Add human-pose-estimation-0001 support

resources/nn/human-pose-estimation-0001/handler.py

@@ -0,0 +1,195 @@
+import cv2
+import numpy as np
+
+from depthai_sdk import toTensorResult, Previews
+
+keypointsMapping = ['Nose', 'Neck', 'R-Sho', 'R-Elb', 'R-Wr', 'L-Sho', 'L-Elb', 'L-Wr', 'R-Hip', 'R-Knee', 'R-Ank',
+                    'L-Hip', 'L-Knee', 'L-Ank', 'R-Eye', 'L-Eye', 'R-Ear', 'L-Ear']
+POSE_PAIRS = [[1, 2], [1, 5], [2, 3], [3, 4], [5, 6], [6, 7], [1, 8], [8, 9], [9, 10], [1, 11], [11, 12], [12, 13],
+              [1, 0], [0, 14], [14, 16], [0, 15], [15, 17], [2, 17], [5, 16]]
+mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], [23, 24], [25, 26], [27, 28],
+          [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], [55, 56], [37, 38], [45, 46]]
+colors = [[0, 100, 255], [0, 100, 255], [0, 255, 255], [0, 100, 255], [0, 255, 255], [0, 100, 255], [0, 255, 0],
+          [255, 200, 100], [255, 0, 255], [0, 255, 0], [255, 200, 100], [255, 0, 255], [0, 0, 255], [255, 0, 0],
+          [200, 200, 0], [255, 0, 0], [200, 200, 0], [0, 0, 0]]
+
+
+def getKeypoints(probMap, threshold=0.2):
+    mapSmooth = cv2.GaussianBlur(probMap, (3, 3), 0, 0)
+    mapMask = np.uint8(mapSmooth > threshold)
+    keypoints = []
+    contours = None
+    try:
+        # OpenCV4.x
+        contours, _ = cv2.findContours(mapMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    except:
+        # OpenCV3.x
+        _, contours, _ = cv2.findContours(mapMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+    for cnt in contours:
+        blobMask = np.zeros(mapMask.shape)
+        blobMask = cv2.fillConvexPoly(blobMask, cnt, 1)
+        maskedProbMap = mapSmooth * blobMask
+        _, maxVal, _, maxLoc = cv2.minMaxLoc(maskedProbMap)
+        keypoints.append(maxLoc + (probMap[maxLoc[1], maxLoc[0]],))
+
+    return keypoints
+
+
+def getValidPairs(outputs, w, h, detectedKeypoints):
+    validPairs = []
+    invalidPairs = []
+    nInterpSamples = 10
+    pafScoreTh = 0.2
+    confTh = 0.4
+    for k in range(len(mapIdx)):
+
+        pafA = outputs[0, mapIdx[k][0], :, :]
+        pafB = outputs[0, mapIdx[k][1], :, :]
+        pafA = cv2.resize(pafA, (w, h))
+        pafB = cv2.resize(pafB, (w, h))
+        candA = detectedKeypoints[POSE_PAIRS[k][0]]
+
+        candB = detectedKeypoints[POSE_PAIRS[k][1]]
+
+        nA = len(candA)
+        nB = len(candB)
+
+        if (nA != 0 and nB != 0):
+            validPair = np.zeros((0, 3))
+            for i in range(nA):
+                maxJ = -1
+                maxScore = -1
+                found = 0
+                for j in range(nB):
+                    d_ij = np.subtract(candB[j][:2], candA[i][:2])
+                    norm = np.linalg.norm(d_ij)
+                    if norm:
+                        d_ij = d_ij / norm
+                    else:
+                        continue
+                    interp_coord = list(zip(np.linspace(candA[i][0], candB[j][0], num=nInterpSamples),
+                                            np.linspace(candA[i][1], candB[j][1], num=nInterpSamples)))
+                    pafInterp = []
+                    for k in range(len(interp_coord)):
+                        pafInterp.append([pafA[int(round(interp_coord[k][1])), int(round(interp_coord[k][0]))],
+                                           pafB[int(round(interp_coord[k][1])), int(round(interp_coord[k][0]))]])
+                    pafScores = np.dot(pafInterp, d_ij)
+                    avgPafScore = sum(pafScores) / len(pafScores)
+
+                    if (len(np.where(pafScores > pafScoreTh)[0]) / nInterpSamples) > confTh:
+                        if avgPafScore > maxScore:
+                            maxJ = j
+                            maxScore = avgPafScore
+                            found = 1
+                if found:
+                    validPair = np.append(validPair, [[candA[i][3], candB[maxJ][3], maxScore]], axis=0)
+
+            validPairs.append(validPair)
+        else:
+            invalidPairs.append(k)
+            validPairs.append([])
+    return validPairs, invalidPairs
+
+
+def getPersonwiseKeypoints(validPairs, invalidPairs, keypointsList):
+    personwiseKeypoints = -1 * np.ones((0, 19))
+
+    for k in range(len(mapIdx)):
+        if k not in invalidPairs:
+            partAs = validPairs[k][:, 0]
+            partBs = validPairs[k][:, 1]
+            indexA, indexB = np.array(POSE_PAIRS[k])
+
+            for i in range(len(validPairs[k])):
+                found = 0
+                personIdx = -1
+                for j in range(len(personwiseKeypoints)):
+                    if personwiseKeypoints[j][indexA] == partAs[i]:
+                        personIdx = j
+                        found = 1
+                        break
+
+                if found:
+                    personwiseKeypoints[personIdx][indexB] = partBs[i]
+                    personwiseKeypoints[personIdx][-1] += keypointsList[partBs[i].astype(int), 2] + validPairs[k][i][
+                        2]
+
+                elif not found and k < 17:
+                    row = -1 * np.ones(19)
+                    row[indexA] = partAs[i]
+                    row[indexB] = partBs[i]
+                    row[-1] = sum(keypointsList[validPairs[k][i, :2].astype(int), 2]) + validPairs[k][i][2]
+                    personwiseKeypoints = np.vstack([personwiseKeypoints, row])
+    return personwiseKeypoints
+
+
+threshold = 0.3
+nPoints = 18
+detectedKeypoints = []
+
+
+def decode(nnManager, packet):
+    heatmaps = np.array(packet.getLayerFp16('Mconv7_stage2_L2')).reshape((1, 19, 32, 57)).astype('float32')
+    pafs = np.array(packet.getLayerFp16('Mconv7_stage2_L1')).reshape((1, 38, 32, 57)).astype('float32')
+    outputs = np.concatenate((heatmaps, pafs), axis=1)
+    w, h = nnManager.inputSize
+
+    detectedKeypoints = []
+    keypointsList = np.zeros((0, 3))
+    keypointId = 0
+
+    for part in range(nPoints):
+        probMap = outputs[0, part, :, :]
+        probMap = cv2.resize(probMap, (w, h))  # (456, 256)
+        keypoints = getKeypoints(probMap, threshold)
+        keypointsWithId = []
+
+        for i in range(len(keypoints)):
+            keypointsWithId.append(keypoints[i] + (keypointId,))
+            keypointsList = np.vstack([keypointsList, keypoints[i]])
+            keypointId += 1
+
+        detectedKeypoints.append(keypointsWithId)
+
+    validPairs, invalidPairs = getValidPairs(outputs, w, h, detectedKeypoints)
+    personwiseKeypoints = getPersonwiseKeypoints(validPairs, invalidPairs, keypointsList)
+    keypointsLimbs = [detectedKeypoints, personwiseKeypoints, keypointsList]
+
+    return keypointsLimbs
+
+
+def draw(nnManager, keypointsLimbs, frames):
+    for name, frame in frames:
+        if name == "color" and nnManager.source == "color" and not nnManager._fullFov:
+            scaleFactor = frame.shape[0] / nnManager.inputSize[1]
+            offsetW = int(frame.shape[1] - nnManager.inputSize[0] * scaleFactor) // 2
+
+            def scale(point):
+                return int(point[0] * scaleFactor) + offsetW, int(point[1] * scaleFactor)
+        elif name in (Previews.color.name, Previews.nnInput.name, "host"):
+            scaleH = frame.shape[0] / nnManager.inputSize[1]
+            scaleW = frame.shape[1] / nnManager.inputSize[0]
+
+            def scale(point):
+                return int(point[0] * scaleW), int(point[1] * scaleH)
+        else:
+            continue
+
+        if len(keypointsLimbs) == 3:
+            detectedKeypoints = keypointsLimbs[0]
+            personwiseKeypoints = keypointsLimbs[1]
+            keypointsList = keypointsLimbs[2]
+
+            for i in range(nPoints):
+                for j in range(len(detectedKeypoints[i])):
+                    cv2.circle(frame, scale(detectedKeypoints[i][j][0:2]), 5, colors[i], -1, cv2.LINE_AA)
+            for i in range(17):
+                for n in range(len(personwiseKeypoints)):
+                    index = personwiseKeypoints[n][np.array(POSE_PAIRS[i])]
+                    if -1 in index:
+                        continue
+                    B = np.int32(keypointsList[index.astype(int), 0])
+                    A = np.int32(keypointsList[index.astype(int), 1])
+                    cv2.line(frame, scale((B[0], A[0])), scale((B[1], A[1])), colors[i], 3, cv2.LINE_AA)
+

resources/nn/human-pose-estimation-0001/human-pose-estimation-0001.json

@@ -0,0 +1,8 @@
+{
+    "nn_config": {
+        "output_format" : "raw",
+        "input_size": "456x256"
+    },
+    "handler": "handler.py"
+}
+