import cv2
from basecomponent import BaseComponent
class SimpleDetector(BaseComponent):
'''
A SimpleDetector uses Haar like or LBPH based cascade of weak classifiers
for detection.
'''
def __init__(self, cfg):
BaseComponent.__init__(self, cfg)
params = cfg['params']
self.detector = cv2.CascadeClassifier(params['model'])
self.scaledown_factor = params.get('scaledown_factor', 1.1)
self.min_neighbors = params.get('min_neighbors', 3)
self.output_label = params['outputlabel']
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']
min_size = (min(50, gray_img.shape[0] // 10), min(50, gray_img.shape[1] // 10))
# hits returned are (x,y,width, height) where x,y are topleft coordinates
hits = self.detector.detectMultiScale(gray_img,
self.scaledown_factor,
self.min_neighbors,
0, min_size)
results = [
{
'labels' : [{'label':self.output_label}],
# these int casts are to avoid json serialization typerror because
# the coords returned by opencv are actually np.int32s
'rect':[int(x),int(y),int(x+w),int(y+h)]
} for (x,y,w,h) in hits]
return results
def detect_in_rois(self, input_data, comp_reports):
gray_img = input_data['gray']
min_size = (min(50, gray_img.shape[0] // 10), min(50, gray_img.shape[1] // 10))
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("simpledetector: ROI rect ", rect)
x_offset = rect[0]
y_offset = rect[1]
roi = gray_img[ rect[1]:rect[3], rect[0]:rect[2] ]
hits = self.detector.detectMultiScale(roi,
self.scaledown_factor,
self.min_neighbors,
0, min_size)
# These detections in ROI are relative to ROI. So we must add ROI origin to
# those coordinates to make them full image coordinates.
print("simpledetector hits:", hits)
results = [
{
'labels' : [{'label':self.output_label}],
# these int casts are to avoid json serialization typerror because
# the coords returned by opencv are actually np.int32s
'rect':[
int(x + x_offset),
int(y + y_offset),
int(x + x_offset + w),
int(y + y_offset + h)
]
} for (x,y,w,h) in hits]
roi_detections.extend(results)
return roi_detections
