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import math |
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import numpy as np |
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import cv2 |
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eps = 0.01 |
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def smart_width(d): |
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if d<5: |
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return 1 |
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elif d<10: |
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return 2 |
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elif d<20: |
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return 3 |
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elif d<40: |
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return 4 |
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elif d<80: |
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return 5 |
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elif d<160: |
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return 6 |
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elif d<320: |
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return 7 |
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else: |
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return 8 |
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def draw_bodypose(canvas, candidate, subset): |
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H, W, C = canvas.shape |
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candidate = np.array(candidate) |
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subset = np.array(subset) |
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limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ |
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[10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ |
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[1, 16], [16, 18], [3, 17], [6, 18]] |
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colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ |
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[0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ |
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[170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] |
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for i in range(17): |
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for n in range(len(subset)): |
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index = subset[n][np.array(limbSeq[i]) - 1] |
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if -1 in index: |
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continue |
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Y = candidate[index.astype(int), 0] * float(W) |
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X = candidate[index.astype(int), 1] * float(H) |
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mX = np.mean(X) |
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mY = np.mean(Y) |
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length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 |
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angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) |
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width = smart_width(length) |
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polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), width), int(angle), 0, 360, 1) |
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cv2.fillConvexPoly(canvas, polygon, colors[i]) |
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canvas = (canvas * 0.6).astype(np.uint8) |
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for i in range(18): |
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for n in range(len(subset)): |
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index = int(subset[n][i]) |
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if index == -1: |
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continue |
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x, y = candidate[index][0:2] |
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x = int(x * W) |
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y = int(y * H) |
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radius = 4 |
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cv2.circle(canvas, (int(x), int(y)), radius, colors[i], thickness=-1) |
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return canvas |
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def draw_handpose(canvas, all_hand_peaks): |
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import matplotlib |
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H, W, C = canvas.shape |
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edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \ |
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[10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] |
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for i in range(len(all_hand_peaks)): |
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peaks = all_hand_peaks[i] |
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peaks = np.array(peaks) |
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for ie, e in enumerate(edges): |
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x1, y1 = peaks[e[0]] |
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x2, y2 = peaks[e[1]] |
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x1 = int(x1 * W) |
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y1 = int(y1 * H) |
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x2 = int(x2 * W) |
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y2 = int(y2 * H) |
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if x1 > eps and y1 > eps and x2 > eps and y2 > eps: |
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length = ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5 |
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width = smart_width(length) |
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cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=width) |
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for _, keyponit in enumerate(peaks): |
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x, y = keyponit |
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x = int(x * W) |
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y = int(y * H) |
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if x > eps and y > eps: |
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radius = 3 |
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cv2.circle(canvas, (x, y), radius, (0, 0, 255), thickness=-1) |
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return canvas |
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def draw_facepose(canvas, all_lmks): |
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H, W, C = canvas.shape |
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for lmks in all_lmks: |
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lmks = np.array(lmks) |
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for lmk in lmks: |
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x, y = lmk |
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x = int(x * W) |
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y = int(y * H) |
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if x > eps and y > eps: |
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radius = 3 |
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cv2.circle(canvas, (x, y), radius, (255, 255, 255), thickness=-1) |
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return canvas |
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def size_calculate(h, w, resolution): |
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H = float(h) |
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W = float(w) |
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k = float(resolution) / min(H, W) |
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H *= k |
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W *= k |
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H = int(np.round(H / 64.0)) * 64 |
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W = int(np.round(W / 64.0)) * 64 |
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return H, W |
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def warpAffine_kps(kps, M): |
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a = M[:,:2] |
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t = M[:,2] |
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kps = np.dot(kps, a.T) + t |
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return kps |
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