193 lines
7.2 KiB
Python
193 lines
7.2 KiB
Python
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"""
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输入：原图，图中face框
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输出：每张face的5点特征点的位置
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"""
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# from common.face_landmark_points_util import shape_index_process
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# from common.face_detector_util import prior_box_forward, decode, nms_sorted
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import cv2
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import onnxruntime as ort
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import numpy as np
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# 设置ONNX Runtime的日志级别为ERROR
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ort.set_default_logger_severity(3) # 3表示ERROR级别
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m_origin_patch = [15, 15]
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m_origin = [112, 112]
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class HypeShape:
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def __init__(self, shape):
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self.m_shape = shape
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self.m_weights = [0]*len(self.m_shape)
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size = len(self.m_shape)
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self.m_weights[size - 1] = self.m_shape[size - 1]
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for times in range(size - 1):
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self.m_weights[size - 1 - times - 1] = self.m_weights[size - 1 - times] * self.m_shape[size - 1 - times - 1]
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def to_index(self, coordinate):
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if len(coordinate) == 0:
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return 0
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size = len(coordinate)
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weight_start = len(self.m_weights) - size + 1
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index = 0
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for times in range(size - 1):
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index += self.m_weights[weight_start + times] * coordinate[times]
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index += coordinate[size - 1]
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return index
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def shape_index_process(feat_data, pos_data):
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feat_h = feat_data.shape[2]
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feat_w = feat_data.shape[3]
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landmarkx2 = pos_data.shape[1]
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x_patch_h = int( m_origin_patch[0] * feat_data.shape[2] / float( m_origin[0] ) + 0.5 )
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x_patch_w = int( m_origin_patch[1] * feat_data.shape[3] / float( m_origin[1] ) + 0.5 )
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feat_patch_h = x_patch_h
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feat_patch_w = x_patch_w
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num = feat_data.shape[0]
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channels = feat_data.shape[1]
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r_h = ( feat_patch_h - 1 ) / 2.0
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r_w = ( feat_patch_w - 1 ) / 2.0
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landmark_num = int(landmarkx2 * 0.5)
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pos_offset = HypeShape([pos_data.shape[0], pos_data.shape[1]])
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feat_offset = HypeShape([feat_data.shape[0], feat_data.shape[1], feat_data.shape[2], feat_data.shape[3]])
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nmarks = int( landmarkx2 * 0.5 )
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out_shape = [feat_data.shape[0], feat_data.shape[1], x_patch_h, nmarks, x_patch_w]
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out_offset = HypeShape([feat_data.shape[0], feat_data.shape[1], x_patch_h, nmarks, x_patch_w])
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buff = np.zeros(out_shape)
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zero = 0
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buff = buff.reshape((-1))
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pos_data = pos_data.reshape((-1))
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feat_data = feat_data.reshape((-1))
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for i in range(landmark_num):
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for n in range(num):
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# coordinate of the first patch pixel, scale to the feature map coordinate
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y = int( pos_data[pos_offset.to_index( [n, 2 * i + 1] )] * ( feat_h - 1 ) - r_h + 0.5 )
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x = int( pos_data[pos_offset.to_index( [n, 2 * i] )] * ( feat_w - 1 ) - r_w + 0.5 )
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for c in range(channels):
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for ph in range(feat_patch_h):
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for pw in range(feat_patch_w):
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y_p = y + ph
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x_p = x + pw
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# set zero if exceed the img bound
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if y_p < 0 or y_p >= feat_h or x_p < 0 or x_p >= feat_w:
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buff[out_offset.to_index( [n, c, ph, i, pw] )] = zero
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else:
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buff[out_offset.to_index( [n, c, ph, i, pw] )] = feat_data[feat_offset.to_index( [n, c, y_p, x_p] )]
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return buff.reshape((1,-1,1,1)).astype(np.float32)
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def crop_face(image:np.ndarray, face, H, W):
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"""
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Crop a face from an image with padding if the face is out of bounds.
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Args:
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image (np.ndarray): The input image as a numpy array of shape (H, W, C).
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face (tuple): A tuple containing (x, y, w, h) for the face rectangle.
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padding (list): Padding data for padding the image.
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Returns:
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np.ndarray: Cropped and padded image.
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"""
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x0, y0, x1, y1 = int(round(face[0])), int(round(face[1])), int(round(face[2])), int(round(face[3]))
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# Calculate padding
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pad_top = max(0, -y0)
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pad_bottom = max(0, y1 - H)
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pad_left = max(0, -x0)
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pad_right = max(0, x1 - W)
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# Apply padding
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padded_image = np.pad(image, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), mode='constant', constant_values=0)
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# Update new coordinates after padding
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new_x0, new_y0 = x0 + pad_left, y0 + pad_top
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new_x1, new_y1 = x1 + pad_left, y1 + pad_top
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# Crop the face
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cropped_image = padded_image[new_y0:new_y1, new_x0:new_x1, :]
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return cropped_image, (x0,y0,x1,y1)
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class Landmark5er():
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def __init__(self, onnx_path1, onnx_path2, num_threads=1) -> None:
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session_options = ort.SessionOptions()
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session_options.intra_op_num_threads = num_threads
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# 初始化 InferenceSession 时传入 SessionOptions 对象
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self.ort_session1 = ort.InferenceSession(onnx_path1, session_options=session_options)
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self.ort_session2 = ort.InferenceSession(onnx_path2, session_options=session_options)
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self.first_input_name = self.ort_session1.get_inputs()[0].name
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self.second_input_name = self.ort_session2.get_inputs()[0].name
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def inference(self, image, box):
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# face_img = image[int(box[1]):int(box[3]), int(box[0]):int(box[2])] # 这种裁剪不对,不合适
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H,W,C = image.shape
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face_img, box = crop_face(image,box,H,W)
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x1,y1,x2,y2 = int(box[0]) ,int(box[1]), int(box[2]),int(box[3])
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if x1 < 0 or x1 > W-1 or x2 < 0 or x2 > W:
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print("x超出边界")
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if y1 < 0 or y1 > H-1 or y2 < 0 or y2 > H:
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print("y超出边界")
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face_img = cv2.resize(face_img,(112,112))
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gray_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY)
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gray_img = gray_img.reshape((1, 1, 112, 112)).astype(np.float32) # 输入必须是(1,1,112,112)
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# points5 net1
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results_1 = self.ort_session1.run([], {self.first_input_name: gray_img})
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# shape index process
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feat_data = results_1[0]
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pos_data = results_1[1]
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shape_index_results = shape_index_process(feat_data, pos_data)
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results_2 = self.ort_session2.run([], {self.second_input_name: shape_index_results})
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landmarks = (results_2[0] + results_1[1]) * 112
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# print("results_2[0] , results_1[1]: ",results_2[0], results_1[1])
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# print("in find_landmarks, landmarks:", landmarks)
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landmarks = landmarks.reshape((-1)).astype(np.int32)
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scale_x = (box[2] - box[0]) / 112.0
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scale_y = (box[3] - box[1]) / 112.0
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mapped_landmarks = []
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for i in range(landmarks.size // 2):
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x = box[0] + landmarks[2 * i] * scale_x
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y = box[1] + landmarks[2 * i + 1] * scale_y
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x = max(0.01, min(x,W-0.01))
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y = max(0.01, min(y,H-0.01))
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mapped_landmarks.append((x, y))
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return mapped_landmarks
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if __name__ == "__main__":
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import sys
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ld5 = Landmark5er(onnx_path1="./checkpoints/face_landmarker_pts5_net1.onnx",
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onnx_path2="./checkpoints/face_landmarker_pts5_net2.onnx",
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num_threads=1)
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if len(sys.argv) > 1:
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jpg_path = sys.argv[1]
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else:
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jpg_path = "asserts/1.jpg"
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image = cv2.imread(jpg_path)
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if image is None:
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print("Error: Could not load image.")
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exit()
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box = (201.633087308643, 42.78490193881931, 319.49375572419393, 191.68867463550623)
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landmarks5 = ld5.inference(image,box)
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print(landmarks5)
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