def predict(img_path, evaluator, cfg, debug=False):
# detect objects in single image
regressed_rois, cls_probs = evaluator.process_image(img_path)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs,
cfg)
# visualize detections on image
if debug:
od.visualize_results(img_path,
bboxes,
labels,
scores,
cfg,
store_to_path=img_path + "o.jpg")
# write detection results to output
fg_boxes = np.where(labels > 0)
result = []
for i in fg_boxes[0]:
# print (cfg["DATA"].CLASSES)
# print(labels)
result.append({
'label': cfg["DATA"].CLASSES[labels[i]],
'score': '%.3f' % (scores[i]),
'box': [int(v) for v in bboxes[i]]
})
return result
def evaluate_model(eval_model, detector_name):
cfg = get_configuration(detector_name)
cfg['NUM_CHANNELS'] = 3
print("Map file = ", cfg["DATA"].CLASS_MAP_FILE)
cfg["DATA"].CLASSES = parse_class_map_file(
os.path.join("Steer_Bad_Relevant_output", cfg["DATA"].CLASS_MAP_FILE))
cfg["DATA"].NUM_CLASSES = len(cfg["DATA"].CLASSES)
# detect objects in single image
img_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
r"Steer_Bad_Relevant_output/testImages/Steer_Bad_Front_Zoom (269).jpg")
regressed_rois, cls_probs = od.evaluate_single_image(
eval_model, img_path, cfg)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs, cfg)
fg_boxes = np.where(labels > 0)
print("#bboxes: before nms: {}, after nms: {}, foreground: {}".format(
len(regressed_rois), len(bboxes), len(fg_boxes[0])))
for i in fg_boxes[0]:
print("{:<12} (label: {:<2}), score: {:.3f}, box: {}".format(
cfg["DATA"].CLASSES[labels[i]], labels[i], scores[i],
[int(v) for v in bboxes[i]]))
od.visualize_results(img_path,
bboxes,
labels,
scores,
cfg,
store_to_path="Steer_Bad_Relevant_output/output.jpg")
eval_results = od.evaluate_test_set(eval_model, cfg)
# write AP results to output
for class_name in eval_results:
print('AP for {:>15} = {:.4f}'.format(class_name,
eval_results[class_name]))
print('Mean AP = {:.4f}'.format(np.nanmean(list(eval_results.values()))))
# detect objects in single image
img_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
r"../DataSets/Grocery/testImages/WIN_20160803_11_28_42_Pro.jpg")
regressed_rois, cls_probs = od.evaluate_single_image(
eval_model, img_path, cfg)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs, cfg)
# write detection results to output
fg_boxes = np.where(labels > 0)
print("#bboxes: before nms: {}, after nms: {}, foreground: {}".format(
len(regressed_rois), len(bboxes), len(fg_boxes[0])))
for i in fg_boxes[0]:
print("{:<12} (label: {:<2}), score: {:.3f}, box: {}".format(
cfg["DATA"].CLASSES[labels[i]], labels[i], scores[i],
[int(v) for v in bboxes[i]]))
# visualize detections on image
od.visualize_results(img_path, bboxes, labels, scores, cfg)
# measure inference time
od.measure_inference_time(eval_model, img_path, cfg, num_repetitions=100)
if __name__ == '__main__':
# Currently supported detectors: 'FastRCNN', 'FasterRCNN'
args = sys.argv
detector_name = get_detector_name(args)
cfg = get_configuration(detector_name)
# train and test
eval_model = od.train_object_detector(cfg)
eval_results = od.evaluate_test_set(eval_model, cfg)
# write AP results to output
for class_name in eval_results: print('AP for {:>15} = {:.4f}'.format(class_name, eval_results[class_name]))
print('Mean AP = {:.4f}'.format(np.nanmean(list(eval_results.values()))))
# detect objects in single image
img_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), r"../DataSets/Grocery/testImages/WIN_20160803_11_28_42_Pro.jpg")
regressed_rois, cls_probs = od.evaluate_single_image(eval_model, img_path, cfg)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs, cfg)
# write detection results to output
fg_boxes = np.where(labels > 0)
print("#bboxes: before nms: {}, after nms: {}, foreground: {}".format(len(regressed_rois), len(bboxes), len(fg_boxes[0])))
for i in fg_boxes[0]: print("{:<12} (label: {:<2}), score: {:.3f}, box: {}".format(
cfg["DATA"].CLASSES[labels[i]], labels[i], scores[i], [int(v) for v in bboxes[i]]))
# visualize detections on image
od.visualize_results(img_path, bboxes, labels, scores, cfg)
# measure inference time
od.measure_inference_time(eval_model, img_path, cfg, num_repetitions=100)