def buildDescriptorIndex():
"""
Build index and search.
"""
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
warper = faceEngine.createFaceWarper()
extractor = faceEngine.createFaceDescriptorEstimator()
descriptorsBatch = faceEngine.createFaceDescriptorFactory(
).generateDescriptorsBatch(2)
for image in (EXAMPLE_O, EXAMPLE_1):
vlImage = VLImage.load(filename=image)
faceDetection = detector.detectOne(vlImage)
warp = warper.warp(faceDetection)
faceDescriptor = extractor.estimate(warp.warpedImage)
descriptorsBatch.append(faceDescriptor)
indexBuilder = faceEngine.createIndexBuilder()
indexBuilder.appendBatch(descriptorsBatch)
pprint.pprint(f"index buf size: {indexBuilder.bufSize}")
index = indexBuilder.buildIndex()
pprint.pprint(index[0])
result = index.search(faceDescriptor, 1)
pprint.pprint(f"result: {result}")
async def asyncEstimateBasicAttributes():
"""
Async estimate basic attributes.
"""
image = VLImage.load(filename=EXAMPLE_SEVERAL_FACES)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetections = detector.detect([image])[0]
warper = faceEngine.createFaceWarper()
warps = [warper.warp(faceDetection) for faceDetection in faceDetections]
basicAttributesEstimator = faceEngine.createBasicAttributesEstimator()
basicAttributes = await basicAttributesEstimator.estimate(
warps[0].warpedImage,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True,
asyncEstimate=True)
pprint.pprint(basicAttributes.asDict())
task1 = basicAttributesEstimator.estimate(warps[0].warpedImage,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True,
asyncEstimate=True)
task2 = basicAttributesEstimator.estimate(warps[0].warpedImage,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True,
asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get().asDict())
def estimateGazeDirection():
"""
Estimate gaze direction.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetection = detector.detectOne(image, detect68Landmarks=True)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
landMarks5Transformation = warper.makeWarpTransformationWithLandmarks(faceDetection, "L5")
gazeEstimator = faceEngine.createGazeEstimator()
warpWithLandmarks5 = WarpWithLandmarks5(warp, landMarks5Transformation)
pprint.pprint(gazeEstimator.estimate(warpWithLandmarks5).asDict())
faceDetection2 = detector.detectOne(VLImage.load(filename=EXAMPLE_1), detect68Landmarks=True)
warp2 = warper.warp(faceDetection2)
landMarks5Transformation2 = warper.makeWarpTransformationWithLandmarks(faceDetection2, "L5")
warpWithLandmarks5List = [
WarpWithLandmarks5(warp, landMarks5Transformation),
WarpWithLandmarks5(warp2, landMarks5Transformation2),
]
estimations = gazeEstimator.estimateBatch(warpWithLandmarks5List)
pprint.pprint([estimation.asDict() for estimation in estimations])
def matchFacesFromImages():
"""
Match faces from images.
"""
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
extractor = faceEngine.createFaceDescriptorEstimator()
warper = faceEngine.createFaceWarper()
matcher = faceEngine.createFaceMatcher()
image1 = VLImage.load(filename=EXAMPLE_O)
faceDetection1 = detector.detectOne(image1)
warp1 = warper.warp(faceDetection1)
descriptor1 = extractor.estimate(warp1.warpedImage)
image2 = VLImage.load(filename=EXAMPLE_1)
faceDetection2 = detector.detectOne(image2)
warp2 = warper.warp(faceDetection2)
descriptor2 = extractor.estimate(warp2.warpedImage)
batch, _ = extractor.estimateDescriptorsBatch(
[warp1.warpedImage, warp2.warpedImage])
print(matcher.match(descriptor1, descriptor2))
print(matcher.match(descriptor1, batch))
print(matcher.match(descriptor1, [descriptor2, descriptor1]))
async def asyncEstimateMedicalMask():
"""
Async medical mask estimation example
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
medicalMaskEstimator = faceEngine.createMaskEstimator()
# Estimate from detection
pprint.pprint(medicalMaskEstimator.estimate(faceDetection).asDict())
# Estimate from wrap
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
mask = await medicalMaskEstimator.estimate(warp.warpedImage,
asyncEstimate=True)
pprint.pprint(mask.asDict())
warp2 = warper.warp(detector.detectOne(VLImage.load(filename=EXAMPLE_1)))
task1 = medicalMaskEstimator.estimate(warp.warpedImage, asyncEstimate=True)
task2 = medicalMaskEstimator.estimate(warp2.warpedImage,
asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get())
def estimateRedEye():
"""
Red-eye estimation example.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
landMarks5Transformation = warper.makeWarpTransformationWithLandmarks(
faceDetection, "L5")
redEyeEstimator = faceEngine.createRedEyeEstimator()
warpWithLandmarks = WarpWithLandmarks5(warp, landMarks5Transformation)
pprint.pprint(redEyeEstimator.estimate(warpWithLandmarks).asDict())
image2 = VLImage.load(filename=EXAMPLE_1)
faceDetection2 = detector.detectOne(image2)
warp2 = warper.warp(faceDetection2)
landMarks5Transformation2 = warper.makeWarpTransformationWithLandmarks(
faceDetection2, "L5")
warpWithLandmarksList = [
WarpWithLandmarks5(warp, landMarks5Transformation),
WarpWithLandmarks5(warp2, landMarks5Transformation2),
]
estimations = redEyeEstimator.estimateBatch(warpWithLandmarksList)
pprint.pprint([estimation.asDict() for estimation in estimations])
def estimateBasicAttributes():
"""
Estimate emotion from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_SEVERAL_FACES)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetections = detector.detect([image])[0]
warper = faceEngine.createFaceWarper()
warps = [warper.warp(faceDetection) for faceDetection in faceDetections]
basicAttributesEstimator = faceEngine.createBasicAttributesEstimator()
pprint.pprint(
basicAttributesEstimator.estimate(warps[0].warpedImage,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True).asDict())
pprint.pprint(
basicAttributesEstimator.estimateBasicAttributesBatch(
warps,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True))
pprint.pprint(
basicAttributesEstimator.estimateBasicAttributesBatch(
warps,
estimateAge=True,
estimateGender=True,
estimateEthnicity=True,
aggregate=True))
async def asyncEstimateEyes():
"""
Async eyes estimation example.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
landMarks5Transformation = warper.makeWarpTransformationWithLandmarks(
faceDetection, "L5")
eyesEstimator = faceEngine.createEyeEstimator()
warpWithLandmarks = WarpWithLandmarks(warp, landMarks5Transformation)
eyes = await eyesEstimator.estimate(warpWithLandmarks, asyncEstimate=True)
pprint.pprint(eyes.asDict())
image2 = VLImage.load(filename=EXAMPLE_1)
faceDetection2 = detector.detectOne(image2)
warp2 = warper.warp(faceDetection2)
landMarks5Transformation2 = warper.makeWarpTransformationWithLandmarks(
faceDetection2, "L5")
task1 = eyesEstimator.estimateBatch(
[WarpWithLandmarks(warp, landMarks5Transformation)],
asyncEstimate=True)
task2 = eyesEstimator.estimateBatch(
[WarpWithLandmarks(warp2, landMarks5Transformation2)],
asyncEstimate=True)
for task in (task1, task2):
estimations = task.get()
pprint.pprint([estimation.asDict() for estimation in estimations])
def createWarp():
"""
Create face warp from detection.
"""
faceEngine = VLFaceEngine()
image = VLImage.load(filename=EXAMPLE_O)
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
pprint.pprint(warp.warpedImage.rect)
def estimateGlasses():
"""
Create warp to detection.
"""
image = VLImage.load(filename=EXAMPLE_3)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
glassesEstimator = faceEngine.createGlassesEstimator()
pprint.pprint(glassesEstimator.estimate(warp.warpedImage).asDict())
def estimateCredibility():
"""
Estimate credibility of a person
"""
image = VLImage.load(filename=EXAMPLE_1)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
credibilityEstimator = faceEngine.createCredibilityEstimator()
pprint.pprint(credibilityEstimator.estimate(warp.warpedImage).asDict())
def estimateEmotion():
"""
Estimate emotion from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
emotionEstimator = faceEngine.createEmotionEstimator()
pprint.pprint(emotionEstimator.estimate(warp.warpedImage).asDict())
def estimateHeadwear():
"""
Estimate headwear from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
eyebrowEstimator = faceEngine.createHeadwearEstimator()
pprint.pprint(eyebrowEstimator.estimate(warp.warpedImage).asDict())
def estimateImageColorType():
"""
Estimate image color type from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
imageColorTypeEstimator = faceEngine.createImageColorTypeEstimator()
pprint.pprint(imageColorTypeEstimator.estimate(warp.warpedImage).asDict())
def estimateWarpQuality():
"""
Create warp from detection.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
qualityEstimator = faceEngine.createWarpQualityEstimator()
pprint.pprint(qualityEstimator.estimate(warp.warpedImage).asDict())
def createWarp():
"""
Create warp from detection.
"""
image = VLImage.load(url="https://cdn1.savepice.ru/uploads/2019/4/15/194734af15c4fcd06dec6db86bbeb7cd-full.jpg")
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
pprint.pprint(warp.warpedImage.rect)
cv2.imshow("Wapred image", warp.warpedImage.asNPArray())
cv2.imshow("Original image", image.asNPArray())
cv2.waitKey(0)
cv2.destroyAllWindows()
async def asyncEstimateEmotion():
"""
Async estimate emotion from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
emotionEstimator = faceEngine.createEmotionEstimator()
emotions = await emotionEstimator.estimate(warp.warpedImage, asyncEstimate=True)
pprint.pprint(emotions.asDict())
task = emotionEstimator.estimate(warp.warpedImage, asyncEstimate=True)
pprint.pprint(task.get().asDict())
def estimateGazeDirection():
"""
Estimate gaze direction.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetection = detector.detectOne(image, detect68Landmarks=True)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
landMarks5Transformation = warper.makeWarpTransformationWithLandmarks(
faceDetection, "L5")
gazeEstimator = faceEngine.createGazeEstimator()
pprint.pprint(
gazeEstimator.estimate(landMarks5Transformation, warp).asDict())
def estimateEyes():
"""
Estimate emotion from a warped image.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V1)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
landMarks5Transformation = warper.makeWarpTransformationWithLandmarks(
faceDetection, "L5")
eyesEstimator = faceEngine.createEyeEstimator()
pprint.pprint(
eyesEstimator.estimate(landMarks5Transformation,
warp.warpedImage).asDict())
async def asyncEstimateDescriptor():
"""
Async estimate face descriptor.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
extractor = faceEngine.createFaceDescriptorEstimator()
pprint.pprint(await extractor.estimateDescriptorsBatch(
[warp.warpedImage, warp.warpedImage], asyncEstimate=True))
# run tasks and get results
task1 = extractor.estimate(warp.warpedImage, asyncEstimate=True)
task2 = extractor.estimate(warp.warpedImage, asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get().asDict())
async def asyncEstimateMouthState():
"""
Async mouth state estimation example.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
mouthEstimator = faceEngine.createMouthEstimator()
mouth = await mouthEstimator.estimate(warp.warpedImage, asyncEstimate=True)
pprint.pprint(mouth.asDict())
task1 = mouthEstimator.estimate(warp.warpedImage, asyncEstimate=True)
task2 = mouthEstimator.estimate(warp.warpedImage, asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get())
async def asyncEstimateCredibility():
"""
Async estimate credibility of a person
"""
image = VLImage.load(filename=EXAMPLE_1)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
credibilityEstimator = faceEngine.createCredibilityEstimator()
estimation = await credibilityEstimator.estimate(warp.warpedImage,
asyncEstimate=True)
pprint.pprint(estimation.asDict())
task1 = credibilityEstimator.estimate(warp.warpedImage, asyncEstimate=True)
task2 = credibilityEstimator.estimate(warp.warpedImage, asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get().asDict())
def estimateDescriptor():
"""
Estimate face descriptor.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
extractor = faceEngine.createFaceDescriptorEstimator()
pprint.pprint(extractor.estimate(warp.warpedImage))
pprint.pprint(
extractor.estimateDescriptorsBatch(
[warp.warpedImage, warp.warpedImage]))
batch, aggregateDescriptor = extractor.estimateDescriptorsBatch(
[warp.warpedImage, warp.warpedImage], aggregate=True)
pprint.pprint(batch)
pprint.pprint(aggregateDescriptor)
def estimateMedicalMask():
"""
Medical mask estimation example
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
medicalMaskEstimator = faceEngine.createMaskEstimator()
# Estimate from detection
pprint.pprint(medicalMaskEstimator.estimate(faceDetection).asDict())
# Estimate from wrap
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
pprint.pprint(medicalMaskEstimator.estimate(warp.warpedImage).asDict())
warp2 = warper.warp(detector.detectOne(VLImage.load(filename=EXAMPLE_1)))
pprint.pprint(medicalMaskEstimator.estimateBatch([warp, warp2]))
async def estimateGlassesAsync():
"""
Example of an async glasses estimation.
"""
image = VLImage.load(filename=EXAMPLE_3)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
# async estimation
glassesEstimator = faceEngine.createGlassesEstimator()
pprint.pprint((await
glassesEstimator.estimate(warp.warpedImage,
asyncEstimate=True)).asDict())
# run tasks and get results
task1 = glassesEstimator.estimate(warp.warpedImage, asyncEstimate=True)
task2 = glassesEstimator.estimate(warp.warpedImage, asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get().asDict())
async def estimateGazeDirectionAsync():
"""
Example of an async gaze direction estimation.
"""
image = VLImage.load(filename=EXAMPLE_O)
faceEngine = VLFaceEngine()
detector = faceEngine.createFaceDetector(DetectorType.FACE_DET_V3)
faceDetection = detector.detectOne(image, detect68Landmarks=True)
warper = faceEngine.createFaceWarper()
warp = warper.warp(faceDetection)
landMarks5Transformation = warper.makeWarpTransformationWithLandmarks(faceDetection, "L5")
gazeEstimator = faceEngine.createGazeEstimator()
warpWithLandmarks5 = WarpWithLandmarks5(warp, landMarks5Transformation)
# async estimation
pprint.pprint((await gazeEstimator.estimate(warpWithLandmarks5, asyncEstimate=True)).asDict())
# run tasks and get results
task1 = gazeEstimator.estimate(warpWithLandmarks5, asyncEstimate=True)
task2 = gazeEstimator.estimate(warpWithLandmarks5, asyncEstimate=True)
for task in (task1, task2):
pprint.pprint(task.get().asDict())