def __init__(self):
self.results = Results()
def run(images_path="media/",
filename="",
num_of_results=1,
hm_lvl=0,
certainty=0,
data_dir=DATA_DIR):
"""execute face detection than vgg face and finally grad-cam
:param filename: query image filename (default = "")
:param images_path: query and output image path (default = "media/")
:param data_dir: openCV directory path (default = DATA_DIR)
:return TODO
"""
res = Results(certainty)
file_path = os.path.join(images_path, filename)
if not file_path:
res.err_msg = "ERROR: cannot load input image {}".format(filename)
res.err_code = 1
message(res.err_msg)
return res
face = FaceDetect(data_dir, file_path)
if not face.is_valid:
res.err_msg = "ERROR: cannot load input image {}".format(filename)
res.err_code = 1
message(res.err_msg)
return res
face.load_cascades()
if not face.is_loaded:
res.err_msg = "ERROR: cannot load cascades from: {}".format(data_dir)
res.err_code = 2
message(res.err_msg)
return res
face.detect_face()
if not face.has_face:
res.err_msg = "ERROR: sorry, frontal face wasn't detected"
res.err_code = 3
message(res.err_msg)
return res
# Crop faces from query image
im = cv2.imread(file_path)
# If found more than one face, pick the biggest one (w * h)
cropped_im = crop_rect(im, max(face.features, key=lambda f: f[2] * f[3]))
cv2.imwrite(os.path.join(images_path, "cropped.jpg"), cropped_im)
# Run forward pass and GradCam on cropped image
pred_labels, err_msg = classifier_gcam.predict(images_path, num_of_results)
if (pred_labels is None) or (len(pred_labels) == 0):
res.err_msg = err_msg
res.err_code = 4
message(res.err_msg)
return res
# Get predicted label from Torch output
pred_ids = get_prediction_from_names(pred_labels)
if len(pred_labels) == 0:
res.err_msg = "ERROR: could not load names.txt file"
res.err_code = 5
message(res.err_msg)
return res
res.set_results(cropped_im, pred_ids[0], pred_labels[0], pred_ids[1:],
hm_lvl)
res.find_significant_features()
return res
