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| import math | |
| import numpy as np | |
| import cv2 | |
| eps = 0.01 | |
| def smart_width(d): | |
| if d<5: | |
| return 1 | |
| elif d<10: | |
| return 2 | |
| elif d<20: | |
| return 3 | |
| elif d<40: | |
| return 4 | |
| elif d<80: | |
| return 5 | |
| elif d<160: | |
| return 6 | |
| elif d<320: | |
| return 7 | |
| else: | |
| return 8 | |
| def draw_bodypose(canvas, candidate, subset): | |
| H, W, C = canvas.shape | |
| candidate = np.array(candidate) | |
| subset = np.array(subset) | |
| limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ | |
| [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ | |
| [1, 16], [16, 18], [3, 17], [6, 18]] | |
| colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ | |
| [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ | |
| [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] | |
| for i in range(17): | |
| for n in range(len(subset)): | |
| index = subset[n][np.array(limbSeq[i]) - 1] | |
| if -1 in index: | |
| continue | |
| Y = candidate[index.astype(int), 0] * float(W) | |
| X = candidate[index.astype(int), 1] * float(H) | |
| mX = np.mean(X) | |
| mY = np.mean(Y) | |
| length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 | |
| angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) | |
| width = smart_width(length) | |
| polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), width), int(angle), 0, 360, 1) | |
| cv2.fillConvexPoly(canvas, polygon, colors[i]) | |
| canvas = (canvas * 0.6).astype(np.uint8) | |
| for i in range(18): | |
| for n in range(len(subset)): | |
| index = int(subset[n][i]) | |
| if index == -1: | |
| continue | |
| x, y = candidate[index][0:2] | |
| x = int(x * W) | |
| y = int(y * H) | |
| radius = 4 | |
| cv2.circle(canvas, (int(x), int(y)), radius, colors[i], thickness=-1) | |
| return canvas | |
| def draw_handpose(canvas, all_hand_peaks): | |
| import matplotlib | |
| H, W, C = canvas.shape | |
| edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \ | |
| [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] | |
| # (person_number*2, 21, 2) | |
| for i in range(len(all_hand_peaks)): | |
| peaks = all_hand_peaks[i] | |
| peaks = np.array(peaks) | |
| for ie, e in enumerate(edges): | |
| x1, y1 = peaks[e[0]] | |
| x2, y2 = peaks[e[1]] | |
| x1 = int(x1 * W) | |
| y1 = int(y1 * H) | |
| x2 = int(x2 * W) | |
| y2 = int(y2 * H) | |
| if x1 > eps and y1 > eps and x2 > eps and y2 > eps: | |
| length = ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5 | |
| width = smart_width(length) | |
| cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=width) | |
| for _, keyponit in enumerate(peaks): | |
| x, y = keyponit | |
| x = int(x * W) | |
| y = int(y * H) | |
| if x > eps and y > eps: | |
| radius = 3 | |
| cv2.circle(canvas, (x, y), radius, (0, 0, 255), thickness=-1) | |
| return canvas | |
| def draw_facepose(canvas, all_lmks): | |
| H, W, C = canvas.shape | |
| for lmks in all_lmks: | |
| lmks = np.array(lmks) | |
| for lmk in lmks: | |
| x, y = lmk | |
| x = int(x * W) | |
| y = int(y * H) | |
| if x > eps and y > eps: | |
| radius = 3 | |
| cv2.circle(canvas, (x, y), radius, (255, 255, 255), thickness=-1) | |
| return canvas | |
| # Calculate the resolution | |
| def size_calculate(h, w, resolution): | |
| H = float(h) | |
| W = float(w) | |
| # resize the short edge to the resolution | |
| k = float(resolution) / min(H, W) # short edge | |
| H *= k | |
| W *= k | |
| # resize to the nearest integer multiple of 64 | |
| H = int(np.round(H / 64.0)) * 64 | |
| W = int(np.round(W / 64.0)) * 64 | |
| return H, W | |
| def warpAffine_kps(kps, M): | |
| a = M[:,:2] | |
| t = M[:,2] | |
| kps = np.dot(kps, a.T) + t | |
| return kps | |