From 45b38b2f347913f95ca6ad5ec3f918e5b757e742 Mon Sep 17 00:00:00 2001 From: guoxiang <1637349696@qq.com> Date: Sat, 13 Apr 2024 22:05:52 +0800 Subject: [PATCH] feat: add transfer keypoint to coordinate not only a image in `util.py` --- src/util.py | 55 ++++++++++++++++++++++++++++++++++++++++------------- 1 file changed, 42 insertions(+), 13 deletions(-) diff --git a/src/util.py b/src/util.py index 16dc24a3..364b0dda 100644 --- a/src/util.py +++ b/src/util.py @@ -8,29 +8,32 @@ import matplotlib.pyplot as plt import cv2 +from src.hand_model_outputsize import model + 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 + 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)) + 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)) + 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)) + 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)) + 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 = {} @@ -38,6 +41,27 @@ def transfer(model, model_weights): transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] return transfered_model_weights + +def transfer2coordinate(candidate, subset): + coordinates = [] + keyMap = ['eyebrow_center', 'neck', + 'left_shoulder', 'left_elbow', 'left_wrist', + 'right_shoulder', 'right_elbow', 'right_wrist', + 'left_hip', 'left_knee', 'left_ankle', + 'right_hip', 'right_knee', 'right_ankle' + ] + for n in range(len(subset)): + keypoint = {} + for i in range(18): + index = int(subset[n][i]) + if index == -1: + continue + x, y = candidate[index][0:2] + key = keyMap[i] + keypoint[key] = (x, y) + coordinates.append(keypoint) + return coordinates + # draw the body keypoint and lims def draw_bodypose(canvas, candidate, subset): stickwidth = 4 @@ -74,6 +98,7 @@ def draw_bodypose(canvas, candidate, subset): # 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]] @@ -90,10 +115,10 @@ def draw_handpose(canvas, all_hand_peaks, show_number=False): for peaks in all_hand_peaks: for ie, e in enumerate(edges): - if np.sum(np.all(peaks[e], axis=1)==0)==0: + 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])) + 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 @@ -104,6 +129,7 @@ def draw_handpose(canvas, all_hand_peaks, show_number=False): 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], \ @@ -111,10 +137,11 @@ def draw_handpose_by_opencv(canvas, peaks, show_number=False): # 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: + 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) + 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 @@ -123,6 +150,7 @@ def draw_handpose_by_opencv(canvas, peaks, show_number=False): 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): @@ -138,7 +166,7 @@ def handDetect(candidate, subset, oriImg): if not (has_left or has_right): continue hands = [] - #left hand + # 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] @@ -189,6 +217,7 @@ def handDetect(candidate, subset, oriImg): ''' return detect_result + # get max index of 2d array def npmax(array): arrayindex = array.argmax(1)