Module ambianic.pipeline.ai.face_detect
Face detection pipe element.
Expand source code
"""Face detection pipe element."""
import logging
from .image_boundingBox_detection import TFBoundingBoxDetection
log = logging.getLogger(__name__)
class FaceDetector(TFBoundingBoxDetection):
"""Detecting faces in an image."""
@staticmethod
def crop_image(image, box):
"""Crop image to given box."""
# Size of the image in pixels (size of orginal image)
# (This is not mandatory)
width, height = image.size
# Setting the points for cropped image
left = box["xmin"] * width
top = box["ymin"] * height
right = box["xmax"] * width
bottom = box["ymax"] * height
# Cropped image of above dimension
# (It will not change orginal image)
im1 = image.crop((left, top, right, bottom))
return im1
def process_sample(self, **sample):
"""Detect faces in the given image sample."""
log.debug(
"Pipe element %s received new sample with keys %s.",
self.__class__.__name__,
str([*sample]),
)
if not sample:
# pass through empty samples to next element
yield None
else:
try:
image = sample["image"]
prev_inference_result = sample.get("inference_result", None)
log.debug(
"Received sample with inference_result: %s",
str(prev_inference_result),
)
person_regions = []
if not prev_inference_result:
yield None
else:
# - apply face detection to cropped person areas
# - pass face detections on to next pipe element
for e_result in prev_inference_result:
label, confidence, box = (
e_result["label"],
e_result["confidence"],
e_result["box"],
)
if (
label == "person"
and confidence >= self._tfengine.confidence_threshold
):
person_regions.append(box)
log.debug(
"Received %d person boxes for face detection",
len(person_regions),
)
for box in person_regions:
person_image = self.crop_image(image, box)
thumbnail, tensor_image, inference_result = self.detect(
image=person_image
)
inference_result = self.convert_inference_result(
inference_result
)
log.debug(
"Face detection inference_result: %r", inference_result
)
inf_meta = {
"display": "Face Detection",
}
processed_sample = {
"image": person_image,
"thumbnail": thumbnail,
"inference_result": inference_result,
"inference_meta": inf_meta,
}
yield processed_sample
except Exception as e:
log.exception(
"Error %r while processing sample. " "Dropping sample: %r",
e,
sample,
)
def convert_inference_result(self, inference_result):
inf_json = []
if inference_result:
for inf in inference_result:
label, confidence, box = inf[0:3]
log.info(
"label: %s , confidence: %.0f, box: %s", label, confidence, box
)
one_inf = {
"label": label,
"confidence": confidence,
"box": {
"xmin": box[0],
"ymin": box[1],
"xmax": box[2],
"ymax": box[3],
},
}
inf_json.append(one_inf)
return inf_jsonClasses
class FaceDetector (model=None, **kwargs)-
Detecting faces in an image.
Initialize detector with config parameters. :Parameters:
model: ai_models/mobilenet_ssd_v2_face.tflite
Expand source code
class FaceDetector(TFBoundingBoxDetection): """Detecting faces in an image.""" @staticmethod def crop_image(image, box): """Crop image to given box.""" # Size of the image in pixels (size of orginal image) # (This is not mandatory) width, height = image.size # Setting the points for cropped image left = box["xmin"] * width top = box["ymin"] * height right = box["xmax"] * width bottom = box["ymax"] * height # Cropped image of above dimension # (It will not change orginal image) im1 = image.crop((left, top, right, bottom)) return im1 def process_sample(self, **sample): """Detect faces in the given image sample.""" log.debug( "Pipe element %s received new sample with keys %s.", self.__class__.__name__, str([*sample]), ) if not sample: # pass through empty samples to next element yield None else: try: image = sample["image"] prev_inference_result = sample.get("inference_result", None) log.debug( "Received sample with inference_result: %s", str(prev_inference_result), ) person_regions = [] if not prev_inference_result: yield None else: # - apply face detection to cropped person areas # - pass face detections on to next pipe element for e_result in prev_inference_result: label, confidence, box = ( e_result["label"], e_result["confidence"], e_result["box"], ) if ( label == "person" and confidence >= self._tfengine.confidence_threshold ): person_regions.append(box) log.debug( "Received %d person boxes for face detection", len(person_regions), ) for box in person_regions: person_image = self.crop_image(image, box) thumbnail, tensor_image, inference_result = self.detect( image=person_image ) inference_result = self.convert_inference_result( inference_result ) log.debug( "Face detection inference_result: %r", inference_result ) inf_meta = { "display": "Face Detection", } processed_sample = { "image": person_image, "thumbnail": thumbnail, "inference_result": inference_result, "inference_meta": inf_meta, } yield processed_sample except Exception as e: log.exception( "Error %r while processing sample. " "Dropping sample: %r", e, sample, ) def convert_inference_result(self, inference_result): inf_json = [] if inference_result: for inf in inference_result: label, confidence, box = inf[0:3] log.info( "label: %s , confidence: %.0f, box: %s", label, confidence, box ) one_inf = { "label": label, "confidence": confidence, "box": { "xmin": box[0], "ymin": box[1], "xmax": box[2], "ymax": box[3], }, } inf_json.append(one_inf) return inf_jsonAncestors
Static methods
def crop_image(image, box)-
Crop image to given box.
Expand source code
@staticmethod def crop_image(image, box): """Crop image to given box.""" # Size of the image in pixels (size of orginal image) # (This is not mandatory) width, height = image.size # Setting the points for cropped image left = box["xmin"] * width top = box["ymin"] * height right = box["xmax"] * width bottom = box["ymax"] * height # Cropped image of above dimension # (It will not change orginal image) im1 = image.crop((left, top, right, bottom)) return im1
Methods
def convert_inference_result(self, inference_result)-
Expand source code
def convert_inference_result(self, inference_result): inf_json = [] if inference_result: for inf in inference_result: label, confidence, box = inf[0:3] log.info( "label: %s , confidence: %.0f, box: %s", label, confidence, box ) one_inf = { "label": label, "confidence": confidence, "box": { "xmin": box[0], "ymin": box[1], "xmax": box[2], "ymax": box[3], }, } inf_json.append(one_inf) return inf_json def process_sample(self, **sample)-
Detect faces in the given image sample.
Expand source code
def process_sample(self, **sample): """Detect faces in the given image sample.""" log.debug( "Pipe element %s received new sample with keys %s.", self.__class__.__name__, str([*sample]), ) if not sample: # pass through empty samples to next element yield None else: try: image = sample["image"] prev_inference_result = sample.get("inference_result", None) log.debug( "Received sample with inference_result: %s", str(prev_inference_result), ) person_regions = [] if not prev_inference_result: yield None else: # - apply face detection to cropped person areas # - pass face detections on to next pipe element for e_result in prev_inference_result: label, confidence, box = ( e_result["label"], e_result["confidence"], e_result["box"], ) if ( label == "person" and confidence >= self._tfengine.confidence_threshold ): person_regions.append(box) log.debug( "Received %d person boxes for face detection", len(person_regions), ) for box in person_regions: person_image = self.crop_image(image, box) thumbnail, tensor_image, inference_result = self.detect( image=person_image ) inference_result = self.convert_inference_result( inference_result ) log.debug( "Face detection inference_result: %r", inference_result ) inf_meta = { "display": "Face Detection", } processed_sample = { "image": person_image, "thumbnail": thumbnail, "inference_result": inference_result, "inference_meta": inf_meta, } yield processed_sample except Exception as e: log.exception( "Error %r while processing sample. " "Dropping sample: %r", e, sample, )
Inherited members