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Merge pull request #26486 from gursimarsingh:object_detection_engine_update

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Code Fixes and changed post processing based on models.yml in Object Detection Sample #26486

## Major Changes

1. Changes to add findModel support for config file in models like yolov4, yolov4-tiny, yolov3, ssd_caffe, tiny-yolo-voc, ssd_tf and faster_rcnn_tf.
2. Added new model and config download links for ssd_caffe, as previous links were not working.
3. Switched to DNN ENGINE_CLASSIC for non-cpu convig as new engine does not support it.
4. Fixes in python sample related to yolov5 usage.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Gursimar Singh
2024-12-02 11:35:25 +05:30
committed by GitHub
parent b476ed6d06
commit efbe580ff3
5 changed files with 110 additions and 74 deletions
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samples/dnn/object_detection.py
+52 -33
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@@ -37,9 +37,6 @@ parser.add_argument('--out_tf_graph', default='graph.pbtxt',
help='For models from TensorFlow Object Detection API, you may '
'pass a .config file which was used for training through --config '
'argument. This way an additional .pbtxt file with TensorFlow graph will be created.')
parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'darknet', 'dldt', 'onnx'],
help='Optional name of an origin framework of the model. '
'Detect it automatically if it does not set.')
parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
parser.add_argument('--nms', type=float, default=0.4, help='Non-maximum suppression threshold')
parser.add_argument('--backend', default="default", type=str, choices=backends,
@@ -76,8 +73,9 @@ if args.alias is None or hasattr(args, 'help'):
args.model = findModel(args.model, args.sha1)
if args.config is not None:
args.config = findFile(args.config)
args.labels = findFile(args.labels)
args.config = findModel(args.config, args.config_sha1)
if args.labels is not None:
args.labels = findFile(args.labels)
# If config specified, try to load it as TensorFlow Object Detection API's pipeline.
config = readTextMessage(args.config)
@@ -100,7 +98,10 @@ if args.labels:
labels = f.read().rstrip('\n').split('\n')
# Load a network
net = cv.dnn.readNet(args.model, args.config, args.framework)
engine = cv.dnn.ENGINE_AUTO
if args.backend != "default" or args.target != "cpu":
engine = cv.dnn.ENGINE_CLASSIC
net = cv.dnn.readNet(args.model, args.config, "", engine)
net.setPreferableBackend(get_backend_id(args.backend))
net.setPreferableTarget(get_target_id(args.target))
outNames = net.getUnconnectedOutLayersNames()
@@ -126,14 +127,10 @@ def postprocess(frame, outs):
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]
layerNames = net.getLayerNames()
lastLayerId = net.getLayerId(layerNames[-1])
lastLayer = net.getLayer(lastLayerId)
classIds = []
confidences = []
boxes = []
if lastLayer.type == 'DetectionOutput':
if args.postprocessing == 'ssd':
# Network produces output blob with a shape 1x1xNx7 where N is a number of
# detections and an every detection is a vector of values
# [batchId, classId, confidence, left, top, right, bottom]
@@ -157,21 +154,12 @@ def postprocess(frame, outs):
classIds.append(int(detection[1]) - 1) # Skip background label
confidences.append(float(confidence))
boxes.append([left, top, width, height])
elif lastLayer.type == 'Region' or args.postprocessing == 'yolov8':
# Network produces output blob with a shape NxC where N is a number of
# detected objects and C is a number of classes + 4 where the first 4
# numbers are [center_x, center_y, width, height]
if args.postprocessing == 'yolov8':
box_scale_w = frameWidth / args.width
box_scale_h = frameHeight / args.height
else:
box_scale_w = frameWidth
box_scale_h = frameHeight
elif args.postprocessing == 'darknet':
box_scale_w = frameWidth
box_scale_h = frameHeight
for out in outs:
if args.postprocessing == 'yolov8':
out = out[0].transpose(1, 0)
for detection in out:
scores = detection[4:]
if args.background_label_id >= 0:
@@ -188,13 +176,47 @@ def postprocess(frame, outs):
classIds.append(classId)
confidences.append(float(confidence))
boxes.append([left, top, width, height])
elif args.postprocessing == 'yolov8' or args.postprocessing == 'yolov5':
# Network produces output blob with a shape NxC where N is a number of
# detected objects and C is a number of classes + 4 where the first 4
# numbers are [center_x, center_y, width, height]
box_scale_w = frameWidth / args.width
box_scale_h = frameHeight / args.height
for out in outs:
if args.postprocessing == 'yolov8':
out = out[0].transpose(1, 0)
else: # YOLOv5, no transposition needed
out = out[0]
for detection in out:
if args.postprocessing == 'yolov8':
scores = detection[4:]
obj_conf = 1
else:
scores = detection[5:]
obj_conf = detection[4]
classId = np.argmax(scores)
confidence = scores[classId]*obj_conf
if confidence > confThreshold:
center_x = int(detection[0] * box_scale_w)
center_y = int(detection[1] * box_scale_h)
width = int(detection[2] * box_scale_w)
height = int(detection[3] * box_scale_h)
left = int(center_x - width / 2)
top = int(center_y - height / 2)
classIds.append(classId)
confidences.append(float(confidence))
boxes.append([left, top, width, height])
else:
print('Unknown output layer type: ' + lastLayer.type)
print('Unknown postprocessing method: ' + args.postprocessing)
exit()
# NMS is used inside Region layer only on DNN_BACKEND_OPENCV for another backends we need NMS in sample
# or NMS is required if number of outputs > 1
if len(outNames) > 1 or (lastLayer.type == 'Region' or args.postprocessing == 'yolov8') and args.backend != cv.dnn.DNN_BACKEND_OPENCV:
if len(outNames) > 1 or (args.postprocessing == 'darknet' or args.postprocessing == 'yolov8' or args.postprocessing == 'yolov5') and args.backend != cv.dnn.DNN_BACKEND_OPENCV:
indices = []
classIds = np.array(classIds)
boxes = np.array(boxes)
@@ -308,14 +330,11 @@ def processingThreadBody():
# Create a 4D blob from a frame.
inpWidth = args.width if args.width else frameWidth
inpHeight = args.height if args.height else frameHeight
blob = cv.dnn.blobFromImage(frame, size=(inpWidth, inpHeight), swapRB=args.rgb, ddepth=cv.CV_32F)
blob = cv.dnn.blobFromImage(frame, scalefactor=args.scale, mean=args.mean, size=(inpWidth, inpHeight), swapRB=args.rgb, ddepth=cv.CV_32F)
processedFramesQueue.put(frame)
# Run a model
net.setInput(blob, scalefactor=args.scale, mean=args.mean)
if net.getLayer(0).outputNameToIndex('im_info') != -1: # Faster-RCNN or R-FCN
frame = cv.resize(frame, (inpWidth, inpHeight))
net.setInput(np.array([[inpHeight, inpWidth, 1.6]], dtype=np.float32), 'im_info')
net.setInput(blob)
if asyncN:
futureOutputs.append(net.forwardAsync())
@@ -385,9 +404,9 @@ else:
inpWidth = args.width if args.width else frameWidth
inpHeight = args.height if args.height else frameHeight
blob = cv.dnn.blobFromImage(frame, size=(inpWidth, inpHeight), swapRB=args.rgb, ddepth=cv.CV_32F)
blob = cv.dnn.blobFromImage(frame, scalefactor=args.scale, mean=args.mean, size=(inpWidth, inpHeight), swapRB=args.rgb, ddepth=cv.CV_32F)
net.setInput(blob, scalefactor=args.scale, mean=args.mean)
net.setInput(blob)
outs = net.forward(outNames)
boxes, classIds, confidences, indices = postprocess(frame, outs)