Update README.md

README.md

@@ -51,6 +51,87 @@ tags:
 ![images8](./000048_scribble_concat.webp)
 ![images9](./000083_scribble_concat.webp)
 
+## Replace the default draw pose function to get better result
+thanks feiyuuu for report the problem. When using the default pose line the performance may be unstable, this is because the pose label use more thick line in training to have a better look.
+This difference can be fix by using the following method:
+
+Find the util.py in controlnet_aux python package, usually the path is like: /your anaconda3 path/envs/your env name/lib/python3.8/site-packages/controlnet_aux/open_pose/util.py
+Replace the draw_bodypose function with the following code:
+```python
+def draw_bodypose(canvas: np.ndarray, keypoints: List[Keypoint]) -> np.ndarray:
+    """
+    Draw keypoints and limbs representing body pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the body pose.
+        keypoints (List[Keypoint]): A list of Keypoint objects representing the body keypoints to be drawn.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn body pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    H, W, C = canvas.shape
+
+    
+    if max(W, H) < 500:
+        ratio = 1.0
+    elif max(W, H) >= 500 and max(W, H) < 1000:
+        ratio = 2.0
+    elif max(W, H) >= 1000 and max(W, H) < 2000:
+        ratio = 3.0
+    elif max(W, H) >= 2000 and max(W, H) < 3000:
+        ratio = 4.0
+    elif max(W, H) >= 3000 and max(W, H) < 4000:
+        ratio = 5.0
+    elif max(W, H) >= 4000 and max(W, H) < 5000:
+        ratio = 6.0
+    else:
+        ratio = 7.0
+
+    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],
+    ]
+
+    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 (k1_index, k2_index), color in zip(limbSeq, colors):
+        keypoint1 = keypoints[k1_index - 1]
+        keypoint2 = keypoints[k2_index - 1]
+
+        if keypoint1 is None or keypoint2 is None:
+            continue
+
+        Y = np.array([keypoint1.x, keypoint2.x]) * float(W)
+        X = np.array([keypoint1.y, keypoint2.y]) * float(H)
+        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), int(stickwidth * ratio)), int(angle), 0, 360, 1)
+        cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color])
+
+    for keypoint, color in zip(keypoints, colors):
+        if keypoint is None:
+            continue
+
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        cv2.circle(canvas, (int(x), int(y)), int(4 * ratio), color, thickness=-1)
+
+    return canvas
+```
+
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.