Face detection beta features by dizcology · Pull Request #1414 · GoogleCloudPlatform/python-docs-samples · GitHub

This PR adds samples for the following beta features:

face bounding boxes
face emotions
speech transcription

tested against the released client library 1.1.0.

Is there any way to use the videointelligence.enums object directly or pull this programmatically? How badly will this break if the enum changes in the future?

The simplest way is for the enums in videointelligence.enums to extend python's Enums class, which would allow us to map the enums back to their names. However the client libraries do not do that (yet).

I don't know of a clean way to pull the names programmatically, and as it stands now the sample code will break in these possible ways:

the order of the enums changes (unlikely), in which case the printout of the sample will be incorrect.
more enums are added (possible) and the data we use for testing receive those added enums in the API responses, in which case we would get an IndexError in our testing.
(worst) more enums are added, but the testing data does not receive them, so we don't notice it is broken. Users relying on the same tuple to map enums to names might get IndexError with their data.

The best long term solution is to simply extend Enums. When that happens we will need to come back and update these samples.

+1 Programmatically, this keeps showing up in Python samples and, as a user, it seems really frustrating that Python makes you do this... you get the proto's enum value index for enums via the library but not the enum value name?

I don't think any of the other languages have this problem?

(Make this better! 😭)

Without fixing the client library generation tool, this is the closest I could come up with:

from google.cloud import videointelligence_v1p1beta1 as videointelligence
emotion_label = {value:name for name, value in vars(videointelligence.enums.Emotion).iteritems() if not name.startswith('__')}

I would rather not have this as is in a code snippet - but perhaps abstracted away as a helper function? The reader of the sample would not be able to see the full list of enums this way.

To be clear, I wasn't thinking about fixing this in the code sample, but rather in google-cloud-core

IMO The code sample should use an inline, literal List with the values. The display name of the enum value should be printed by getting it by index. As we do today.

I'll move this to a thread on google-cloud-core 😄

Code in this PR regarding Enum LGTM

Looks pretty good, mostly stuff for discussion

Should we make another demo project to hold public files that can be used across languages?

... face detection, face emotions, video transcription

done.

Is this still required? I thought we had them pull that?

done.

Can they use a local file too or just a GCS file?

They can. The request needs to be only slightly different, such as https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/video/cloud-client/analyze/analyze.py#L134

Do we only need to provide local file snippets, no GCS snippets?

Here we are showing only GCS snippets.

OH, do Python samples all use path for GCS? I figured that was for a local file

It should be uri, based on existing samples, eg: https://cloud.google.com/vision/docs/detecting-labels#vision-label-detection-python

def detect_labels(path):
    """Detects labels in the file."""
    client = vision.ImageAnnotatorClient()

def detect_labels_uri(uri):
    """Detects labels in the file located in Google Cloud Storage or on the
    Web."""
    client = vision.ImageAnnotatorClient()

Let's make this change across the board to all 3 samples

you are right - it should be uri, or rather gcs_uri (since currently the API only supports that). fixed.

This comment confused me a little, especially with the possibility of segments above.
"There are typically many frames for each face, since a face is in a segment of time" (I don't know if that helps clarify it or just makes it more confusing)

Could a face appear in multiple segments?

Like:
Face 1: (Segment 0:00 --> 0:05, 0:15 --> 0:25) then the face would be in all the frames between those segments.

I slightly reworded this comment, please take a look.

For face detection, each face indeed is a segment. If the same person appears in two segments of the same video, two separate faces will be returned, each with its own segment.

Should we merely print out the results without sorting based on score? Will help simplify the code, if we were going across the whole segment to determine the most likely emotion across all frames I think that would be cool, but not as simple.

Also by printing out the time_offset, isn't it always the same, since we only get the one frame for each face?

Here actually we are sorting by time_offset - an earlier version of of the API did not sort the frames by it. Emotion is a frame level output since detecting the change of emotions is useful.

The output might look like this:

	30.56s: INTEREST      (0.606)
	30.60s: INTEREST      (0.582)
	30.63s: INTEREST      (0.561)
	30.66s: AMUSEMENT     (0.559)
	30.70s: AMUSEMENT     (0.556)
	30.73s: AMUSEMENT     (0.549)
	30.76s: CONTENTMENT   (0.564)
	30.80s: CONTENTMENT   (0.634)
	30.83s: CONTENTMENT   (0.688)

One line?

done.

Can you add a comment that notes you are only pulling out the first result or is there only one result?

done.

Looks great (1 tiny fix)

Each detected face
(I like the rewording)

Non-trivial logic alert!

@nnegrey I think I misunderstood your comment about this part - indeed I was sorting by emotion scores. The reason being that the API returns one score for each emotion, and here I am trying to show only the one that scores the highest.

added a comment here to clarify what I was doing. please take a look.

This line is really impressive:

multiple return values
sorting
list comprehension
lambda
-1 index used

Still somewhat impressive!

sorting
lambda expression
-1

Simplified. Thanks for taking the time explaining the issues to me!

The other 2 functions use gcs_uri but this uses input_uri

We should make them consistent (and I'd TAL at whatever variable we use in all of the other Vision samples and use that for consistency)

…s-samples#1414)

googlebot added the cla: yes This human has signed the Contributor License Agreement. label Mar 19, 2018

dizcology requested review from andrewsg, beccasaurus and nnegrey March 19, 2018 20:10

dizcology added 6 commits March 20, 2018 13:18

add beta_snippets.py

c610dad

add beta_snippets for face detection features

4bed5f6

add beta_snippets test

c24243a

correct test names

69071f9

update client library version

3c1800b

update client library version

95a0b50

dizcology force-pushed the face_detection branch from 679b6a8 to 95a0b50 Compare March 20, 2018 20:58

dizcology added 2 commits March 20, 2018 14:49

add speech transcription

8d633ef

flake

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andrewsg approved these changes Mar 21, 2018

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nnegrey reviewed Mar 23, 2018

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address review comments

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nnegrey approved these changes Mar 26, 2018

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fix missing word

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beccasaurus reviewed Mar 26, 2018

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dizcology added 4 commits March 26, 2018 13:51

add comment

8c0ac58

rename path to gcs_uri

ba30eec

process emotions differently

5a5da60

simpler code

c15c729

beccasaurus reviewed Mar 26, 2018

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dizcology added 2 commits March 26, 2018 16:31

use max instead of sorted

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update comment

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beccasaurus approved these changes Mar 26, 2018

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dizcology added 2 commits March 26, 2018 16:49

process emotions more simply

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flake

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beccasaurus reviewed Mar 27, 2018

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andrewsg merged commit 93124e0 into GoogleCloudPlatform:master Mar 27, 2018

busunkim96 pushed a commit to busunkim96/python-videointelligence that referenced this pull request May 20, 2020

Face detection beta features [(#1414)](GoogleCloudPlatform/python-doc…

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…s-samples#1414)

danoscarmike pushed a commit to googleapis/python-videointelligence that referenced this pull request Sep 30, 2020

Face detection beta features [(#1414)](GoogleCloudPlatform/python-doc…

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…s-samples#1414)

dizcology added a commit that referenced this pull request Sep 11, 2023

Face detection beta features [(#1414 )](#1414 )

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dizcology mentioned this pull request Sep 11, 2023

migrate code from googleapis/python-videointelligence #10605

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leahecole pushed a commit that referenced this pull request Sep 15, 2023

Face detection beta features [(#1414 )](#1414 )

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parthea pushed a commit to googleapis/google-cloud-python that referenced this pull request Sep 22, 2023

Face detection beta features [(#1414 )](GoogleCloudPlatform/python-doc…

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…s-samples#1414)

+                  # include_bounding_boxes must be set to True for include_emotions
+                  # to work.
+                  config = videointelligence.types.FaceConfig(
+                      include_bounding_boxes=True,

+                      for time_offset, emotion_label, score in frame_emotions:
+                          print('\t{:04.2f}s: {:14}({:4.3f})'.format(
+                              time_offset, emotion_label, score))
+                      print('\n')

+                          emotions = frame.attributes[0].emotions
+                          # from videointelligence.enums
+                          emotion_labels = (

+              Usage Examples:
+                  python beta_snippets.py boxes \
+                  gs://python-docs-samples-tests/video/googlework_short.mp4

+              # limitations under the License.
+              """This application demonstrates face detection, label detection,
+              explicit content, and shot change detection using the Google Cloud API.



		# [START video_face_bounding_boxes]
		def face_bounding_boxes(path):

+                      print('\tSegment: {}\n'.format(positions))
+                      # There are typically many frames for each face,
+                      # here we print information on only the first frame.

+                  features = [
+                      videointelligence.enums.Feature.SPEECH_TRANSCRIPTION
+                  ]


		result = operation.result(timeout=180)

		annotation_results = result.annotation_results[0]

+                      positions = '{}s to {}s'.format(start_time, end_time)
+                      print('\tSegment: {}\n'.format(positions))
+                      # Each detected may appear in many frames of the video.

+                          emotion, score = sorted(
+                              [(em.emotion, em.score) for em in emotions],
+                              key=lambda p: p[1])[-1]

+                          # every emotion gets a score, here we sort them by
+                          # scores and keep only the one that scores the highest.
+                          most_likely_emotion = sorted(emotions, key=lambda em: em.score)[-1]



		# [START video_speech_transcription]
		def speech_transcription(input_uri):

Face detection beta features by dizcology · Pull Request #1414 · GoogleCloudPlatform/python-docs-samples · GitHub

Uh oh!

Face detection beta features - #1414

Face detection beta features#1414andrewsg merged 18 commits intoGoogleCloudPlatform:masterGoogleCloudPlatform/python-docs-samples:masterfrom dizcology:face_detectiondizcology/python-docs-samples:face_detectionCopy head branch name to clipboard

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Face detection beta features#1414
andrewsg merged 18 commits into
GoogleCloudPlatform:masterfrom
dizcology:face_detection

dizcology commented Mar 19, 2018 •
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