cleanup: unused util functions

invokeai/backend/image_util/dwpose/utils.py

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