def get_bbox(images_path_name):
# TODO: Currently for 1 image only
for index, image in enumerate(images_path_name):
image_name_list = image.split('\\') # 修改处
image = image_name_list[0] + '/' + image_name_list[1] # 修改处
bboxes = selective_search_bbox(image)
logging.debug('bboxes {}'.format(bboxes))
return bboxes
def calculate_bbox_score_and_save_img(image_path_name, dataset_image_path, gt_x1, gt_y1, gt_x2, gt_y2):
logging.debug('dataset_image_path {}'.format(dataset_image_path))
logging.debug('image_path_name {}'.format(image_path_name))
candidates = selective_search_bbox(image_path_name)
logging.debug('candidates {}'.format(candidates))
image_name = image_path_name.split('/')[-1].split('.')[0]
logging.debug('image_name {}'.format(image_name))
img_read = Image.open(image_path_name)
logging.debug('{} {} {}'.format(img_read.format, img_read.size, img_read.mode))
i=0
for x, y, w, h in (candidates):
# left, upper, right, and lower pixel; The cropped section includes the left column and
# the upper row of pixels and goes up to (but doesn't include) the right column and bottom row of pixels
logging.debug('Cropped image: i {}'.format(i))
i=i+1
boxA = (gt_x1, gt_y1, gt_x2, gt_y2)
boxB = (x, y, x+w, y+h)
iou = bb_intersection_over_union(boxA, boxB)
logging.debug('boxA {}'.format(boxA))
logging.debug('boxB {}'.format(boxB))
logging.debug('iou {}'.format(iou))
# Uncomment only for testing as too much cpu/memory wastage
#display_bbox(image_path_name, boxA, boxB)
#img_crop = img_read.crop((y, x, y+w, x+h))
img_crop = img_read.crop((x, y, x+w, y+h))
image_save_name = image_path_name.split('/')[-2] + '_' + image_path_name.split('/')[-1].split('.')[0]
image_save_path = dataset_image_path.rsplit('/', 1)[0]
image_save_path_name = image_save_path + '/' + image_save_name + '_crop_' + str(x) + '-' + str(y) + '-' + str(x+w) + '-' + str(y+h) + '_iou_' + str(iou) + '.jpg'
logging.debug('image_save_path_name {}'.format(image_save_path_name))
img_crop.save(image_save_path_name)
logging.debug('img_crop {} {} {}'.format(img_crop.format, img_crop.size, img_crop.mode))
# img_crop_resize = img_crop.resize((img_width, img_height))
# img_crop_resize.save('temp/test/'+ image_name + '_' + str(i) + '_cropped_resize' + '.jpg')
# logging.debug('img_crop_resize {} {} {}'.format(img_crop_resize.format, img_crop_resize.size, img_crop_resize.mode))
# Ground Truth
image_save_name = image_path_name.split('/')[-2] + '_' + image_path_name.split('/')[-1].split('.')[0]
image_save_path = dataset_image_path.rsplit('/', 1)[0]
image_save_path_name = image_save_path + '/' + image_save_name + '_gt_' + str(gt_x1) + '-' + str(gt_y1) + '-' + str(gt_x2) + '-' + str(gt_y2) + '_iou_' + '1.0' + '.jpg'
logging.debug('image_save_path_name {}'.format(image_save_path_name))
#img_crop = img_read.crop((gt_y1, gt_x1, gt_y2, gt_x2))
img_crop = img_read.crop((gt_x1, gt_y1, gt_x2, gt_y2))
img_crop.save(image_save_path_name)
logging.debug('img_crop {} {} {}'.format(img_crop.format, img_crop.size, img_crop.mode))
def predict_image_name(model, image_path_name):
logging.debug('image_path_name {}'.format(image_path_name))
candidates = selective_search_bbox(image_path_name)
logging.debug('candidates {}'.format(candidates))
image_name = image_path_name.split('/')[-1].split('.')[0]
logging.debug('image_name {}'.format(image_name))
img_read = Image.open(image_path_name)
logging.debug('{} {} {}'.format(img_read.format, img_read.size,
img_read.mode))
# img_read.show()
i = 0
for x, y, w, h in (candidates):
# left, upper, right, and lower pixel; The cropped section includes the left column and
# the upper row of pixels and goes up to (but doesn't include) the right column and bottom row of pixels
img_crop = img_read.crop((y, x, y + w, x + h))
img_crop.save('temp/test/' + image_name + '_' + str(i) + '_cropped_' +
'.jpg')
logging.debug('img_crop {} {} {}'.format(img_crop.format,
img_crop.size, img_crop.mode))
img_crop_resize = img_crop.resize((img_width, img_height))
img_crop_resize.save('temp/test/' + image_name + '_' + str(i) +
'_cropped_resize' + '.jpg')
logging.debug('img_crop_resize {} {} {}'.format(
img_crop_resize.format, img_crop_resize.size,
img_crop_resize.mode))
i = i + 1
img = np.array(img_crop_resize).astype(np.float32)
img[:, :, 0] -= 103.939
img[:, :, 1] -= 116.779
img[:, :, 2] -= 123.68
#img = img.transpose((2,0,1))
img = np.expand_dims(img, axis=0)
prediction = model.predict(img, batch_size, verbose=1)
logging.debug('\n\nprediction \n{}'.format(prediction))
for index, preds in enumerate(prediction):
for pred in preds:
logging.debug('pred {0:6f}'.format(float(pred)))
def get_bbox(images_path_name):
# TODO: Currently for 1 image only
for index, image in enumerate(images_path_name):
bboxes = selective_search_bbox(image)
logging.debug('bboxes {}'.format(bboxes))
return bboxes
def predict_image_name(image_path_name):
logging.debug('image_path_name {}'.format(image_path_name))
candidates = selective_search_bbox(image_path_name)
logging.debug('candidates {}'.format(candidates))
image_name = image_path_name.split('/')[-1].split('.')[0]
logging.debug('image_name {}'.format(image_name))
# img = Image.open(image_path_name)
# logging.debug('{} {} {}'.format(img.format, img.size, img.mode))
#img2 = img.crop((0, 0, 100, 100))
# img2.save("img2.jpg")
# img2.show()
#crop_img = img[200:400, 100:300] # Crop from x, y, w, h -> 100, 200, 300, 400
# NOTE: its img[y: y + h, x: x + w] and *not* img[x: x + w, y: y + h]
# img = cv2.imread(image_path_name)
# fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
img_read = Image.open(image_path_name)
logging.debug('{} {} {}'.format(img_read.format, img_read.size, img_read.mode))
# img_read.show()
i=0
for x, y, w, h in (candidates):
# left, upper, right, and lower pixel; The cropped section includes the left column and
# the upper row of pixels and goes up to (but doesn't include) the right column and bottom row of pixels
img_crop = img_read.crop((y, x, y+w, x+h))
img_crop.save('temp/test/'+ image_name + '_' + str(i) + '_cropped_' + '.jpg')
logging.debug('img_crop {} {} {}'.format(img_crop.format, img_crop.size, img_crop.mode))
img_crop_resize = img_crop.resize((img_width, img_height))
img_crop_resize.save('temp/test/'+ image_name + '_' + str(i) + '_cropped_resize' + '.jpg')
logging.debug('img_crop_resize {} {} {}'.format(img_crop_resize.format, img_crop_resize.size, img_crop_resize.mode))
i=i+1
# crop_img = img[x:y, w:h] # Crop from x, y, w, h -> 100, 200, 300, 400
# logging.debug('crop_img {}'.format(crop_img.shape))
# ax.imshow(crop_img)
# # cv2.imshow('cropped', crop_img)
# # cv2.waitKey(0)
# plt.show()
# # Convert Image to array
# img = PIL.Image.open("foo.jpg").convert("L")
# arr = numpy.array(img)
# # Convert array to Image
# img = PIL.Image.fromarray(arr)
# img = cv2.resize(cv2.imread(image_path_name), (224, 224)).astype(np.float32)
# img2.save('temp/test/img_'+str(i)+'.jpg')
# img3 = img2.thumbnail((img_width, img_height))
# logging.debug('img3 {}'.format(type(img3)))
# # img3.save('temp/test/img_'+str(i)+'_resized.jpg')
# logging.debug('{} {} {}'.format(img3.format, img3.size, img3.mode))
# img4 = pad_and_crop_image(img3, img_width, img_height)
# logging.debug('{} {} {}'.format(img4.format, img4.size, img4.mode))
# img4.save('temp/test/img_'+str(i)+'_resized1.jpg')
img=np.array(img_crop_resize).astype(np.float32)
img[:,:,0] -= 103.939
img[:,:,1] -= 116.779
img[:,:,2] -= 123.68
#img = img.transpose((2,0,1))
img = np.expand_dims(img, axis=0)
prediction = model.predict(img, batch_size, verbose=1)
logging.debug('\n\nprediction \n{}'.format(prediction))
for index,preds in enumerate(prediction):
for pred in preds:
logging.debug('pred {0:6f}'.format(float(pred)))