Shirt number

generateClassifier.py

@@ -27,7 +27,7 @@
 pp = preprocessing.StandardScaler().fit(hog_features)
 hog_features = pp.transform(hog_features)
 
-print "Count of digits in dataset", Counter(labels)
+print("Count of digits in dataset", Counter(labels))
 
 # Create an linear SVM object
 clf = LinearSVC()

performRecognition.py

@@ -7,6 +7,13 @@
 import numpy as np
 import argparse as ap
 
+
+def show_image(im):
+    cv2.namedWindow("Resulting Image with Rectangular ROIs", cv2.WINDOW_NORMAL)
+    cv2.imshow("Resulting Image with Rectangular ROIs", im)
+    cv2.waitKey()
+
+
 # Get the path of the training set
 parser = ap.ArgumentParser()
 parser.add_argument("-c", "--classiferPath", help="Path to Classifier File", required="True")
@@ -16,41 +23,66 @@
 # Load the classifier
 clf, pp = joblib.load(args["classiferPath"])
 
-# Read the input image 
+# Read the input image
 im = cv2.imread(args["image"])
-
-# Convert to grayscale and apply Gaussian filtering
 im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
 im_gray = cv2.GaussianBlur(im_gray, (5, 5), 0)
 
+im_th = cv2.Canny(im_gray,50,150)
+# show_image(im_th)
+# laplacian = cv2.Laplacian(im_gray, cv2.CV_64F)
+# show_image(laplacian)
+
+# Convert to grayscale and apply Gaussian filtering
+#im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
+#im_gray = cv2.GaussianBlur(im_gray, (5, 5), 0)
+
+# show_image(im_gray)
 # Threshold the image
-ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
+# ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
+
+# show_image(im_th)
 
 # Find contours in the image
-ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+_, ctrs, _ = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
 # Get rectangles contains each contour
 rects = [cv2.boundingRect(ctr) for ctr in ctrs]
 
 # For each rectangular region, calculate HOG features and predict
 # the digit using Linear SVM.
 for rect in rects:
-    # Draw the rectangles
-    cv2.rectangle(im, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3) 
-    # Make the rectangular region around the digit
-    leng = int(rect[3] * 1.6)
-    pt1 = int(rect[1] + rect[3] // 2 - leng // 2)
-    pt2 = int(rect[0] + rect[2] // 2 - leng // 2)
-    roi = im_th[pt1:pt1+leng, pt2:pt2+leng]
-    # Resize the image
-    roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
-    roi = cv2.dilate(roi, (3, 3))
-    # Calculate the HOG features
-    roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False)
-    roi_hog_fd = pp.transform(np.array([roi_hog_fd], 'float64'))
-    nbr = clf.predict(roi_hog_fd)
-    cv2.putText(im, str(int(nbr[0])), (rect[0], rect[1]),cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3)
+    try:
+        if 40 < rect[2] < 60 and 100 < rect[3] < 200:
+            pass
+        else:
+            continue
+
+        # if 440 < rect[0] < 620 and 125 < rect[1] < 165:
+        #     pass
+        # else:
+        #     continue
+
+        # Draw the rectangles
+        cv2.rectangle(im, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3)
+        # Make the rectangular region around the digit
+        leng = int(rect[3] * 1.6)
+        pt1 = int(rect[1] + rect[3] // 2 - leng // 2)
+        pt2 = int(rect[0] + rect[2] // 2 - leng // 2)
+        roi = im_th[pt1:pt1+leng, pt2:pt2+leng]
+        # Resize the image
+        roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
+        roi = cv2.dilate(roi, (3, 3))
+        # Calculate the HOG features
+        roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False)
+        roi_hog_fd = pp.transform(np.array([roi_hog_fd], 'float64'))
+        nbr = clf.predict(roi_hog_fd)
+        # print nbr
+        cv2.putText(im, str(int(nbr[0])), (rect[0], rect[1]),cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3)
+    except:
+        continue
 
 cv2.namedWindow("Resulting Image with Rectangular ROIs", cv2.WINDOW_NORMAL)
 cv2.imshow("Resulting Image with Rectangular ROIs", im)
 cv2.waitKey()
+