def main(oriImg):
shape0 = oriImg.shape
candidate, subset = body_estimation(oriImg)
canvas = copy.deepcopy(oriImg)
shape1 = canvas.shape
canvas = util.draw_bodypose(canvas, candidate, subset)
# detect hand
hands_list = util.handDetect(candidate, subset, oriImg)
all_hand_peaks = []
shape2 = canvas.shape
for x, y, w, is_left in hands_list:
# 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)
# if is_left:
# plt.imshow(oriImg[y:y+w, x:x+w, :][:, :, [2, 1, 0]])
# plt.show()
peaks = hand_estimation(oriImg[y:y+w, x:x+w, :])
peaks[:, 0] = np.where(peaks[:, 0]==0, peaks[:, 0], peaks[:, 0]+x)
peaks[:, 1] = np.where(peaks[:, 1]==0, peaks[:, 1], peaks[:, 1]+y)
# else:
# peaks = hand_estimation(cv2.flip(oriImg[y:y+w, x:x+w, :], 1))
# peaks[:, 0] = np.where(peaks[:, 0]==0, peaks[:, 0], w-peaks[:, 0]-1+x)
# peaks[:, 1] = np.where(peaks[:, 1]==0, peaks[:, 1], peaks[:, 1]+y)
# print(peaks)
all_hand_peaks.append(peaks)
shape3 = canvas.shape
canvas = util.draw_handpose(canvas, all_hand_peaks)
shape4 = canvas.shape
cv2.imwrite("test.png", canvas)
canvas = cv2.resize(canvas, (shape0[1],shape0[0]))
# raise AttributeError(shape0, shape1, shape2, shape3, shape4)
return canvas[:, :, [2, 1, 0]]
def process_frame(txt_file, frame, body=True, hands=False):
canvas = copy.deepcopy(frame)
if body:
candidate, subset = body_estimation(frame)
if (is_data_valid(subset)):
body_points = get_18_body_points(candidate, subset)
list_to_file(txt_file, body_points)
else:
canvas = copy.deepcopy(frame)
canvas = util.draw_bodypose(canvas, candidate, subset)
# plt.imshow(canvas[:, :, [2, 1, 0]])
# plt.axis('off')
# plt.show()
print(subset)
if hands:
hands_list = util.handDetect(candidate, subset, frame)
all_hand_peaks = []
for x, y, w, is_left in hands_list:
peaks = hand_estimation(frame[y:y + w, x:x + w, :])
peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0],
peaks[:, 0] + x)
peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1],
peaks[:, 1] + y)
all_hand_peaks.append(peaks)
canvas = util.draw_handpose(canvas, all_hand_peaks)
return canvas
def detect_keypoint(test_image, is_vis):
body_estimation = Body('model/body_pose_model.pth')
hand_estimation = Hand('model/hand_pose_model.pth')
oriImg = cv2.imread(test_image) # B,G,R order
# detect body
# subset: n*20 array, n is the human_number in the index, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
# candidate: m*4, m is the keypoint number in the image, [x, y, confidence, id]
candidate, subset = body_estimation(
oriImg
) # candidate: output the keypoints([25, 4]), x, y, score, keypoint_index
canvas = copy.deepcopy(oriImg)
canvas, bodypoints = util.draw_bodypose(canvas, candidate, subset)
# detect hand
hands_list = util.handDetect(candidate, subset, oriImg)
all_hand_peaks = []
hand_personid_isleft = []
for x, y, w, is_left, person_id in hands_list:
# 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)
# if is_left:
# plt.imshow(oriImg[y:y+w, x:x+w, :][:, :, [2, 1, 0]])
# plt.show()
peaks = hand_estimation(oriImg[y:y + w, x:x + w, :])
peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x)
peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y)
# else:
# peaks = hand_estimation(cv2.flip(oriImg[y:y+w, x:x+w, :], 1))
# peaks[:, 0] = np.where(peaks[:, 0]==0, peaks[:, 0], w-peaks[:, 0]-1+x)
# peaks[:, 1] = np.where(peaks[:, 1]==0, peaks[:, 1], peaks[:, 1]+y)
# print(peaks)
all_hand_peaks.append(peaks)
hand_personid_isleft.append([person_id, is_left])
# all_hand_peaks: [p, 21, 2] p is the hand number in the image
# hand_personid_isleft: [p, 2] is_isleft, person_id
all_hand_peaks = np.asarray(all_hand_peaks)
hand_personid_isleft = np.asarray(hand_personid_isleft)
canvas = util.draw_handpose(canvas, all_hand_peaks)
if is_vis:
plt.imshow(canvas[:, :, [2, 1, 0]])
plt.axis('off')
plt.show()
return bodypoints, all_hand_peaks, hand_personid_isleft
def process_frame(frame, body=True, hands=True):
canvas = copy.deepcopy(frame)
if body:
candidate, subset = body_estimation(frame)
canvas = util.draw_bodypose(canvas, candidate, subset)
if hands:
hands_list = util.handDetect(candidate, subset, frame)
all_hand_peaks = []
for x, y, w, is_left in hands_list:
peaks = hand_estimation(frame[y:y + w, x:x + w, :])
peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0],
peaks[:, 0] + x)
peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1],
peaks[:, 1] + y)
all_hand_peaks.append(peaks)
canvas = util.draw_handpose(canvas, all_hand_peaks)
return canvas
all_hand_peaks_values = []
all_hand_names = []
end = time.time()
for file in files:
if int(file[:8])>28999 and int(file[:8])<32560:#32560
#import pdb;pdb.set_trace()
image_path = os.path.join(image_folder,file)
oriImg = cv2.imread(image_path) # B,G,R
hand_peaks = []
peaks,values = hand_estimation(images=oriImg)
#import pdb;pdb.set_trace()
hand_peaks.append(peaks)
if int(file[:8])%500 == 0:
save_img_path = os.path.join(save_folder,'image')
os.makedirs(save_img_path, exist_ok=True)
canvas = copy.deepcopy(oriImg)
canvas = util.draw_handpose(canvas, hand_peaks)
plt.imshow(canvas[:, :, [2, 1, 0]])
plt.axis('off')
plt.savefig(os.path.join(save_img_path,file))
print('{0} demo out saved!'.format(file))
plt.close()
print('Time {0:.3f}\t'.format(time.time() - end))
all_hand_peaks.append(peaks)
all_hand_peaks_values.append(values)
#all_hand_names.append(np.array([int(file[:8])]))
all_hand_names.append(np.array([file]))
#import pdb;pdb.set_trace()
detect_out_path = os.path.join(save_folder,'freihand-train.json')
dump(detect_out_path,all_hand_peaks,all_hand_peaks_values,all_hand_names)
binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
#import pdb;pdb.set_trace()
# 全部小于阈值
if np.sum(binary) == 0:
all_peaks.append([0, 0])
all_peaks_value.append([0.0])#cyj
continue
label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
#import pdb;pdb.set_trace()
max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
label_img[label_img != max_index] = 0
map_ori[label_img == 0] = 0
#import pdb;pdb.set_trace()
y, x = util.npmax(map_ori)
all_peaks.append([x, y])
# confidence coefficient
value = map_ori[y,x]
all_peaks_value.append([value])
#return np.array(all_peaks)
return np.array(all_peaks), np.array(all_peaks_value)
if __name__ == "__main__":
hand_estimation = Hand('../model/hand_pose_model.pth')
# test_image = '../images/hand.jpg'
test_image = '../images/hand.jpg'
oriImg = cv2.imread(test_image) # B,G,R order
peaks = hand_estimation(oriImg)
canvas = util.draw_handpose(oriImg, peaks, True)
cv2.imshow('', canvas)
cv2.waitKey(0)
