def hunmen_detect_in_img(origin_img, img_name):
detector = joblib.load(params.model_path)
img = np.copy(gamma_normalize.gamma_normalize(origin_img))
pos_windows_scaled_coordinates = []
pos_feature_vector_in_img = []
pos_windows_scale = []
v, h = img.shape
scale = 0 # to record the resizing times
while params.window_height <= v and params.window_width <= h:
print img.shape
pos_windows_in_single_scale, pos_feature_vector_in_scale = classifier_trainer.search_a_scale(detector, img)
for i in xrange(pos_feature_vector_in_scale.__len__()):
pos_windows_scale.append(scale)
pos_feature_vector_in_img += pos_feature_vector_in_scale
pos_windows_scaled_coordinates += pos_windows_in_single_scale
img = cv2.resize(img, None, fx=params.scale_gap, fy=params.scale_gap)
v, h = img.shape
scale += 1
mark(pos_windows_scaled_coordinates, pos_windows_scale, origin_img, img_name)
return pos_windows_scaled_coordinates, pos_feature_vector_in_img