def cumulative_hist(dir, n_bins=[255, 255, 255], type='lab', mask=None):
files = Utils.get_files_from_dir_and_subdirs(dir)
if mask is None or cv2.countNonZero(mask) == 0:
mask = np.ones((img.shape[0], img.shape[1]), dtype=np.uint8)
if mask.shape[0] != img.shape[0] or mask.shape[1] != img.shape[1]:
print "mask shape: " + str(mask.shape)
print "image shape: " + str(img.shape)
print str(mask.shape[0] / float(mask.shape[1])) + ',' + str(
img.shape[0] / float(img.shape[1]))
raise ValueError('trouble with mask size, resetting to image size')
n_pixels = cv2.countNonZero(mask)
for file in files:
img = cv2.imread(file)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# OpenCV uses H: 0 - 180, S: 0 - 255, V: 0 - 255
# histograms
hist_hue = cv2.calcHist([hsv], [0], mask, [bins[0]], [0, 180])
hist_hue = [item for sublist in hist_hue
for item in sublist] # flatten nested
hist_hue = np.divide(hist_hue, n_pixels)
hist_sat = cv2.calcHist([hsv], [1], mask, [bins[1]], [0, 255])
hist_sat = [item for sublist in hist_sat for item in sublist]
hist_sat = np.divide(hist_sat, n_pixels)
hist_int = cv2.calcHist([hsv], [2], mask, [bins[2]], [0, 255])
hist_int = [item for sublist in hist_int
for item in sublist] # flatten nested list
hist_int = np.divide(hist_int, n_pixels)
def caffe_filelist(path):
types = ['bubba_kush', 'hawg', 'hindu_kush', 'platinum_og2']
file_list = Utils.get_files_from_dir_and_subdirs(path)
print('n_files:'+str(len(file_list)))
for file_n in file_list:
print('file:'+str(file_n))
if 'cropped' in file_n:
print('got :'+str(file_n))
mpath=os.path.split(file_n)[0]
containing_folder=os.path.split(mpath)[0]
print('mpath {0} containing {1}'.format(mpath,containing_folder))
# tail=os.path.split[1]
file_class = None
for i in range(0,len(types)):
if types[i] in file_n:
file_class = i
if file_class is not None:
print('{0} is class {1} :'.format(file_n,file_class))
def batch_crop(path):
file_list = Utils.get_files_from_dir_and_subdirs(path)
print('n_files:'+str(len(file_list)))
#print('filelist:'+str(file_list))
bb = [350,150,450,550]
for file_n in file_list:
# if not '_cropped' in file_n:
if not 0 : #skip the skipping
print('file:'+str(file_n))
img_arr = cv2.imread(file_n)
cropped = crop(img_arr,bb)
name=file_n.split('.')[0]
suffix=file_n.split('.')[1]
name = name+'_cropped.'+suffix
print('newname:'+str(name))
cv2.imwrite(name,cropped)
else:
print('skipping '+str(file_n)+', already done')
