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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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