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Handling Input shape error while using .rknn model and Handling No detections error in the code. #242

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Handling Input shape error while using .rknn model and Handling No detections error in the code. #242

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Input Dimesion error while using rknn model
manikanta5557 Dec 17, 2024
f3319a2
Handling input shape error while using .rknn model and handling no de…
manikanta5557 Dec 17, 2024
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28 changes: 17 additions & 11 deletions examples/yolov8_seg/python/yolov8_seg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -193,13 +193,17 @@ def sp_flatten(_in):
return boxes, classes, scores, seg_img

def draw(image, boxes, scores, classes):
for box, score, cl in zip(boxes, scores, classes):
top, left, right, bottom = [int(_b) for _b in box]
print("%s @ (%d %d %d %d) %.3f" % (CLASSES[cl], top, left, right, bottom, score))
cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
(top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)

#loop through each detected object
if scores is not None:
for box, score, cl in zip(boxes, scores, classes):
top, left, right, bottom = [int(_b) for _b in box]
print("%s @ (%d %d %d %d) %.3f" % (CLASSES[cl], top, left, right, bottom, score))
cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
(top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
#no object is detected in the image
else:
print("No objects found")
def _crop_mask(masks, boxes):
"""
"Crop" predicted masks by zeroing out everything not in the predicted bbox.
Expand Down Expand Up @@ -305,7 +309,7 @@ def img_check(path):
input_data = input_data.reshape(1,*input_data.shape).astype(np.float32)
input_data = input_data/255.
else:
input_data = img
input_data = np.expand_dims(img,0)

outputs = model.run([input_data])
boxes, classes, scores, seg_img = post_process(outputs)
Expand All @@ -314,12 +318,14 @@ def img_check(path):
real_boxs = co_helper.get_real_box(boxes)
real_segs = co_helper.get_real_seg(seg_img)
img_p = merge_seg(img_src, real_segs, classes)

# if there is no object detected in the image
img_p = img_src.copy()

if args.img_show or args.img_save:
print('\n\nIMG: {}'.format(img_name))

draw(img_p, real_boxs, scores, classes)
#draw masks only if there are any objects detected in the image
if boxes is not None:
draw(img_p, real_boxs, scores, classes)

if args.img_save:
if not os.path.exists('./result'):
Expand Down