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facerecognizer.py
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import cv2
import cv2.face as face
import numpy as np
from basecomponent import BaseComponent
import os.path
import simplejson as json
class FaceRecognizer(BaseComponent):
'''
A FaceRecognizer uses Eigen, Fisher, LBPH or combination of them
for face recognition.
'''
def __init__(self, cfg):
BaseComponent.__init__(self, cfg)
params = cfg['params']
models_dir = params['model']
if not os.path.exists(models_dir):
raise "Error: Invalid face recognizer model directory path " + models_dir
strategies = params['strategies']
if not strategies:
raise "Error: Invalid pipeline file. Recognizer should specify atleast 1 strategy: eigen|fischer|lbp"
self.output_label = params['outputlabel']
if 'eigen' in strategies:
self.eigen = face.createEigenFaceRecognizer();
self.eigen.load(os.path.join(models_dir, 'eigen.yml'))
else:
if 'eigen' in self.output_label:
raise "Error: Invalid pipeline file. Recognizer has eigen in output label but not in strategies"
self.eigen = None
if 'fischer' in strategies:
self.fischer = face.createFisherFaceRecognizer();
self.fischer.load(os.path.join(models_dir, 'fischer.yml'))
else:
if 'fischer' in self.output_label:
raise "Error: Invalid pipeline file. Recognizer has fischer in output label but not in strategies"
self.fischer = None
if 'lbp' in strategies:
self.lbp = face.createLBPHFaceRecognizer();
self.lbp.load(os.path.join(models_dir, 'lbp.yml'))
else:
if 'lbp' in self.output_label:
raise "Error: Invalid pipeline file. Recognizer has lbp in output label but not in strategies"
self.lbp = None
with open(os.path.join(models_dir, 'model.json'), 'r') as model_file:
self.model = json.load(model_file)
self.train_img_size = (self.model['height'], self.model['width'])
self.labels = self.model['labels']
self.equalize_hist = params.get('equalizehist', False)
def execute(self, input_data, input_directory, output_directory):
# Check what configured inputs are - whether complete image or ROIs output by some
# other components.
all_detections = []
for source in self.cfg['inputs']:
if source == 'files':
detections = self.detect_in_image(input_data)
all_detections.extend(detections)
else:
triggerlabels = self.cfg['params'].get('triggerlabels')
if not triggerlabels:
print("Warning: pipeline file specifies {} in inputs but there are no triggerlabels in params".format(source))
continue
comp_outputs = input_data.get(source)
if comp_outputs:
comp_reports = comp_outputs['reports']
detections = self.detect_in_rois(input_data, comp_reports)
all_detections.extend(detections)
print(all_detections)
results = {
'reports' : all_detections
}
return results
def detect_in_image(self, input_data):
gray_img = input_data['gray']
results = self._detect_in_area(gray_img.copy())
return results
def detect_in_rois(self, input_data, comp_reports):
print("Facerecognizer: detect in ROIs")
gray_img = input_data['gray']
print("Facerecognizer: gray image", gray_img.shape)
roi_detections = []
for r in comp_reports:
if ('all' in self.cfg['params']['triggerlabels']) or \
any( [ l['label'] in self.cfg['params']['triggerlabels'] for l in r['labels'] ] ) :
rect = r['rect']
print("Facerecognizer: ROI rect ", rect)
x_offset = rect[0]
y_offset = rect[1]
roi = gray_img[ rect[1]:rect[3], rect[0]:rect[2] ]
print("Facerecognizer: ROI ", roi.shape)
results = self._detect_in_area(roi.copy())
for r in results:
rect = r['rect']
rect[0] += x_offset
rect[1] += y_offset
rect[2] += x_offset
rect[3] += y_offset
roi_detections.extend(results)
return roi_detections
def _detect_in_area(self, gray_img):
if self.equalize_hist:
gray_img = cv2.equalizeHist(gray_img)
if gray_img.shape != self.train_img_size:
print("recognizer input, trainings sizes:", gray_img.shape, self.train_img_size[::-1])
roi = cv2.resize( gray_img, (self.train_img_size[1], self.train_img_size[0]) )
else:
roi = gray_img
eigen_label, eigen_conf = self.eigen.predict(roi) if self.eigen else (-1,-1)
fischer_label, fischer_conf = self.fischer.predict(roi) if self.fischer else (-1,-1)
lbp_label,lbp_conf = self.lbp.predict(roi) if self.lbp else (-1,-1)
labels = []
if 'all' in self.output_label:
if self.eigen:
labels.append({'label':self.labels[str(eigen_label)], 'method':'eigen'})
if self.fischer:
labels.append({'label':self.labels[str(fischer_label)], 'method':'fischer'})
if self.lbp:
labels.append({'label':self.labels[str(lbp_label)], 'method':'lbp'})
elif 'mostvotes' in self.output_label:
methods = ['eigen', 'fischer', 'lbp']
label_values = [eigen_label, fischer_label, lbp_label]
votes = [label_values.count(l) if l >= 0 else 0 for l in label_values]
most_votes = max(votes)
most_voted_label_value = label_values[np.argmax(most_votes)]
most_votes_methods = []
for i,m in enumerate(methods):
if votes[i] == most_votes:
most_votes_methods.append(m)
labels.append({'label':self.labels[str(most_voted_label_value)], 'method':','.join(most_votes_methods)})
elif 'eigen' in self.output_label:
labels.append({'label':self.labels[str(eigen_label)], 'method':'eigen'})
elif 'fischer' in self.output_label:
labels.append({'label':self.labels[str(fischer_label)], 'method':'fischer'})
elif 'lbp' in self.output_label:
labels.append({'label':self.labels[str(lbp_label)], 'method':'lbp'})
results = [
{
'labels' : labels,
'rect':[0,0,gray_img.shape[1],gray_img.shape[0]]
} ]
return results