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| import numpy as np | |
| import math | |
| import cv2 | |
| import matplotlib | |
| from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas | |
| from matplotlib.figure import Figure | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import cv2 | |
| def padRightDownCorner(img, stride, padValue): | |
| h = img.shape[0] | |
| w = img.shape[1] | |
| pad = 4 * [None] | |
| pad[0] = 0 # up | |
| pad[1] = 0 # left | |
| pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down | |
| pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right | |
| img_padded = img | |
| pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1)) | |
| img_padded = np.concatenate((pad_up, img_padded), axis=0) | |
| pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1)) | |
| img_padded = np.concatenate((pad_left, img_padded), axis=1) | |
| pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1)) | |
| img_padded = np.concatenate((img_padded, pad_down), axis=0) | |
| pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1)) | |
| img_padded = np.concatenate((img_padded, pad_right), axis=1) | |
| return img_padded, pad | |
| # transfer caffe model to pytorch which will match the layer name | |
| def transfer(model, model_weights): | |
| transfered_model_weights = {} | |
| for weights_name in model.state_dict().keys(): | |
| transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] | |
| return transfered_model_weights | |
| # draw the body keypoint and lims | |
| def draw_bodypose(canvas, candidate, subset): | |
| stickwidth = 4 | |
| 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(18): | |
| for n in range(len(subset)): | |
| index = int(subset[n][i]) | |
| if index == -1: | |
| continue | |
| x, y = candidate[index][0:2] | |
| cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1) | |
| for i in range(17): | |
| for n in range(len(subset)): | |
| index = subset[n][np.array(limbSeq[i]) - 1] | |
| if -1 in index: | |
| continue | |
| cur_canvas = canvas.copy() | |
| Y = candidate[index.astype(int), 0] | |
| X = candidate[index.astype(int), 1] | |
| 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])) | |
| polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) | |
| cv2.fillConvexPoly(cur_canvas, polygon, colors[i]) | |
| canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0) | |
| # plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]]) | |
| # plt.imshow(canvas[:, :, [2, 1, 0]]) | |
| return canvas | |
| def draw_handpose(canvas, all_hand_peaks, show_number=False): | |
| 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]] | |
| fig = Figure(figsize=plt.figaspect(canvas)) | |
| fig.subplots_adjust(0, 0, 1, 1) | |
| fig.subplots_adjust(bottom=0, top=1, left=0, right=1) | |
| bg = FigureCanvas(fig) | |
| ax = fig.subplots() | |
| ax.axis('off') | |
| ax.imshow(canvas) | |
| width, height = ax.figure.get_size_inches() * ax.figure.get_dpi() | |
| for peaks in all_hand_peaks: | |
| for ie, e in enumerate(edges): | |
| if np.sum(np.all(peaks[e], axis=1)==0)==0: | |
| x1, y1 = peaks[e[0]] | |
| x2, y2 = peaks[e[1]] | |
| ax.plot([x1, x2], [y1, y2], color=matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])) | |
| for i, keyponit in enumerate(peaks): | |
| x, y = keyponit | |
| ax.plot(x, y, 'r.') | |
| if show_number: | |
| ax.text(x, y, str(i)) | |
| bg.draw() | |
| canvas = np.fromstring(bg.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3) | |
| return canvas | |
| # image drawed by opencv is not good. | |
| def draw_handpose_by_opencv(canvas, peaks, show_number=False): | |
| 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]] | |
| # cv2.rectangle(canvas, (x, y), (x+w, y+w), (0, 255, 0), 2, lineType=cv2.LINE_AA) | |
| # cv2.putText(canvas, 'left' if is_left else 'right', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) | |
| for ie, e in enumerate(edges): | |
| if np.sum(np.all(peaks[e], axis=1)==0)==0: | |
| x1, y1 = peaks[e[0]] | |
| x2, y2 = peaks[e[1]] | |
| cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])*255, thickness=2) | |
| for i, keyponit in enumerate(peaks): | |
| x, y = keyponit | |
| cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1) | |
| if show_number: | |
| cv2.putText(canvas, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), lineType=cv2.LINE_AA) | |
| 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 | |
| # 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 | |