Crops face bounding box and normalizes it by size
Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
| Endpoint Name | Data Format | Nature |
|---|---|---|
| video | system/array_4d_uint8/1 | Input |
| annotations | system/bounding_box_video/1 | Input |
| cropped_faces | system/array_3d_uint8/1 | Output |
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
| Name | Description | Type | Default | Range/Choices |
|---|---|---|---|---|
| normed-height | uint32 | 64 | ||
| normed-width | uint32 | 64 | ||
| minimum-face-size | uint32 | 50 |
xxxxxxxxxximport numpyimport bobclass Algorithm: def __init__(self): self.normed_height = 64 self.normed_width = 64 self.minimum_face_size = 50 # minimum size of a valid face def setup(self, parameters): self.normed_height = parameters.get('crop-height', self.normed_height) self.normed_width = parameters.get('crop-width', self.normed_width) self.right_eye_y = parameters.get('minimum-face-size', self.minimum_face_size) return True def process(self, inputs, outputs): video = inputs["video"].data.value annotations = inputs["annotations"].data.value preproc_faces_array = numpy.ndarray([video.shape[0], self.normed_height, self.normed_width], 'uint8') for i in range(video.shape[0]): #range(video.shape[0], -1, -1): frame = bob.ip.rgb_to_gray(video[i,:,:,:]) if annotations[i].height < self.minimum_face_size or annotations[i].width < self.minimum_face_size: continue # don't add the invalid frames cropped_face = bob.ip.crop(frame, int(getattr(annotations[i], 'top-left-y')), int(getattr(annotations[i], 'top-left-x')), int(annotations[i].height), int(annotations[i].width)) tempbbx = numpy.ndarray((self.normed_height, self.normed_width), 'float64') normed_face = numpy.ndarray((self.normed_height, self.normed_width), 'uint8') bob.ip.scale(cropped_face, tempbbx) # normalization tempbbx_ = tempbbx + 0.5 tempbbx_ = numpy.floor(tempbbx_) normed_face = numpy.cast['uint8'](tempbbx_) preproc_faces_array[i] = normed_face outputs["cropped_faces"].write({ 'value': preproc_faces_array }) return TrueThe code for this algorithm in Python
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This algorithm performs pre-processing of the input videos in two steps:
Parameters:
This algorithm relies on the Bob library.
| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| smarcel/ivana7c/simple-antispoofing-updated/1/replay2-antispoofing-lbp-histograms-fix | replay/3@grandtest | Kanma/iqm_spoof_eer_analyzer/1 | ||||
| sbhatta/ivana7c/simple-antispoofing-updated/1/replay2-antispoofing-lbp-histograms | replay/2@grandtest | sbhatta/iqm_spoof_eer_analyzer/9 | ||||
| smarcel/ivana7c/simple-antispoofing-updated/1/antispoof-chi2-expA-rr22 | replay/1@countermeasure | ivana7c/spoofing_eer/1 | ||||
| ivana7c/ivana7c/simple-antispoofing-updated/1/antispoof-chi2-expA | replay/1@countermeasure | ivana7c/spoofing_eer/1 |
This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.