.

demo.py

@@ -11,7 +11,8 @@
 body_estimation = Body('model/body_pose_model.pth')
 hand_estimation = Hand('model/hand_pose_model.pth')
 
-test_image = 'images/demo.jpg'
+# test_image = 'images/demo.jpg'
+test_image = '/Users/hzzone/Desktop/1.jpeg'
 oriImg = cv2.imread(test_image)  # B,G,R order
 candidate, subset = body_estimation(oriImg)
 # canvas = util.draw_bodypose(oriImg, candidate, subset)
@@ -24,10 +25,10 @@
     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, :])
-        # canvas = util.draw_handpose(canvas, peaks, True)
+        # plt.imshow(oriImg[y:y+w, x:x+w, :][:, :, [2, 1, 0]])
+        # plt.show()
+        peaks = hand_estimation(oriImg[y:y+w, x:x+w, :], x, y)
+        canvas = util.draw_handpose(canvas, peaks)
 
 plt.imshow(canvas[:, :, [2, 1, 0]])
 plt.show()

python/hand.py

@@ -19,7 +19,7 @@ def __init__(self, model_path):
         self.model.load_state_dict(model_dict)
         self.model.eval()
 
-    def __call__(self, oriImg):
+    def __call__(self, oriImg, initial_x=0, initial_y=0):
         scale_search = [0.5, 1.0, 1.5, 2.0]
         # scale_search = [0.5]
         boxsize = 368
@@ -67,7 +67,7 @@ def __call__(self, oriImg):
             map_ori[label_img == 0] = 0
 
             y, x = util.npmax(map_ori)
-            all_peaks.append([x, y])
+            all_peaks.append([x+initial_x, y+initial_y])
         return np.array(all_peaks)
 
 if __name__ == "__main__":

python/util.py

@@ -1,6 +1,8 @@
 import numpy as np
 import math
 import cv2
+import colorsys
+import matplotlib
 
 
 def padRightDownCorner(img, stride, padValue):
@@ -68,19 +70,13 @@ def draw_bodypose(canvas, candidate, subset):
     # plt.imshow(canvas[:, :, [2, 1, 0]])
     return canvas
 
-def draw_handpose(canvas, peaks, show_number=False, initial_x=0, initial_y=0):
+def draw_handpose(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], \
              [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
-    colors = [[100, 100, 100], [100, 0, 0], [150, 0, 0], \
-             [200, 0, 0], [255, 0, 0], [100, 100, 0], [150, 150, 0], [200, 200, 0], \
-             [255, 255, 0], [0, 100, 50], [0, 150, 75], [0, 200, 100], [0, 255, 125], \
-             [0, 50, 100], [0, 75, 150], [0, 100, 200], \
-             [0, 125, 255], [100, 0, 100], [150, 0, 150], \
-             [200, 0, 200], [255, 0, 255]]
 
     for ie, e in enumerate(edges):
         if np.sum(np.all(peaks[e], axis=1)==0)==0:
-            cv2.line(canvas, tuple(peaks[e[0]]), tuple(peaks[e[1]]), colors[ie], thickness=1)
+            cv2.line(canvas, tuple(peaks[e[0]]), tuple(peaks[e[1]]), matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])*255, thickness=2)
     for i, keyponit in enumerate(peaks):
         cv2.circle(canvas, tuple(keyponit), 2, (0, 0, 255), thickness=-1)
         if show_number:
@@ -142,7 +138,9 @@ def handDetect(candidate, subset, oriImg):
             if x + width > image_width: width1 = image_width - x
             if y + width > image_height: width2 = image_height - y
             width = min(width1, width2)
-            detect_result.append([int(x), int(y), int(width), is_left])
+            # the max hand box value is 20 pixels
+            if width >= 20:
+                detect_result.append([int(x), int(y), int(width), is_left])
 
     '''
     return value: [[x, y, w, True if left hand else False]].