larroy commented on a change in pull request #9784: Fix for the case where
there are no detections
URL: https://github.com/apache/incubator-mxnet/pull/9784#discussion_r169284102
##########
File path: example/ssd/detect/detector.py
##########
@@ -136,31 +132,52 @@ class names
height = img.shape[0]
width = img.shape[1]
colors = dict()
- for i in range(dets.shape[0]):
- cls_id = int(dets[i, 0])
- if cls_id >= 0:
- score = dets[i, 1]
- if score > thresh:
- if cls_id not in colors:
- colors[cls_id] = (random.random(), random.random(),
random.random())
- xmin = int(dets[i, 2] * width)
- ymin = int(dets[i, 3] * height)
- xmax = int(dets[i, 4] * width)
- ymax = int(dets[i, 5] * height)
- rect = plt.Rectangle((xmin, ymin), xmax - xmin,
- ymax - ymin, fill=False,
- edgecolor=colors[cls_id],
- linewidth=3.5)
- plt.gca().add_patch(rect)
- class_name = str(cls_id)
- if classes and len(classes) > cls_id:
- class_name = classes[cls_id]
- plt.gca().text(xmin, ymin - 2,
- '{:s} {:.3f}'.format(class_name, score),
- bbox=dict(facecolor=colors[cls_id],
alpha=0.5),
+ for det in dets:
+ (klass, score, x0, y0, x1, y1) = det
+ if score < thresh:
+ continue
+ cls_id = int(klass)
+ if cls_id not in colors:
+ colors[cls_id] = (random.random(), random.random(),
random.random())
+ xmin = int(x0 * width)
+ ymin = int(y0 * height)
+ xmax = int(x1 * width)
+ ymax = int(y1 * height)
+ rect = plt.Rectangle((xmin, ymin), xmax - xmin,
+ ymax - ymin, fill=False,
+ edgecolor=colors[cls_id],
+ linewidth=3.5)
+ plt.gca().add_patch(rect)
+ class_name = str(cls_id)
+ if classes and len(classes) > cls_id:
+ class_name = classes[cls_id]
+ plt.gca().text(xmin, ymin - 2,
+ '{:s} {:.3f}'.format(class_name, score),
+ bbox=dict(facecolor=colors[cls_id], alpha=0.5),
fontsize=12, color='white')
plt.show()
+ @staticmethod
+ def filter_positive_detections(detections):
+ """
+ First column (class id) is -1 for negative detections
+ :param detections:
+ :return:
+ """
+ class_idx = 0
+ assert(isinstance(detections, mx.nd.NDArray) or isinstance(detections,
np.ndarray))
+ detections_per_image = []
+ # for each image
+ for i in range(detections.shape[0]):
+ result = []
+ det = detections[i, :, :]
+ for obj in det:
+ if obj[class_idx] >= 0:
Review comment:
filter_positive_detections doesn't need any member data, it's a pure
function, reflected by @staticmethod so one knows that is not going to mutate
class state. Anyway, maybe is my pedantic defensive programming from other
safer programming languages, in Python seems everyone codes however they want.
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