work on obj_detect sample

samples/python2/obj_detect.py

@@ -0,0 +1,79 @@
+import numpy as np
+import cv2, cv
+import common
+
+def detect(img, cascade):
+    min_size = (20, 20)
+    haar_scale = 1.1
+    min_neighbors = 3
+    haar_flags = 0
+    rects = cascade.detectMultiScale(img, haar_scale, min_neighbors, haar_flags, min_size)
+    if len(rects) == 0:
+        return
+    rects[:,2:] += rects[:,:2]
+    return rects
+
+def detect_turned(img, cascade):
+    img_t = cv2.transpose(img)
+    img_cw = cv2.flip(img_t, 1)
+    img_ccw = cv2.flip(img_t, 0)
+    r = detect(img, cascade)
+    r_cw = detect(img_cw, cascade)
+    r_ccw = detect(img_ccw, cascade)
+
+    h, w = img.shape[:2]
+    if r_cw is not None:
+        r_cw[:,[0, 2]] = h - r_cw[:,[0, 2]] - 1
+        r_cw = r_cw[:,[1,0,3,2]]
+    if r_ccw is not None:
+        r_ccw[:,[1, 3]] = w - r_ccw[:,[1, 3]] - 1
+        r_ccw = r_ccw[:,[1,0,3,2]]
+    rects = np.vstack( [a for a in [r, r_cw, r_ccw] if a is not None] )
+    return rects
+
+def process_image(fn, cascade):
+    pass
+    
+    
+
+if __name__ == '__main__':
+    import sys
+    import getopt
+    args, img_mask = getopt.getopt(sys.argv[1:], '', ['cascade='])
+    args = dict(args)
+    cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
+
+    cascade = cv2.CascadeClassifier(cascade_fn)
+
+
+    img = cv2.imread('test.jpg')
+    h, w = img.shape[:2]
+    r = 512.0 / max(h, w)
+    small = cv2.resize(img, (int(w*r), int(h*r)), interpolation=cv2.INTER_AREA)
+    rects = detect_turned(small, cascade)
+    print rects
+    for x1, y1, x2, y2 in rects:
+        cv2.rectangle(small, (x1, y1), (x2, y2), (0, 255, 0))
+        cv2.circle(small, (x1, y1), 2, (0, 0, 255), -1)
+
+
+
+    cv2.imshow('img', small)
+    cv2.waitKey()
+
+    
+
+'''
+
+
+    img = cv2.imread('test.jpg')
+    h, w = img.shape[:2]
+
+
+    r = 512.0 / max(h, w)
+    small = cv2.resize(img, (w*r, h*r), interpolation=cv2.INTER_AREA)
+
+cv2.imshow('img', small)
+cv2.waitKey()
+
+'''