hwratio=np.append(hwratio, props[label-1].major_axis_length/props[label-1].minor_axis_length)
density_hull=np.append(density_hull, props[label-1].solidity)
circularity=np.append(circularity, props[label-1].eccentricity)
if len(density_cbcr) == 0:
density_cbcr = -1*np.ones(1)
if len(area_covered) == 0:
area_covered = -1*np.ones(1)
if len(hwratio) == 0:
hwratio = -1*np.ones(1)
if len(density_hull) == 0:
density_hull = -1*np.ones(1)
if len(circularity) == 0:
circularity = -1*np.ones(1)
labels_df['percent_skin_abg'][index] = sm.percent_skin(data.ImgReg.skin.abg)
labels_df['percent_skin_cbcr'][index] = sm.percent_skin(data.ImgReg.skin.cbcr)
labels_df['percent_skin_cccm'][index] = sm.percent_skin(data.ImgReg.skin.cccm)
labels_df['percent_skin_equalized_abg'][index] = sm.percent_skin(data.ImgEq.skin.abg)
labels_df['percent_skin_equalized_cbcr'][index] = sm.percent_skin(data.ImgEq.skin.cbcr)
labels_df['percent_skin_equalized_cccm'][index] = sm.percent_skin(data.ImgEq.skin.cccm)
labels_df['label_number'][index] = len(density_cbcr)
labels_df['area_covered_max'][index] = max(area_covered)
labels_df['area_covered_mode'][index] = float(stats.mode(area_covered)[0])
labels_df['density_max'][index] = max(density_cbcr)
labels_df['density_mode'][index] = float(stats.mode(density_cbcr)[0])
labels_df['hwratio_max'][index] = max(hwratio)
labels_df['hwratio_mode'][index] = float(stats.mode(hwratio)[0])
labels_df['density_hull_max'][index] = max(density_hull)
labels_df['density_hull_mode'][index] = float(stats.mode(density_hull)[0])
labels_df['circularity_max'][index] = max(circularity)
image_EA_cbcr = data.ImgEqAdapt.skin.cbcr
image_EA_cccm = data.ImgEqAdapt.skin.cccm
labeled_abg, shapes_abg = change_morphology_label(image_EA_abg)
labeled_cbcr, shapes_cbcr = change_morphology_label(image_EA_cbcr)
labeled_cccm, shapes_cccm = change_morphology_label(image_EA_cccm)
props_abg = measure.regionprops(labeled_abg)
props_cbcr = measure.regionprops(labeled_cbcr)
props_cccm = measure.regionprops(labeled_cccm)
img_size = (labeled_abg.shape)[0]*(labeled_abg.shape)[1]
features_abg = get_features(labeled_abg, props_abg, image_EA_abg, shapes_abg, img_size)
features_cbcr = get_features(labeled_cbcr, props_cbcr, image_EA_cbcr, shapes_cbcr, img_size)
features_cccm = get_features(labeled_cccm, props_cccm, image_EA_cccm, shapes_cccm, img_size)
print "measure.regionprops done"
labels_df.loc[len(labels_df.index)]=([sm.percent_skin(image_abg), sm.percent_skin(image_cbcr), sm.percent_skin(image_cccm),\
sm.percent_skin(image_EA_abg), sm.percent_skin(image_EA_cbcr), sm.percent_skin(image_EA_cccm),\
features_abg[0], features_abg[1], features_abg[2], features_abg[3], features_abg[4], features_abg[5],\
features_abg[6], features_abg[7], features_abg[8], features_abg[9], features_abg[10],\
features_cbcr[0], features_cbcr[1], features_cbcr[2], features_cbcr[3], features_cbcr[4], features_cbcr[5],\
features_cbcr[6], features_cbcr[7], features_cbcr[8], features_cbcr[9], features_cbcr[10],\
features_cccm[0], features_cccm[1], features_cccm[2], features_cccm[3], features_cccm[4], features_cccm[5],\
features_cccm[6], features_cccm[7], features_cccm[8], features_cccm[9], features_cccm[10]])
#print "label features added to data frame"
labels_df.to_csv("Features_Food.csv")
labels_df["URL"] = urls
index = -1
for url in urls:
index += 1
print "index, url: %d, %s" % (index, url)
try:
read = urlopen(url).read()
obj = Image.open(cStringIO.StringIO(read))
image = np.array(obj)
if image.shape[2] == 3: # skip gray scale
data = sm.SetUpImage(image)
print "image shape:", image.shape
labels_df["pskin_ImgReg_abg"][index] = sm.percent_skin(data.ImgReg.skin.abg)
labels_df["pskin_ImgReg_cbcr"][index] = sm.percent_skin(data.ImgReg.skin.cbcr)
labels_df["pskin_ImgReg_cccm"][index] = sm.percent_skin(data.ImgReg.skin.cccm)
labels_df["pskin_ImgEq_abg"][index] = sm.percent_skin(data.ImgEq.skin.abg)
labels_df["pskin_ImgEq_cbcr"][index] = sm.percent_skin(data.ImgEq.skin.cbcr)
labels_df["pskin_ImgEq_cccm"][index] = sm.percent_skin(data.ImgEq.skin.cccm)
labels_df["pskin_ImgAdapt_abg"][index] = sm.percent_skin(data.ImgEqAdapt.skin.abg)
labels_df["pskin_ImgAdapt_cbcr"][index] = sm.percent_skin(data.ImgEqAdapt.skin.cbcr)
labels_df["pskin_ImgAdapt_cccm"][index] = sm.percent_skin(data.ImgEqAdapt.skin.cccm)
labels_df["pfill_ImgReg_abg"][index] = sm.fill_percent(data.ImgReg.skin.abg)
labels_df["pfill_ImgReg_cbcr"][index] = sm.fill_percent(data.ImgReg.skin.cbcr)
labels_df["pfill_ImgReg_cccm"][index] = sm.fill_percent(data.ImgReg.skin.cccm)
labels_df["pfill_ImgEq_abg"][index] = sm.fill_percent(data.ImgEq.skin.abg)
labels_df["pfill_ImgEq_cbcr"][index] = sm.fill_percent(data.ImgEq.skin.cbcr)
labels_df["pfill_ImgEq_cccm"][index] = sm.fill_percent(data.ImgEq.skin.cccm)
for label in range(1, shapes+1):
density = float(((labeled==label)&data.ImgReg.skin.cccm).sum())/float((labeled==label).sum())
#defines a region of interest if at least 50% of its pixels are skin color, but less than 100%,
#and if the area is bigger than an arbritary number
print "in label loop"
if density>0.5 and density<1 and props[label-1].area > 50:
density_cbcr = density
area_covered = props[label-1].area/img_size
hwratio = props[label-1].major_axis_length/props[label-1].minor_axis_length
density_hull = props[label-1].solidity
circularity = props[label-1].eccentricity
labels_df.loc[len(labels_df.index)] = [label, area_covered, density_cbcr, hwratio, density_hull, circularity]
print "label features added to data frame"
# labels_df = pd.DataFrame(columns=['percent_skin','percent_skin_equalized','label_number', 'area_covered', 'density_cbcr', 'hwratio', 'density_hull', 'circularity'])
labels_df["percent_skin"]=np.nan
labels_df.percent_skin[0:3] = np.array([sm.percent_skin(data.ImgReg.skin.abg), \
sm.percent_skin(data.ImgReg.skin.cbcr), \
sm.percent_skin(data.ImgReg.skin.cccm)])
labels_df["percent_skin_equalized"]=np.nan
labels_df.percent_skin_equalized[0:3] = np.array([sm.percent_skin(data.ImgEq.skin.abg), \
sm.percent_skin(data.ImgEq.skin.cbcr), \
sm.percent_skin(data.ImgEq.skin.cccm)])
print "percent skin added to df"
labels_df.to_csv("Features.csv")
for url in urls:
index += 1
print "index, url: %d -- %s" % (index,url)
try:
read = urlopen(url).read()
obj = Image.open(cStringIO.StringIO(read))
img = np.array(obj)
data = sm.SetUpImage(img)
todd_map= data.SkinLikelihood()
SkinFilter= sm.FilterSlice(todd_map,6)
print "image read in"
print url
#percent skin-like pixels in each skinmap
labels_df['percent_skin_SkinLikelihood_6'][index] = sm.percent_skin(SkinFilter)
labels_df['percent_skin_abg'][index] = sm.percent_skin(data.ImgReg.skin.abg)
labels_df['percent_skin_cbcr'][index] = sm.percent_skin(data.ImgReg.skin.cbcr)
labels_df['percent_skin_equalized_cccm'][index] = sm.percent_skin(data.ImgReg.skin.cccm)
labels_df['percent_skin_equalized_abg'][index] = sm.percent_skin(data.ImgEq.skin.abg)
labels_df['percent_skin_equalized_cbcr'][index] = sm.percent_skin(data.ImgEq.skin.cbcr)
labels_df['percent_skin_equalized_cccm'][index] = sm.percent_skin(data.ImgEq.skin.cccm)
labels_df['percent_skin_adapt_abg'][index] = sm.percent_skin(data.ImgEqAdapt.skin.abg)
labels_df['percent_skin_adapt_cbcr'][index] = sm.percent_skin(data.ImgEqAdapt.skin.cbcr)
labels_df['percent_skin_adapt_cccm'][index] = sm.percent_skin(data.ImgEqAdapt.skin.cccm)
#find blobs and measure properties
image = SkinFilter
#dilate,fill,erode
struct1 = ndimage.generate_binary_structure(2, 2)
img_dilated = ndimage.morphology.binary_dilation(image, iterations=1, structure=struct1)
def extract_features_and_feature_Figs_to_plot(n_images,nth_image,rgb_image,image_name):
cols= [
'UserUploadedImageName',
'percent_skin_SkinLikelihood_6',
'percent_skin_abg',
'percent_skin_cbcr',
'percent_skin_cccm',
'percent_skin_equalized_abg',
'percent_skin_equalized_cbcr',
'percent_skin_equalized_cccm',
'percent_skin_adapt_abg',
'percent_skin_adapt_cbcr',
'percent_skin_adapt_cccm',
'N_area_greater_HardCutoff',
'N_area_greater_floor',
'N_skin_pix_filled_greater_floor',
'N_amt_horiz_vert_lines_less_ceil',
'N_hull_filled_greater_floor',
'percent_area_gr_HardCutoff',
'percent_area_gr_floor',
'percent_area_skinpix_gr_floor',
'percent_area_horiz_vert_less_ceil',
'percent_area_hull_filled_gr_floor'
]
labels_df=DataFrame()
for name in cols:
labels_df[name] = np.zeros(n_images)
labels_df["UserUploadedImageName"]= image_name
Figs= BlobFeaturesToPlot() #store blob feature images for imshow
Figs.rgb_image= rgb_image
obj= sm.SetUpImage(rgb_image)
im= obj.SkinLikelihood()
SkinFilter= FilterSlice(im,6) #6 is best value, 7 too retrictive, < 6 not great
Figs.SkinLikelihood_6 = SkinFilter
#percent of skin-like pixels in each skinmap
labels_df['percent_skin_SkinLikelihood_6'][nth_image] = sm.percent_skin(SkinFilter)
labels_df['percent_skin_abg'][nth_image] = sm.percent_skin(obj.ImgReg.skin.abg)
labels_df['percent_skin_cbcr'][nth_image] = sm.percent_skin(obj.ImgReg.skin.cbcr)
labels_df['percent_skin_equalized_cccm'][nth_image] = sm.percent_skin(obj.ImgReg.skin.cccm)
labels_df['percent_skin_equalized_abg'][nth_image] = sm.percent_skin(obj.ImgEq.skin.abg)
labels_df['percent_skin_equalized_cbcr'][nth_image] = sm.percent_skin(obj.ImgEq.skin.cbcr)
labels_df['percent_skin_equalized_cccm'][nth_image] = sm.percent_skin(obj.ImgEq.skin.cccm)
labels_df['percent_skin_adapt_abg'][nth_image] = sm.percent_skin(obj.ImgEqAdapt.skin.abg)
labels_df['percent_skin_adapt_cbcr'][nth_image] = sm.percent_skin(obj.ImgEqAdapt.skin.cbcr)
labels_df['percent_skin_adapt_cccm'][nth_image] = sm.percent_skin(obj.ImgEqAdapt.skin.cccm)
image= SkinFilter
fl= GetFloorsObj(image)
#find blobs, consider only those containing > 900 skin pixels
labels,n =ndimage.measurements.label(image,np.ones((3,3)))
area = ndimage.measurements.sum(image,labels, index=np.arange(labels.max() + 1))
areaImg = area[labels]
Figs.areaImg_AllBlobs= areaImg
#cutoff, blobs must be sufficiently large
ind_cutoff= np.where(areaImg < fl.HardAreaCutoff)
areaImg[ind_cutoff] = 0.
Figs.areaImg_AreaGrHardCutoff= areaImg
props= measure.regionprops(areaImg.astype('int'),cache=True)
ind_area_greater_floor=[]
ind_skin_pix_filled_greater_floor=[]
ind_amt_horiz_vert_lines_less_ceil=[]
ind_hull_filled_greater_floor=[]
for cnt in range(len(props)):
blob= np.zeros(areaImg.shape).astype('bool')
ind_blob= np.where(areaImg == props[cnt].label)
blob[ind_blob]=True
filled_blob= ndimage.morphology.binary_fill_holes(blob)
if props[cnt].area > fl.AreaFloor:
ind_area_greater_floor.append(cnt)
if skin_pix_percent_filled(blob,filled_blob) > fl.SkinPercentGotFilledFloor:
ind_skin_pix_filled_greater_floor.append(cnt)
if percent_vert_horiz_lines(filled_blob,props[cnt].area) < fl.VertHorizCeil:
ind_amt_horiz_vert_lines_less_ceil.append(cnt)
if hull_percent_filled(filled_blob) > fl.HullPercentFilledFloor:
ind_hull_filled_greater_floor.append(cnt)
Figs.AreaGrFloor= getBlobsPropsIndex(areaImg,props,ind_area_greater_floor)
Figs.SkinPixFilledGrFloor= getBlobsPropsIndex(areaImg,props,ind_skin_pix_filled_greater_floor)
Figs.AmtHorizVertLessCeil= getBlobsPropsIndex(areaImg,props,ind_amt_horiz_vert_lines_less_ceil)
Figs.HullFilledGreaterFloor= getBlobsPropsIndex(areaImg,props,ind_hull_filled_greater_floor)
#fill labels
labels_df['N_area_greater_HardCutoff'][nth_image]= len(props)
labels_df['N_area_greater_floor'][nth_image]= len(ind_area_greater_floor)
labels_df['N_skin_pix_filled_greater_floor'][nth_image]= len(ind_skin_pix_filled_greater_floor)
labels_df['N_amt_horiz_vert_lines_less_ceil'][nth_image]= len(ind_amt_horiz_vert_lines_less_ceil)
labels_df['N_hull_filled_greater_floor'][nth_image]= len(ind_hull_filled_greater_floor)
#labels_df['EllipseFitResid_4HullFilled'][nth_image]= EllipseFitResid_4HullFilled
#total percent area of all blobs in each grouping
labels_df['percent_area_gr_HardCutoff'][nth_image]= percent_area_blobs_in_list(props,range(len(props)),SkinFilter)
labels_df['percent_area_gr_floor'][nth_image]= percent_area_blobs_in_list(props,ind_area_greater_floor,SkinFilter)
labels_df['percent_area_skinpix_gr_floor'][nth_image]= percent_area_blobs_in_list(props,ind_skin_pix_filled_greater_floor,SkinFilter)
labels_df['percent_area_horiz_vert_less_ceil'][nth_image]= percent_area_blobs_in_list(props,ind_amt_horiz_vert_lines_less_ceil,SkinFilter)
labels_df['percent_area_hull_filled_gr_floor'][nth_image]= percent_area_blobs_in_list(props,ind_hull_filled_greater_floor,SkinFilter)
return labels_df,Figs