FEATURE: codes change to greatly enhance the performance

dnn/darknet.py

@@ -19,8 +19,9 @@ def sample(probs):
     return len(probs)-1
 
 def c_array(ctype, values):
-    arr = (ctype*len(values))()
-    arr[:] = values
+    arr = (ctype * len(values))()
+    values = values.astype(ctype)
+    memmove(arr, values.ctypes.data, values.nbytes)
     return arr
 
 class BOX(Structure):
@@ -115,8 +116,7 @@ class METADATA(Structure):
 import numpy as np
 import cv2
 def array_to_image(image):
-    boxed_image = np.array(image)
-    boxed_image = np.array(cv2.split(boxed_image))
+    boxed_image = np.array(cv2.split(image))
     c = boxed_image.shape[0]
     h = boxed_image.shape[1]
     w = boxed_image.shape[2]

dnn/ocr.py

@@ -7,6 +7,8 @@
 ## add opencv dnn for relu and stride 
 ## add ocr prob for every char
 """
+from ctypes import memmove
+
 import cv2
 import os
 import time
@@ -84,8 +86,9 @@ def predict_darknet(image):
     res=predict_image(ocrNet,im)
     outW = int(np.ceil(w/4)-3)
     nchars = len(charactersPred)
-    out = [ res[i] for i in range(outW*nchars)] 
-    out = np.array(out).reshape((nchars,outW))
+    out = np.zeros(outW * nchars, dtype=res._type_)
+    memmove(out.ctypes.data, res, out.nbytes)
+    out = out.reshape((nchars, outW))
     out = out.transpose((1,0))
     out = softmax(out)
 

dnn/text.py

@@ -4,6 +4,8 @@
 text detect
 @author: chineseocr
 """
+from ctypes import memmove
+
 import cv2
 import numpy as np
 from config import textPath,anchors,GPU
@@ -26,9 +28,10 @@ def detect_box(image,scale=600,maxScale=900):
             scale=16
             iw = int(np.ceil(im.w/scale))
             ih = int(np.ceil(im.h/scale))
-            h,w = image.shape[:2] 
-            out = [ res[i] for i in range(40*ih*iw)] 
-            out=np.array(out).reshape((1,40,ih,iw))
+            h,w = image.shape[:2]
+            out = np.zeros(40 * ih * iw, dtype=res._type_)
+            memmove(out.ctypes.data, res, out.nbytes)
+            out = out.reshape((1, 40, ih, iw))
         else:
             inputBlob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(w,h),swapRB=False ,crop=False);
             outputName = textNet.getUnconnectedOutLayersNames()

helper/image.py

@@ -128,10 +128,10 @@ def box_to_center(box):
         xmin,ymin,xmax,ymax = box
         w = xmax-xmin
         h = ymax-ymin
-        return [round(xmin,4),round(ymin,4),round(w,4),round(h,4)]
+        return [xmin, ymin, w, h]
 
     newBoxes = [ box_to_center(box) for box in boxes]
-    newscores = [ round(float(x),6) for x in scores]
+    newscores = [ float(x) for x in scores]
     index = cv2.dnn.NMSBoxes(newBoxes, newscores, score_threshold=score_threshold, nms_threshold=nms_threshold)
     if len(index)>0:
         index = index.reshape((-1,))