OpenCV DNN module does not predict correct detections for YOLOv3. Whereas the Darknet detector detects correctly.
System information (version)
I tested results with compiled OpenCV from source code and I tried with pre-built opencv-python also but OpenCV DNN detects wrong objects.
Whereas Darknet detector detects correctly.
Correct detection with darknet detector:
Wrong detection with OpenCV DNN module:
YOLOv3 network and model weights are from https://github.com/AlexeyAB/darknet
- modelWeights:
yolov3.weights
- modelConfiguration:
yolov3.cfg
- ClassesFile:
coco.names
Detailed description
Please see the output images at the link appended below. (correct detection with darknet detector)
compared with the wrong detection (with OpenCV DNN)
Output images available in this Google Drive link.
The above link includes test-images also for steps to test
# The following code is partial to demonstrate steps
net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
layerNames = net.getLayerNames()
layerNames = [layerNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# construct a blob from the input frame and then perform a forward pass of the YOLO object detector,
# giving us our bounding boxes and associated probabilities
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
swapRB=True, crop=False)
net.setInput(blob)
layerOutputs = net.forward(layerNames)
# initialize our lists of detected bounding boxes, confidences,
# and class IDs, respectively
boxes = []
confidences = []
classIDs = []
# loop over each of the layer outputs
for output in layerOutputs:
# loop over each of the detections
for detection in output:
# extract the class ID and confidence (i.e., probability)
# of the current object detection
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# filter out weak predictions by ensuring the detected
# probability is greater than the minimum probability
if confidence > args["confidence"]:
# scale the bounding box coordinates back relative to
# the size of the image, keeping in mind that YOLO
# actually returns the center (x, y)-coordinates of
# the bounding box followed by the boxes' width and
# height
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
# use the center (x, y)-coordinates to derive the top
# and and left corner of the bounding box
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
# update our list of bounding box coordinates,
# confidences, and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
# apply non-maxima suppression to suppress weak, overlapping
# bounding boxes
idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"])
dets = []
if len(idxs) > 0:
# loop over the indexes we are keeping
for i in idxs.flatten():
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
dets.append([x, y, x+w, y+h, confidences[i]])
if len(boxes) > 0:
i = int(0)
for box in boxes:
# extract the bounding box coordinates
(x, y) = (int(box[0]), int(box[1]))
(w, h) = (int(box[2]), int(box[3]))
# draw a bounding box rectangle and label on the image
# color = [int(c) for c in COLORS[classIDs[i]]]
# cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
color = [int(c) for c in COLORS[indexIDs[i] % len(COLORS)]]
cv2.rectangle(frame, (x, y), (w, h), color, 2)
cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.75, color, 2)# 1.0 0.5, color, 2)
i += 1
cv2.imwrite("detection-output.jpg", frame)
question from:
https://stackoverflow.com/questions/65557322/how-to-test-opencv-dnn-module-accuracy-it-does-not-predict-correct-detections-f 
