def predict(img, pngFilePath=None):
# loading lena image
# img = skimage.data.chelsea() # => numpy.ndarray
# print img[0][0]
# 書きの方法はダメ！ネガポジ画像になってしまう。
# img = Image.open("images/fruit.jpg")
# img.load()
# img = np.asarray( img, dtype="int32" )
# predictでは、data/bbox_image/n07747607_631_box2.JPEGは正解できた。
# ということは、n07747607_631.jpg でselective search すると正解できるかも？
# tgt_img_path = "data/image/n07747607_631.jpg"
if pngFilePath is not None:
im = Image.open(pngFilePath)
bg = Image.new("RGB", im.size, (255,255,255))
bg.paste(im,im)
bg.save(pngFilePath + ".jpg")
img = io.imread(pngFilePath + ".jpg")
print "get img from file"
# perform selective search
img_lbl, regions = selectivesearch.selective_search(img, scale=500, sigma=0.9, min_size=10)
candidates = set()
for r in regions:
# excluding same rectangle (with different segments)
if r['rect'] in candidates:
continue
# excluding regions smaller than 2000 pixels
if r['size'] < 2000:
continue
# distorted rects
x, y, w, h = r['rect']
if w / h > 1.2 or h / w > 1.2:
continue
candidates.add(r['rect'])
print candidates
# img = Image.open(tgt_img_path)
# imgs = [resize(img.crop(rect)) for rect in candidates]
res = []
img = Image.open(pngFilePath + ".jpg")
for rect in candidates:
print rect
cropped_img = resize(img.crop(rect))
if cropped_img is None:
continue
print "cropped!"
(answer, prob) = pd.predict_by_data(cropped_img)
print "get answer and prob!"
if prob > 0.9:
res.append((rect,answer))
print "send back answers!"
return res
def main():
# loading lena image
# img = skimage.data.chelsea() # => numpy.ndarray
# print img[0][0]
# 書きの方法はダメ！ネガポジ画像になってしまう。
# img = Image.open("images/fruit.jpg")
# img.load()
# img = np.asarray( img, dtype="int32" )
# predictでは、data/bbox_image/n07747607_631_box2.JPEGは正解できた。
# ということは、n07747607_631.jpg でselective search すると正解できるかも？
tgt_img_path = "data/image/n07747607_631.jpg"
img = io.imread(tgt_img_path)
# perform selective search
img_lbl, regions = selectivesearch.selective_search(img, scale=500, sigma=0.9, min_size=10)
candidates = set()
for r in regions:
# excluding same rectangle (with different segments)
if r['rect'] in candidates:
continue
# excluding regions smaller than 2000 pixels
if r['size'] < 2000:
continue
# distorted rects
x, y, w, h = r['rect']
if w / h > 1.2 or h / w > 1.2:
continue
candidates.add(r['rect'])
img = Image.open(tgt_img_path)
# imgs = [resize(img.crop(rect)) for rect in candidates]
res = []
for rect in candidates:
print rect
cropped_img = resize(img.crop(rect))
(answer, prob) = predict.predict_by_data(cropped_img)
if prob > 0.9:
res.append((rect,answer))
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(img)
for v in res:
x, y, w, h = v[0] # rect object
print v[1] # answer
print x, y, w, h
rect = mpatches.Rectangle((x, y), w, h, fill=False, edgecolor='red', linewidth=1)
ax.add_patch(rect)
plt.show()