Add files via upload

vision_for_anchors.py

+import numpy as np
+import matplotlib.pyplot as plt
+def sigmoid(x):
+    s = 1 / (1 + np.exp(-x))
+    return s
+#---------------------------------------------------#
+#   将预测值的每个特征层调成真实值
+#---------------------------------------------------#
+# 13x13
+def yolo_head(feats, anchors, num_classes):
+    # 3
+    num_anchors = len(anchors)
+    # [1, 1, 1, num_anchors, 2]
+    anchors_tensor = np.reshape(anchors, [1, 1, 1, num_anchors, 2])  / 32
+
+    # 获得x，y的网格
+    # (13,13, 1, 2)
+    grid_shape = np.shape(feats)[1:3] # height, width
+    print(grid_shape)
+    grid_y = np.tile(np.reshape(np.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
+        [1, grid_shape[1], 1, 1])
+    grid_x = np.tile(np.reshape(np.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
+        [grid_shape[0], 1, 1, 1])
+    grid = np.concatenate([grid_x, grid_y],-1)
+    print(np.shape(grid))
+    # (batch_size,13,13,3,85)
+    feats = np.reshape(feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])
+
+    # 将预测值调成真实值
+    # box_xy对应框的中心点
+    # box_wh对应框的宽和高
+    box_xy = (sigmoid(feats[..., :2]) + grid)
+    box_wh = np.exp(feats[..., 2:4]) * anchors_tensor
+    box_confidence = sigmoid(feats[..., 4:5])
+    box_class_probs = sigmoid(feats[..., 5:])
+
+  
+    fig = plt.figure()
+    ax = fig.add_subplot(121)
+    plt.ylim(-2,15)
+    plt.xlim(-2,15)
+    plt.scatter(grid_x,grid_y)
+    plt.scatter(5,5,c='black')
+    plt.gca().invert_yaxis()
+
+
+    anchor_left = grid_x - anchors_tensor/2 
+    anchor_top = grid_y - anchors_tensor/2 
+    print(np.shape(anchors_tensor))
+    rect1 = plt.Rectangle([anchor_left[0,5,5,0,0],anchor_top[0,5,5,0,1]],anchors_tensor[0,0,0,0,0],anchors_tensor[0,0,0,0,1],color="r",fill=False)
+    rect2 = plt.Rectangle([anchor_left[0,5,5,1,0],anchor_top[0,5,5,1,1]],anchors_tensor[0,0,0,1,0],anchors_tensor[0,0,0,1,1],color="r",fill=False)
+    rect3 = plt.Rectangle([anchor_left[0,5,5,2,0],anchor_top[0,5,5,2,1]],anchors_tensor[0,0,0,2,0],anchors_tensor[0,0,0,2,1],color="r",fill=False)
+
+    ax.add_patch(rect1)
+    ax.add_patch(rect2)
+    ax.add_patch(rect3)
+
+    ax = fig.add_subplot(122)
+    plt.ylim(-2,15)
+    plt.xlim(-2,15)
+    plt.scatter(grid_x,grid_y)
+    plt.scatter(5,5,c='black')
+    plt.scatter(box_xy[0,5,5,:,0],box_xy[0,5,5,:,1],c='r')
+    plt.gca().invert_yaxis()
+
+    pre_left = box_xy[...,0] - box_wh[...,0]/2 
+    pre_top = box_xy[...,1] - box_wh[...,1]/2 
+
+    rect1 = plt.Rectangle([pre_left[0,5,5,0],pre_top[0,5,5,0]],box_wh[0,5,5,0,0],box_wh[0,5,5,0,1],color="r",fill=False)
+    rect2 = plt.Rectangle([pre_left[0,5,5,1],pre_top[0,5,5,1]],box_wh[0,5,5,1,0],box_wh[0,5,5,1,1],color="r",fill=False)
+    rect3 = plt.Rectangle([pre_left[0,5,5,2],pre_top[0,5,5,2]],box_wh[0,5,5,2,0],box_wh[0,5,5,2,1],color="r",fill=False)
+
+    ax.add_patch(rect1)
+    ax.add_patch(rect2)
+    ax.add_patch(rect3)
+
+    plt.show()
+    #
+feat = np.random.normal(0,0.5,[4,13,13,75])
+anchors = [[142, 110],[192, 243],[459, 401]]
+yolo_head(feat,anchors,20)
\ No newline at end of file