Update detection result images

object_detection/efficientdet/efficientdet.py

@@ -11,7 +11,7 @@
 sys.path.append('../../util')
 from utils import get_base_parser, update_parser, get_savepath  # noqa: E402
 from model_utils import check_and_download_models  # noqa: E402
-from detector_utils import load_image  # noqa: E402
+from detector_utils import load_image, plot_results  # noqa: E402
 from nms_utils import bb_intersection_over_union  # noqa: E402
 from webcamera_utils import get_capture  # noqa: E402
 
@@ -191,22 +191,26 @@ def postprocess(
     return out
 
 
-def display(preds, imgs):
-    for i in range(len(imgs)):
-        if len(preds[i]['rois']) == 0:
-            continue
+def convert_to_ailia_detector_object(preds, w, h):
+    i = 0
+    detector_object = []
+    for j in range(len(preds[i]['rois'])):
+        (x1, y1, x2, y2) = preds[i]['rois'][j].astype(np.int)
+        obj = preds[i]['class_ids'][j]
+        score = float(preds[i]['scores'][j])
 
-        for j in range(len(preds[i]['rois'])):
-            (x1, y1, x2, y2) = preds[i]['rois'][j].astype(np.int)
-            cv2.rectangle(imgs[i], (x1, y1), (x2, y2), (255, 255, 0), 2)
-            obj = obj_list[preds[i]['class_ids'][j]]
-            score = float(preds[i]['scores'][j])
+        r = ailia.DetectorObject(
+            category=obj,
+            prob=score,
+            x=x1 / w,
+            y=y1 / h,
+            w=(x2 - x1) / w,
+            h=(y2 - y1) / h,
+        )
 
-            cv2.putText(imgs[i], '{}, {:.3f}'.format(obj, score),
-                        (x1, y1 + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
-                        (255, 255, 0), 1)
+        detector_object.append(r)
 
-    return imgs
+    return detector_object
 
 
 # ======================
@@ -267,26 +271,26 @@ def recognize_from_image(image_path, net):
     # inference
     logger.info('Start inference...')
     if args.benchmark:
-        # if not args.onnx:
-        #    net.set_profile_mode(True)
+        if not args.profile:
+            net.set_profile_mode(True)
         logger.info('BENCHMARK mode')
         for i in range(5):
             start = int(round(time.time() * 1000))
             pred = predict(img, net)
             end = int(round(time.time() * 1000))
             logger.info(f'\tailia processing time {end - start} ms')
-        # if not args.onnx:
-        #    print(net.get_summary())
+        if not args.profile:
+            print(net.get_summary())
     else:
         pred = predict(img, net)
 
     # plot result
-    imgs = display(pred, [img])
+    detect_object = convert_to_ailia_detector_object(pred, img.shape[1], img.shape[0])
+    img = plot_results(detect_object, img, obj_list)
 
-    # plot result
     savepath = get_savepath(args.savepath, image_path)
     logger.info(f'saved at : {savepath}')
-    cv2.imwrite(savepath, imgs[0])
+    cv2.imwrite(savepath, img)
 
     if args.profile:
         print(net.get_summary())
@@ -305,8 +309,10 @@ def recognize_from_video(video, net):
         pred = predict(frame, net)
 
         # plot result
-        imgs = display(pred, [frame])
-        cv2.imshow('frame', imgs[0])
+        detect_object = convert_to_ailia_detector_object(pred, frame.shape[1], frame.shape[0])
+        img = plot_results(detect_object, frame, obj_list)
+
+        cv2.imshow('frame', img)
 
     capture.release()
     logger.info('Script finished successfully.')

object_detection/m2det/m2det.py

@@ -10,7 +10,7 @@
 sys.path.append('../../util')
 from utils import get_base_parser, update_parser, get_savepath  # noqa: E402
 from model_utils import check_and_download_models  # noqa: E402
-from detector_utils import load_image  # noqa: E402
+from detector_utils import plot_results, load_image  # noqa: E402
 import webcamera_utils  # noqa: E402
 
 # logger
@@ -115,25 +115,23 @@ def to_color(indx, base):
 COLORS = [to_color(x, BASE) for x in range(len(COCO_CATEGORY))]
 
 
-def draw_detection(im, bboxes, scores, cls_inds):
-    imgcv = np.copy(im)
-    h, w, _ = imgcv.shape
+def convert_to_ailia_detector_object(bboxes, scores, cls_inds, w, h):
+    detector_object = []
     for i, box in enumerate(bboxes):
         cls_indx = int(cls_inds[i])
-        box = [int(_) for _ in box]
-        thick = int((h + w) / 300)
-        cv2.rectangle(
-            imgcv,
-            (box[0], box[1]),
-            (box[2], box[3]),
-            COLORS[cls_indx],
-            thick
+
+        r = ailia.DetectorObject(
+            category=cls_indx,
+            prob=scores[i],
+            x=box[0] / w,
+            y=box[1] / h,
+            w=(box[2] - box[0]) / w,
+            h=(box[3] - box[1]) / h,
         )
-        mess = '%s: %.3f' % (COCO_CATEGORY[cls_indx], scores[i])
-        cv2.putText(imgcv, mess, (box[0], box[1] - 7),
-                    0, 1e-3 * h, COLORS[cls_indx], thick // 3)
-    return imgcv
 
+        detector_object.append(r)
+
+    return detector_object
 
 # ======================
 # Main functions
@@ -212,10 +210,12 @@ def recognize_from_image(filename, detector):
         pass
 
     # show image
-    im2show = draw_detection(img, boxes, scores, cls_inds)
+    detect_object = convert_to_ailia_detector_object(boxes, scores, cls_inds, img.shape[1], img.shape[0])
+    img = plot_results(detect_object, img, COCO_CATEGORY)
+
     savepath = get_savepath(args.savepath, filename)
     logger.info(f'saved at : {savepath}')
-    cv2.imwrite(savepath, im2show)
+    cv2.imwrite(savepath, img)
 
     if args.profile:
         print(detector.get_summary())
@@ -241,7 +241,10 @@ def recognize_from_video(video, detector):
             break
 
         boxes, scores, cls_inds = detect_objects(img, detector)
-        img = draw_detection(img, boxes, scores, cls_inds)
+
+        # show image
+        detect_object = convert_to_ailia_detector_object(boxes, scores, cls_inds, img.shape[1], img.shape[0])
+        img = plot_results(detect_object, img, COCO_CATEGORY)
         cv2.imshow('frame', img)
 
         # save results

util/detector_utils.py

@@ -163,7 +163,7 @@ def plot_results(detector, img, category, segm_masks=None, logging=True):
         color = colors[idx]
         cv2.rectangle(img, top_left, bottom_right, color, thickness=-1)
 
-        text_color = (255,255,255)
+        text_color = (255,255,255,255)
         cv2.putText(
             img,
             text,