def extractHOGfromVideo(path):
vE = videoExplorer()
vE.setVideoStream(path, info=False, frameMode='RGB')
feats = []
for frame in vE:
feats += [feat.hog(frame)]
f = np.asarray(feats)
np.save(path.split('.avi')[0] + '.feat.{ending}.npy'.format(ending='hog-8'), f)
for k in range(9): print k tmpBoxRot = allBoxesRot[k] # tmpBoxRot = np.nan_to_num(tmpBoxRot) tmpBoxRot[tmpBoxRot>0] -= tmpBoxRot[tmpBoxRot>0].min() tmpBoxRot[tmpBoxRot>0] /= tmpBoxRot.max() # tmpBoxRot[tmpBoxRot>0] = np.log(tmpBoxRot[tmpBoxRot>0]) tmpBoxD = np.dstack([tmpBoxRot, tmpBoxRot, tmpBoxRot]) f = features.hog(tmpBoxD, 4) # figure(1); subplot(3,3,k+1) figure(1); imshow(tmpBoxRot) # figure(2); subplot(3,3,k+1) # imshow(f[:,:,i-1]) im = HOGpicture(f, 4) figure(2); imshow(im, interpolation='nearest')
from vision import features
from PIL import Image
from scipy.io import savemat as savematlab
im = Image.open("/scratch/vatic/syn-bounce-level/0/0/0.jpg")
f = features.hog(im)
savematlab(open("features.mat", "w"), {"py": f})
pos[b] = (pos[b][0] + int(size[b][0] / 2) - boxSize / 2,
pos[b][1] + int(size[b][1] / 2) - boxSize / 2)
# cv2.rectangle(im, pos[b], (pos[b][0]+size[b][0], pos[b][1]+size[b][1]), [200,100,100])
box = im[pos[b][1]:pos[b][1] + size[b][1],
pos[b][0]:pos[b][0] + size[b][0], 2]
# boxMean = int(box.mean())
# boxMax = box.max()
# box[box<boxMean] = box.max()
# box[box>=boxMean] -= box[box>0].min()
bodyPartDepths[b].append(deepcopy(box))
if calcHOG and box.shape[0] > 0 and box.shape[
1] > 0 and box.shape[0] == boxSize and box.shape[
1] == boxSize:
# tmpBox = np.dstack([box[:,:,2], box[:,:,2], box[:,:,2]])
tmpBoxD = np.dstack([box, box, box])
f = features.hog(tmpBoxD, 4)
bodyPartFeatures[b].append(deepcopy(f))
# im2 = HOGpicture(f, 4)
# figure(2); imshow(im, interpolation='nearest')
# for i in range(5):
# posNew = [pos[b][0]+random.randint(30,50), pos[b][1]+random.randint(30,50)]
# box = im[pos[b][1]:pos[b][1]+size[b][1], pos[b][0]:pos[b][0]+size[b][0], 2]
# if calcHOG and box.shape[0] > 0 and box.shape[1] > 0 and box.shape[0] == 30 and box.shape[1] == 30:
# otherDepths.append(deepcopy(box))
# tmpBoxD = np.dstack([box,box,box])
# f = features.hog(tmpBoxD, 4)
# otherFeatures.append(deepcopy(f))
# figure(1); subplot(2,4,b)
from vision import features
import Image
from scipy.io import savemat as savematlab
im = Image.open("/scratch/vatic/syn-bounce-level/0/0/0.jpg")
f = features.hog(im)
savematlab(open("features.mat", "w"), {"py": f})
for i in range(len(trainingDepths[bodypart]) - 1):
tmpImg = np.asarray(trainingDepths[bodypart][i][0], dtype=float)
## Normalize the image. Min min/max within standard deviations to help eliminate the background
if not np.all(tmpImg == 0):
imMean = tmpImg.mean()
imSTD = tmpImg.std()
min_ = tmpImg[tmpImg > imMean - 1 * imSTD].min()
tmpImg -= min_
max_ = tmpImg[tmpImg < imMean - min_ + 1 * imSTD].max()
tmpImg[tmpImg > max_] = max_
tmpImg[tmpImg < 0] = max_
tmpImg /= np.float(max_ / 255.0)
tmpImg = np.asarray(tmpImg, dtype=np.uint8)
tmp = np.dstack([tmpImg, tmpImg, tmpImg])
hogs[part].append(features.hog(tmp, hogRes))
from pyKinectTools.utils.HOGUtils import *
hogList = list(hogs["face"])
for p in hogs["l_hand"]:
hogList.append(p)
# hogList.append(hogs['l_hand'])
# hogList = np.array(hogList)
hogVals = np.empty([len(hogList), len(hogList[0].flatten())])
for i in range(len(hogList)):
# hogList[i] = hogList[i].flatten()
hogVals[i, :] = hogList[i][0].flatten()[0]
hogLabels = np.zeros(len(hogs["face"]) + len(hogs["l_hand"]))
# hogLabels[0:len(hogs['face'])]+=1
for i in range(len(trainingDepths[bodypart])-1): tmpImg = np.asarray(trainingDepths[bodypart][i][0], dtype=float) ## Normalize the image. Min min/max within standard deviations to help eliminate the background if not np.all(tmpImg == 0): imMean = tmpImg.mean() imSTD = tmpImg.std() min_ = tmpImg[tmpImg > imMean-1*imSTD].min() tmpImg -= min_ max_ = tmpImg[tmpImg < imMean-min_+1*imSTD].max() tmpImg[tmpImg>max_]=max_ tmpImg[tmpImg<0]=max_ tmpImg /= np.float(max_/255.0) tmpImg = np.asarray(tmpImg, dtype=np.uint8) tmp = np.dstack([tmpImg, tmpImg, tmpImg]) hogs[part].append(features.hog(tmp, hogRes)) from pyKinectTools.utils.HOGUtils import * hogList = list(hogs['face']) for p in hogs['l_hand']: hogList.append(p) # hogList.append(hogs['l_hand']) # hogList = np.array(hogList) hogVals = np.empty([len(hogList), len(hogList[0].flatten())]) for i in range(len(hogList)): # hogList[i] = hogList[i].flatten() hogVals[i,:] = hogList[i][0].flatten()[0] hogLabels = np.zeros(len(hogs['face'])+len(hogs['l_hand'])) # hogLabels[0:len(hogs['face'])]+=1 hogLabels[len(hogs['face']):]+=1
