arrayfire-python/_modules/arrayfire/vision.html at gh-pages · arrayfire/arrayfire-python

Repository navigation

History

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

<!DOCTYPE html>

<html>

<head>

<title>arrayfire.vision — ArrayFire Python documentation</title>

</head><body>

<h1>Source code for arrayfire.vision</h1><div class="highlight"><pre>

#######################################################

#

# This file is distributed under 3-clause BSD license.

# The complete license agreement can be obtained at:

# http://arrayfire.com/licenses/BSD-3-Clause

########################################################

"""

Computer vision functions (FAST, ORB, etc)

"""

from .library import *

from .array import *

from .features import *

<div class="viewcode-block" id="fast"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.fast">[docs]</a>def fast(image, threshold=20.0, arc_length=9, non_max=True, feature_ratio=0.05, edge=3):

"""

FAST feature detector.

Parameters

----------

image : af.Array

A 2D array representing an image.

threshold : scalar. optional. default: 20.0.

FAST threshold for which a pixel of the circle around a central pixel is consdered.

arc_length : scalar. optional. default: 9

The minimum length of arc length to be considered. Max length should be 16.

non_max : Boolean. optional. default: True

A boolean flag specifying if non max suppression has to be performed.

feature_ratio : scalar. optional. default: 0.05 (5%)

Specifies the maximum ratio of features to pixels in the image.

edge : scalar. optional. default: 3.

Specifies the number of edge rows and columns to be ignored.

Returns

---------

features : af.Features()

Contains the location and score. Orientation and size are not computed.

"""

out = Features()

image.arr, c_float_t(threshold), c_uint_t(arc_length), non_max,

c_float_t(feature_ratio), c_uint_t(edge)))

return out</div>

<div class="viewcode-block" id="harris"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.harris">[docs]</a>def harris(image, max_corners=500, min_response=1E5, sigma=1.0, block_size=0, k_thr=0.04):

"""

Harris corner detector.

Parameters

----------

image : af.Array

A 2D array specifying an image.

max_corners : scalar. optional. default: 500.

Specifies the maximum number of corners to be calculated.

min_response : scalar. optional. default: 1E5

Specifies the cutoff score for a corner to be considered

sigma : scalar. optional. default: 1.0

- Specifies the standard deviation of a circular window.

- Only used when block_size == 0. Must be >= 0.5 and <= 5.0.

block_size : scalar. optional. default: 0

Specifies the window size.

k_thr : scalar. optional. default: 0.04

Harris constant. must be >= 0.01

Returns

---------

features : af.Features()

Contains the location and score. Orientation and size are not computed.

Note

------

The covariation matrix will be square when `block_size` is used and circular when `sigma` is used.

"""

out = Features()

image.arr, c_uint_t(max_corners), c_float_t(min_response),

c_float_t(sigma), c_uint_t(block_size), c_float_t(k_thr)))

return out</div>

<div class="viewcode-block" id="orb"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.orb">[docs]</a>def orb(image, threshold=20.0, max_features=400, scale = 1.5, num_levels = 4, blur_image = False):

"""

ORB Feature descriptor.

Parameters

----------

image : af.Array

A 2D array representing an image.

threshold : scalar. optional. default: 20.0.

FAST threshold for which a pixel of the circle around a central pixel is consdered.

max_features : scalar. optional. default: 400.

Specifies the maximum number of features to be considered.

scale : scalar. optional. default: 1.5.

Specifies the factor by which images are down scaled at each level.

num_levles : scalar. optional. default: 4.

Specifies the number of levels used in the image pyramid.

blur_image : Boolean. optional. default: False.

Flag specifying if the input has to be blurred before computing descriptors.

A gaussian filter with sigma = 2 is applied if True.

Returns

---------

(features, descriptor) : tuple of (af.Features(), af.Array)

- descriptor is an af.Array of size N x 8

"""

feat = Features()

desc = Array()

safe_call(backend.get().af_orb(c_pointer(feat.feat), c_pointer(desc.arr), image.arr,

c_float_t(threshold), c_uint_t(max_features),

c_float_t(scale), c_uint_t(num_levels), blur_image))

return feat, desc</div>

<div class="viewcode-block" id="hamming_matcher"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.hamming_matcher">[docs]</a>def hamming_matcher(query, database, dim = 0, num_nearest = 1):

"""

Hamming distance matcher.

Parameters

-----------

query : af.Array

A query feature descriptor

database : af.Array

A multi dimensional array containing the feature descriptor database.

dim : scalar. optional. default: 0.

Specifies the dimension along which feature descriptor lies.

num_nearest: scalar. optional. default: 1.

Specifies the number of nearest neighbors to find.

Returns

---------

(location, distance): tuple of af.Array

location and distances of closest matches.

"""

index = Array()

dist = Array()

safe_call(backend.get().af_hamming_matcher(c_pointer(index.arr), c_pointer(dist.arr),

query.arr, database.arr,

c_dim_t(dim), c_dim_t(num_nearest)))

return index, dist</div>

<div class="viewcode-block" id="nearest_neighbour"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.nearest_neighbour">[docs]</a>def nearest_neighbour(query, database, dim = 0, num_nearest = 1, match_type=MATCH.SSD):

"""

Nearest Neighbour matcher.

Parameters

-----------

query : af.Array

A query feature descriptor

database : af.Array

A multi dimensional array containing the feature descriptor database.

dim : scalar. optional. default: 0.

Specifies the dimension along which feature descriptor lies.

num_nearest: scalar. optional. default: 1.

Specifies the number of nearest neighbors to find.

match_type: optional: af.MATCH. default: af.MATCH.SSD

Specifies the match function metric.

Returns

---------

(location, distance): tuple of af.Array

location and distances of closest matches.

"""

index = Array()

dist = Array()

safe_call(backend.get().af_nearest_neighbour(c_pointer(index.arr), c_pointer(dist.arr),

match_type.value))

return index, dist</div>

<div class="viewcode-block" id="match_template"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.match_template">[docs]</a>def match_template(image, template, match_type = MATCH.SAD):

"""

Find the closest match of a template in an image.

Parameters

----------

image : af.Array

A multi dimensional array specifying an image or batch of images.

template : af.Array

A multi dimensional array specifying a template or batch of templates.

match_type: optional: af.MATCH. default: af.MATCH.SAD

Specifies the match function metric.

Returns

--------

out : af.Array

An array containing the score of the match at each pixel.

"""

out = Array()

image.arr, template.arr,

match_type.value))

return out</div>

<div class="viewcode-block" id="susan"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.susan">[docs]</a>def susan(image, radius=3, diff_thr=32, geom_thr=10, feature_ratio=0.05, edge=3):

"""

SUSAN corner detector.

Parameters

----------

image : af.Array

A 2D array specifying an image.

radius : scalar. optional. default: 500.

Specifies the radius of each pixel neighborhood.

diff_thr : scalar. optional. default: 1E5

Specifies the intensity difference threshold.

geom_thr : scalar. optional. default: 1.0

Specifies the geometric threshold.

feature_ratio : scalar. optional. default: 0.05 (5%)

Specifies the ratio of corners found to number of pixels.

edge : scalar. optional. default: 3

Specifies the number of edge rows and columns that are ignored.

Returns

---------

features : af.Features()

Contains the location and score. Orientation and size are not computed.

"""

out = Features()

image.arr, c_uint_t(radius), c_float_t(diff_thr),

c_float_t(geom_thr), c_float_t(feature_ratio),

c_uint_t(edge)))

return out</div>

<div class="viewcode-block" id="dog"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.dog">[docs]</a>def dog(image, radius1, radius2):

"""

Difference of gaussians.

Parameters

----------

image : af.Array

A 2D array specifying an image.

radius1 : scalar.

The radius of first gaussian kernel.

radius2 : scalar.

The radius of second gaussian kernel.

Returns

--------

out : af.Array

A multi dimensional array containing the difference of gaussians.

Note

------

The sigma values are calculated to be 0.25 * radius.

"""

out = Array()

image.arr, radius1, radius2))

return out</div>

<div class="viewcode-block" id="sift"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.sift">[docs]</a>def sift(image, num_layers=3, contrast_threshold=0.04, edge_threshold=10.0, initial_sigma = 1.6,

double_input = True, intensity_scale = 0.00390625, feature_ratio = 0.05):

"""

SIFT feature detector and descriptor.

Parameters

----------

image : af.Array

A 2D array representing an image

num_layers : optional: integer. Default: 3

Number of layers per octave. The number of octaves is calculated internally.

contrast_threshold : optional: float. Default: 0.04

Threshold used to filter out features that have low contrast.

edge_threshold : optional: float. Default: 10.0

Threshold used to filter out features that are too edge-like.

initial_sigma : optional: float. Default: 1.6

The sigma value used to filter the input image at the first octave.

double_input : optional: bool. Default: True

If True, the input image will be scaled to double the size for the first octave.

intensity_scale : optional: float. Default: 1.0/255

The inverse of the difference between maximum and minimum intensity values.

feature_ratio : optional: float. Default: 0.05

Specifies the maximum number of features to detect as a ratio of image pixels.

Returns

--------

(features, descriptor) : tuple of (af.Features(), af.Array)

- descriptor is an af.Array of size N x 128

"""

feat = Features()

desc = Array()

safe_call(backend.get().af_sift(c_pointer(feat.feat), c_pointer(desc.arr),

image.arr, num_layers, c_float_t(contrast_threshold), c_float_t(edge_threshold),

c_float_t(initial_sigma), double_input, c_float_t(intensity_scale), c_float_t(feature_ratio)))

return (feat, desc)</div>

<div class="viewcode-block" id="gloh"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.gloh">[docs]</a>def gloh(image, num_layers=3, contrast_threshold=0.04, edge_threshold=10.0, initial_sigma = 1.6,

"""

GLOH feature detector and descriptor.

Parameters

----------

image : af.Array

A 2D array representing an image

num_layers : optional: integer. Default: 3

Number of layers per octave. The number of octaves is calculated internally.

contrast_threshold : optional: float. Default: 0.04

Threshold used to filter out features that have low contrast.

edge_threshold : optional: float. Default: 10.0

Threshold used to filter out features that are too edge-like.

initial_sigma : optional: float. Default: 1.6

The sigma value used to filter the input image at the first octave.

double_input : optional: bool. Default: True

If True, the input image will be scaled to double the size for the first octave.

intensity_scale : optional: float. Default: 1.0/255

The inverse of the difference between maximum and minimum intensity values.

feature_ratio : optional: float. Default: 0.05

Specifies the maximum number of features to detect as a ratio of image pixels.

Returns

--------

(features, descriptor) : tuple of (af.Features(), af.Array)

- descriptor is an af.Array of size N x 272

"""

feat = Features()

desc = Array()

safe_call(backend.get().af_gloh(c_pointer(feat.feat), c_pointer(desc.arr),

c_float_t(edge_threshold), c_float_t(initial_sigma),

double_input, c_float_t(intensity_scale),

c_float_t(feature_ratio)))

return (feat, desc)</div>

<div class="viewcode-block" id="homography"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.homography">[docs]</a>def homography(x_src, y_src, x_dst, y_dst, htype = HOMOGRAPHY.RANSAC,

ransac_threshold = 3.0, iters = 1000, out_type = Dtype.f32):

"""

Homography estimation

Parameters

----------

x_src : af.Array

A list of x co-ordinates of the source points.

y_src : af.Array

A list of y co-ordinates of the source points.

x_dst : af.Array

A list of x co-ordinates of the destination points.

y_dst : af.Array

A list of y co-ordinates of the destination points.

htype : optional: af.HOMOGRAPHY. Default: HOMOGRAPHY.RANSAC

htype can be one of

- HOMOGRAPHY.RANSAC: RANdom SAmple Consensus will be used to evaluate quality.

- HOMOGRAPHY.LMEDS : Least MEDian of Squares is used to evaluate quality.

ransac_threshold : optional: scalar. Default: 3.0

If `htype` is HOMOGRAPHY.RANSAC, it specifies the L2-distance threshold for inliers.

out_type : optional. af.Dtype. Default: Dtype.f32.

Specifies the output data type.

Returns

-------

(H, inliers) : A tuple of (af.Array, integer)

"""

H = Array()

inliers = c_int_t(0)

safe_call(backend.get().af_homography(c_pointer(H), c_pointer(inliers),

x_src.arr, y_src.arr, x_dst.arr, y_dst.arr,

htype.value, ransac_threshold, iters, out_type.value))

return (H, inliers)</div>

</pre></div>

</div>

</a>

<h1 class="logo"><a href="../../index.html">ArrayFire</a></h1>

<h3>Navigation</h3>

<h3>Related Topics</h3>

<ul>

<li><a href="../../index.html">Documentation overview</a><ul>

<li><a href="../index.html">Module code</a><ul>

</ul></li>

</ul>

</div>

<h3 id="searchlabel">Quick search</h3>

</form>

</div>

</div>

</div>

& <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>

</div>

</body>

</html>

FilesExpand file tree

vision.html

Latest commit

History

vision.html

File metadata and controls