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Small updates to Vision examples. #3227

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Mar 28, 2017
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Small updates to Vision examples. #3227

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33 changes: 16 additions & 17 deletions docs/vision-usage.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -84,15 +84,14 @@ You can call the detection method manually.
>>> features = [Feature(FeatureTypes.FACE_DETECTION, 5),
... Feature(FeatureTypes.LOGO_DETECTION, 3)]
>>> annotations = image.detect(features)
>>> len(annotations.faces)
>>> len(annotations)
2
>>> for face in annotations.faces:
... print(face.joy_likelihood)
0.94099093
0.54453093
>>> len(annotations.logos)
2
>>> for logo in annotations.logos:
>>> for face in annotations[0].faces:
... print(face.joy)
Likelihood.VERY_LIKELY
Likelihood.VERY_LIKELY
Likelihood.VERY_LIKELY
>>> for logo in annotations[0].logos:
... print(logo.description)
'google'
'github'
Expand Down Expand Up @@ -140,15 +139,15 @@ was detected.
>>> faces = image.detect_faces(limit=10)
>>> first_face = faces[0]
>>> first_face.landmarks.left_eye.landmark_type
<LandmarkTypes.LEFT_EYE: 'LEFT_EYE'>
LandmarkTypes.LEFT_EYE
>>> first_face.landmarks.left_eye.position.x_coordinate
1301.2404
>>> first_face.detection_confidence
0.9863683
>>> first_face.joy
<Likelihood.VERY_LIKELY: 'VERY_LIKELY'>
Likelihood.VERY_UNLIKELY
>>> first_face.anger
<Likelihood.VERY_UNLIKELY: 'VERY_UNLIKELY'>
Likelihood.VERY_UNLIKELY


***************
Expand Down Expand Up @@ -250,13 +249,13 @@ categorize the entire contents of the image under four categories.
... image = client.image(content=image_file.read())
>>> safe_search = image.detect_safe_search()
>>> safe_search.adult
<Likelihood.VERY_UNLIKELY: 'VERY_UNLIKELY'>
Likelihood.VERY_UNLIKELY
>>> safe_search.spoof
<Likelihood.POSSIBLE: 'POSSIBLE'>
Likelihood.POSSIBLE
>>> safe_search.medical
<Likelihood.VERY_LIKELY: 'VERY_LIKELY'>
Likelihood.VERY_LIKELY
>>> safe_search.violence
<Likelihood.LIKELY: 'LIKELY'>
Likelihood.LIKELY


**************
Expand Down Expand Up @@ -297,9 +296,9 @@ image and determine the dominant colors in the image.
>>> properties = image.detect_properties()
>>> colors = properties.colors
>>> first_color = colors[0]
>>> first_color.red
>>> first_color.color.red
244.0
>>> first_color.blue
>>> first_color.color.blue
134.0
>>> first_color.score
0.65519291
Expand Down