def evaluate_model(self, model_path, iou_threshold = 0.5):
self.model = MaskRCNN(mode = "inference", model_dir = os.getcwd(), config = self.config)
if os.path.isfile(model_path):
model_files = [model_path]
if os.path.isdir(model_path):
model_files = sorted([os.path.join(model_path, file_name) for file_name in os.listdir(model_path)])
for modelfile in model_files:
if str(modelfile).endswith(".h5"):
self.model.load_weights(modelfile, by_name=True)
APs = []
#outputs = list()
for image_id in self.dataset_test.image_ids:
# load image, bounding boxes and masks for the image id
image, image_meta, gt_class_id, gt_bbox, gt_mask = load_image_gt(self.dataset_test, self.config, image_id)
# convert pixel values (e.g. center)
scaled_image = mold_image(image, self.config)
# convert image into one sample
sample = np.expand_dims(scaled_image, 0)
# make prediction
yhat = self.model.detect(sample, verbose=0)
# extract results for first sample
r = yhat[0]
# calculate statistics, including AP
AP, _, _, _ = compute_ap(gt_bbox, gt_class_id, gt_mask, r["rois"], r["class_ids"], r["scores"], r['masks'])
# store
APs.append(AP)
# calculate the mean AP across all images
mAP = np.mean(APs)
print(modelfile, "evaluation using iou_threshold", iou_threshold, "is", f"{mAP:01f}", '\n')
def load_model(self, model_path):
self.model = MaskRCNN(mode="inference",
model_dir=self.model_dir,
config=coco_config)
self.model.load_weights(model_path, by_name=True)
def load_model(self, model_path):
#load the weights for COCO
self.model = MaskRCNN(mode="inference",
model_dir=self.model_dir,
config=self.config)
self.model.load_weights(model_path, by_name=True)