file == None:18 if export_image.file_format == '' or export_image.filepath == '':19 if export_image.is_dirty:20 export_image.save()21 else:22 self.report({'ERROR'}, "Export image has no defined filepath.")23 else:24 self.report({'ERROR'}, "Export image can't be packed to export to an external image editor.")25 bpy.ops.image.external_edit(filepath=export_image.filepath)...
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