用深度學習及opencv進行手關鍵點檢測
1.下載模型
運行getModels.sh來下載手模型，配置到hand/目錄下。
sudo chmod a+x getModels.sh
./getModels.sh
2.加載模型及圖像（Python代碼）
protoFile = "hand/pose_deploy.prototxt"
weightsFile = "hand/pose_iter_102000.caffemodel"
nPoints = 22
frame = cv2.imread("hand.jpg")
net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)
3.運行預測（Python代碼）
inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),
(0, 0, 0), swapRB=False, crop=False)
net.setInput(inpBlob)
output = net.forward()
4.顯示檢測結果
輸出包括了22矩陣點，用opendv的minmaxLoc函數。
points = []
for i in range(nPoints):
# confidence map of corresponding body's part.
probMap = output[0, i, :, :]
probMap = cv2.resize(probMap, (frameWidth, frameHeight))
# Find global maxima of the probMap.
minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
if prob > threshold :
cv2.circle(frameCopy, (int(point[0]), int(point[1])), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)
cv2.putText(frameCopy, "{}".format(i), (int(point[0]), int(point[1])), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, lineType=cv2.LINE_AA)
# Add the point to the list if the probability is greater than the threshold
points.append((int(point[0]), int(point[1])))
else :
points.append(None)cv2.imshow('Output-Keypoints', frameCopy)
5.描畫手部骨骼
# Draw Skeleton
for pair in POSE_PAIRS:
partA = pair[0]
partB = pair[1]
if points[partA] and points[partB]:
cv2.line(frame, points[partA], points[partB], (0, 255, 255), 2)
cv2.circle(frame, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)
cv2.imshow('Output-Skeleton', frame)
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