.

demo.py

@@ -11,25 +11,28 @@
 body_estimation = Body('model/body_pose_model.pth')
 hand_estimation = Hand('model/hand_pose_model.pth')
 
-# test_image = 'images/demo.jpg'
-test_image = '/Users/hzzone/Desktop/1.jpeg'
+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)
 canvas = copy.deepcopy(oriImg)
 # detect hand
 hands_list = util.handDetect(candidate, subset, oriImg)
 
+all_hand_peaks = []
 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)
+    # 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, :], x, y)
-        canvas = util.draw_handpose(canvas, peaks)
+        all_hand_peaks.append(peaks)
+
+canvas = util.draw_handpose(canvas, all_hand_peaks)
 
 plt.imshow(canvas[:, :, [2, 1, 0]])
 plt.show()
-# cv2.imwrite('t.jpg', canvas)
+cv2.imwrite('t.jpg', canvas)

python/hand.py

@@ -74,7 +74,7 @@ def __call__(self, oriImg, initial_x=0, initial_y=0):
     hand_estimation = Hand('../model/hand_pose_model.pth')
 
     # test_image = '../images/hand.jpg'
-    test_image = '/Users/hzzone/Desktop/1.png'
+    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)

python/hand_model_outputsize.py

@@ -0,0 +1,16 @@
+from model import handpose_model
+import torch
+from tqdm import tqdm
+import json
+
+model = handpose_model()
+
+size = {}
+for i in tqdm(range(10, 1000)):
+    data = torch.randn(1, 3, i, i)
+    if torch.cuda.is_available():
+        data = data.cuda()
+    size[i] = model(data).size(2)
+
+with open('hand_model_output_size.json') as f:
+    json.dump(size, f)

python/util.py

@@ -1,8 +1,12 @@
 import numpy as np
 import math
 import cv2
-import colorsys
 import matplotlib
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+from matplotlib.figure import Figure
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
 
 
 def padRightDownCorner(img, stride, padValue):
@@ -70,17 +74,53 @@ def draw_bodypose(canvas, candidate, subset):
     # plt.imshow(canvas[:, :, [2, 1, 0]])
     return canvas
 
-def draw_handpose(canvas, peaks, show_number=False):
+def draw_handpose(canvas, all_hand_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]]
+    fig = Figure(figsize=plt.figaspect(canvas))
+
+    fig.subplots_adjust(0, 0, 1, 1)
+    fig.subplots_adjust(bottom=0, top=1, left=0, right=1)
+    bg = FigureCanvas(fig)
+    ax = fig.subplots()
+    ax.axis('off')
+    ax.imshow(canvas)
+
+    width, height = ax.figure.get_size_inches() * ax.figure.get_dpi()
+
+    for peaks in all_hand_peaks:
+        for ie, e in enumerate(edges):
+            if np.sum(np.all(peaks[e], axis=1)==0)==0:
+                x1, y1 = peaks[e[0]]
+                x2, y2 = peaks[e[1]]
+                ax.plot([x1, x2], [y1, y2], color=matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0]))
 
+        for i, keyponit in enumerate(peaks):
+            x, y = keyponit
+            ax.plot(x, y, 'r.')
+            if show_number:
+                ax.text(x, y, str(i))
+    bg.draw()
+    canvas = np.fromstring(bg.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
+    return canvas
+
+# image drawed by opencv is not good.
+def draw_handpose_by_opencv(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]]
+    # 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)
     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]]), matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])*255, thickness=2)
+            x1, y1 = peaks[e[0]]
+            x2, y2 = peaks[e[1]]
+            cv2.line(canvas, (x1, y1), (x2, y2), 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)
+        x, y = keyponit
+        cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
         if show_number:
-            cv2.putText(canvas, str(i), tuple(keyponit), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), lineType=cv2.LINE_AA)
+            cv2.putText(canvas, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), lineType=cv2.LINE_AA)
     return canvas
 
 # detect hand according to body pose keypoints