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https://github.com/invoke-ai/InvokeAI
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cleanup: unused util functions
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blessedcoolant
Kent Keirsey
parent
f588b95c7f
commit
f8e566d62a
@ -153,155 +153,3 @@ def draw_facepose(canvas, all_lmks):
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if x > eps and y > eps:
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cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
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return canvas
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# detect hand according to body pose keypoints
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# please refer to
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# https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
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def handDetect(candidate, subset, oriImg):
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# right hand: wrist 4, elbow 3, shoulder 2
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# left hand: wrist 7, elbow 6, shoulder 5
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ratioWristElbow = 0.33
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detect_result = []
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image_height, image_width = oriImg.shape[0:2]
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for person in subset.astype(int):
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# if any of three not detected
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has_left = np.sum(person[[5, 6, 7]] == -1) == 0
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has_right = np.sum(person[[2, 3, 4]] == -1) == 0
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if not (has_left or has_right):
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continue
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hands = []
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# left hand
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if has_left:
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left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]
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x1, y1 = candidate[left_shoulder_index][:2]
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x2, y2 = candidate[left_elbow_index][:2]
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x3, y3 = candidate[left_wrist_index][:2]
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hands.append([x1, y1, x2, y2, x3, y3, True])
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# right hand
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if has_right:
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right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]]
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x1, y1 = candidate[right_shoulder_index][:2]
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x2, y2 = candidate[right_elbow_index][:2]
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x3, y3 = candidate[right_wrist_index][:2]
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hands.append([x1, y1, x2, y2, x3, y3, False])
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for x1, y1, x2, y2, x3, y3, is_left in hands:
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# pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
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# handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
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# handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
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# const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
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# const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
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# handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
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x = x3 + ratioWristElbow * (x3 - x2)
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y = y3 + ratioWristElbow * (y3 - y2)
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distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
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distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
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width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
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# x-y refers to the center --> offset to topLeft point
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# handRectangle.x -= handRectangle.width / 2.f;
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# handRectangle.y -= handRectangle.height / 2.f;
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x -= width / 2
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y -= width / 2 # width = height
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# overflow the image
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if x < 0:
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x = 0
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if y < 0:
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y = 0
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width1 = width
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width2 = width
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if x + width > image_width:
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width1 = image_width - x
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if y + width > image_height:
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width2 = image_height - y
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width = min(width1, width2)
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# the max hand box value is 20 pixels
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if width >= 20:
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detect_result.append([int(x), int(y), int(width), is_left])
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"""
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return value: [[x, y, w, True if left hand else False]].
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width=height since the network require squared input.
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x, y is the coordinate of top left
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"""
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return detect_result
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# Written by Lvmin
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def faceDetect(candidate, subset, oriImg):
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# left right eye ear 14 15 16 17
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detect_result = []
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image_height, image_width = oriImg.shape[0:2]
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for person in subset.astype(int):
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has_head = person[0] > -1
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if not has_head:
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continue
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has_left_eye = person[14] > -1
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has_right_eye = person[15] > -1
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has_left_ear = person[16] > -1
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has_right_ear = person[17] > -1
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if not (has_left_eye or has_right_eye or has_left_ear or has_right_ear):
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continue
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head, left_eye, right_eye, left_ear, right_ear = person[[0, 14, 15, 16, 17]]
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width = 0.0
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x0, y0 = candidate[head][:2]
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if has_left_eye:
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x1, y1 = candidate[left_eye][:2]
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d = max(abs(x0 - x1), abs(y0 - y1))
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width = max(width, d * 3.0)
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if has_right_eye:
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x1, y1 = candidate[right_eye][:2]
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d = max(abs(x0 - x1), abs(y0 - y1))
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width = max(width, d * 3.0)
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if has_left_ear:
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x1, y1 = candidate[left_ear][:2]
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d = max(abs(x0 - x1), abs(y0 - y1))
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width = max(width, d * 1.5)
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if has_right_ear:
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x1, y1 = candidate[right_ear][:2]
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d = max(abs(x0 - x1), abs(y0 - y1))
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width = max(width, d * 1.5)
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x, y = x0, y0
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x -= width
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y -= width
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if x < 0:
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x = 0
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if y < 0:
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y = 0
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width1 = width * 2
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width2 = width * 2
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if x + width > image_width:
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width1 = image_width - x
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if y + width > image_height:
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width2 = image_height - y
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width = min(width1, width2)
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if width >= 20:
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detect_result.append([int(x), int(y), int(width)])
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return detect_result
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# get max index of 2d array
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def npmax(array):
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arrayindex = array.argmax(1)
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arrayvalue = array.max(1)
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i = arrayvalue.argmax()
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j = arrayindex[i]
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return i, j
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