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Diffstat (limited to 'notebooks/utils/HaarSaliency.py')
| -rw-r--r-- | notebooks/utils/HaarSaliency.py | 77 |
1 files changed, 0 insertions, 77 deletions
diff --git a/notebooks/utils/HaarSaliency.py b/notebooks/utils/HaarSaliency.py deleted file mode 100644 index 6a511efb..00000000 --- a/notebooks/utils/HaarSaliency.py +++ /dev/null @@ -1,77 +0,0 @@ -import os -from os.path import join -import cv2 -import numpy as np -import xml.etree.ElementTree -class HaarSaliency: - - # Input images should be between 320x240 and 640x480 - classifiers = [] - face_matrix = [] - flags = 0 - nstages = 0 - - def __init__(self, cascade, min_size=60, max_size=400, - face_neighbors=3, sal_neighbors=0, blur_kernel=(31,31), scale_factor=1.1, stage_start=1): - - self.face_neighbors = face_neighbors - self.sal_neighbors = sal_neighbors - self.scale_factor = scale_factor - self.blur_kernel = blur_kernel - self.min_size = (min_size, min_size) - self.max_size = (max_size, max_size) - self.stage_start = stage_start - - cdir, cname = os.path.split(cascade) - cname, ext = os.path.splitext(cname) - - root = xml.etree.ElementTree.parse(cascade) - #width = int(root.find('./cascade/width').text.strip()) - #height = int(root.find('./cascade/height').text.strip()) - self.nstages = int(root.find('./cascade/stageNum').text.strip()) - - # initialize classifiers - cascades = [join(cdir,cname,str(c)+'.xml') for c in range(stage_start,self.nstages+1) ] - self.classifiers = [cv2.CascadeClassifier(c) for c in cascades] - - def get_saliency(self,src): - - # conver to grayscale - src_gray = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY) - - # run face detector on all stage-classifiers - self.face_matrix = [self.detect_faces(src_gray,c) for c in self.classifiers] - - # create saliency map in grayscale - w,h = src.shape[1],src.shape[0] - saliency = np.zeros((h,w), dtype=np.float32).reshape(h,w) - - # draw face-roi as stage-weighted grayscale vals - # min_neighbors sets max value for grayscale --> white - for i,face_list in enumerate(self.face_matrix,1): - inc = round(255./float(self.face_neighbors)/float(self.nstages-self.stage_start)) * i - if face_list is not None: - for x1,y1,fw,fh in face_list: - saliency[y1:y1+fh,x1:x1+fw] += inc - - # normalize, clip, and recast as uint8 - smax = saliency.max() - if smax > 255: - saliency /= (smax/255) - saliency = np.clip(saliency,0,255) - saliency = np.array(saliency,dtype=np.uint8) - - # blur, colormap, and composite - saliency = cv2.GaussianBlur(saliency,self.blur_kernel,0) - dst = cv2.applyColorMap(saliency, cv2.COLORMAP_JET) - return dst - - def detect_faces(self,src,classifier): - - matches = classifier.detectMultiScale(src, self.scale_factor, - self.sal_neighbors, self.flags, self.min_size, self.max_size) - - if matches is None or len(matches) < 1: - return None - else: - return sorted(map(tuple,matches),reverse=True) # lg --> sm
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