Normalize line endings and whitespace

samples/python2/facedetect.py

@@ -1,60 +1,60 @@
-import numpy as np
-import cv2
-import cv2.cv as cv
-from video import create_capture
-from common import clock, draw_str
-
-help_message = '''
-USAGE: facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<video_source>]
-'''
-
-def detect(img, cascade):
-    rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
-    if len(rects) == 0:
-        return []
-    rects[:,2:] += rects[:,:2]
-    return rects
-
-def draw_rects(img, rects, color):
-    for x1, y1, x2, y2 in rects:
-        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
-
-if __name__ == '__main__':
-    import sys, getopt
-    print help_message
-    
-    args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
-    try: video_src = video_src[0]
-    except: video_src = 0
-    args = dict(args)
-    cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
-    nested_fn  = args.get('--nested-cascade', "../../data/haarcascades/haarcascade_eye.xml")
-
-    cascade = cv2.CascadeClassifier(cascade_fn)
-    nested = cv2.CascadeClassifier(nested_fn)
-
-    cam = create_capture(video_src, fallback='synth:bg=../cpp/lena.jpg:noise=0.05')
-
-    while True:
-        ret, img = cam.read()
-        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        gray = cv2.equalizeHist(gray)
-        
-        t = clock()
-        rects = detect(gray, cascade)
-        vis = img.copy()
-        draw_rects(vis, rects, (0, 255, 0))
-        for x1, y1, x2, y2 in rects:
-            roi = gray[y1:y2, x1:x2]
-            vis_roi = vis[y1:y2, x1:x2]
-            subrects = detect(roi.copy(), nested)
-            draw_rects(vis_roi, subrects, (255, 0, 0))
-        dt = clock() - t
-
-        draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000))
-        cv2.imshow('facedetect', vis)
-
-        if 0xFF & cv2.waitKey(5) == 27:
-            break
-    cv2.destroyAllWindows() 			
-
+import numpy as np
+import cv2
+import cv2.cv as cv
+from video import create_capture
+from common import clock, draw_str
+
+help_message = '''
+USAGE: facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<video_source>]
+'''
+
+def detect(img, cascade):
+    rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
+    if len(rects) == 0:
+        return []
+    rects[:,2:] += rects[:,:2]
+    return rects
+
+def draw_rects(img, rects, color):
+    for x1, y1, x2, y2 in rects:
+        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+
+if __name__ == '__main__':
+    import sys, getopt
+    print help_message
+
+    args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
+    try: video_src = video_src[0]
+    except: video_src = 0
+    args = dict(args)
+    cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
+    nested_fn  = args.get('--nested-cascade', "../../data/haarcascades/haarcascade_eye.xml")
+
+    cascade = cv2.CascadeClassifier(cascade_fn)
+    nested = cv2.CascadeClassifier(nested_fn)
+
+    cam = create_capture(video_src, fallback='synth:bg=../cpp/lena.jpg:noise=0.05')
+
+    while True:
+        ret, img = cam.read()
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        gray = cv2.equalizeHist(gray)
+
+        t = clock()
+        rects = detect(gray, cascade)
+        vis = img.copy()
+        draw_rects(vis, rects, (0, 255, 0))
+        for x1, y1, x2, y2 in rects:
+            roi = gray[y1:y2, x1:x2]
+            vis_roi = vis[y1:y2, x1:x2]
+            subrects = detect(roi.copy(), nested)
+            draw_rects(vis_roi, subrects, (255, 0, 0))
+        dt = clock() - t
+
+        draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000))
+        cv2.imshow('facedetect', vis)
+
+        if 0xFF & cv2.waitKey(5) == 27:
+            break
+    cv2.destroyAllWindows()
+