diff --git a/misinformation/cropposts.py b/misinformation/cropposts.py
new file mode 100644
index 0000000..24b5e83
--- /dev/null
+++ b/misinformation/cropposts.py
@@ -0,0 +1,280 @@
+import os
+import ntpath
+from PIL import Image
+from matplotlib.patches import ConnectionPatch
+import cv2
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# use this function to visualize the matches
+def plot_matches(img1, img2, keypoints1, keypoints2):
+    fig, axes = plt.subplots(1, 2, figsize=(16, 7))
+
+    # draw images
+    axes[0].imshow(img1)
+    axes[1].imshow(img2)
+
+    # draw matches
+    for kp1, kp2 in zip(keypoints1, keypoints2):
+        c = np.random.rand(3)
+        con = ConnectionPatch(
+            xyA=kp1,
+            coordsA=axes[0].transData,
+            xyB=kp2,
+            coordsB=axes[1].transData,
+            color=c,
+        )
+        fig.add_artist(con)
+        axes[0].plot(*kp1, color=c, marker="x")
+        axes[1].plot(*kp2, color=c, marker="x")
+
+    plt.show()
+
+
+# use this function to visualize the matches from sift
+def draw_matches(matches, img1, img2, kp1, kp2):
+    MIN_MATCH_COUNT = 4
+    if len(matches) > MIN_MATCH_COUNT:
+        # Estimate homography between template and scene
+        src_pts = np.float32([kp1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
+        dst_pts = np.float32([kp2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
+
+        M = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)[0]
+
+        # Draw detected template in scene image
+        # h, w = img1.shape
+        h = img1.shape[0]
+        w = img1.shape[1]
+        pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(
+            -1, 1, 2
+        )
+        dst = cv2.perspectiveTransform(pts, M)
+
+        img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)
+
+        # h1, w1 = img1.shape
+        # h2, w2 = img2.shape
+        h1 = img1.shape[0]
+        h2 = img2.shape[0]
+        w1 = img1.shape[1]
+        w2 = img2.shape[1]
+        nWidth = w1 + w2
+        nHeight = max(h1, h2)
+        hdif = int((h2 - h1) / 2)
+        newimg = np.zeros((nHeight, nWidth, 3), np.uint8)
+
+        for i in range(3):
+            newimg[hdif : hdif + h1, :w1, i] = img1
+            newimg[:h2, w1 : w1 + w2, i] = img2
+
+        # Draw SIFT keypoint matches
+        for m in matches:
+            pt1 = (int(kp1[m.queryIdx].pt[0]), int(kp1[m.queryIdx].pt[1] + hdif))
+            pt2 = (int(kp2[m.trainIdx].pt[0] + w1), int(kp2[m.trainIdx].pt[1]))
+            cv2.line(newimg, pt1, pt2, (255, 0, 0))
+
+        plt.imshow(newimg)
+        plt.show()
+    else:
+        print("Not enough matches are found - %d/%d" % (len(matches), MIN_MATCH_COUNT))
+
+
+# compute matches from sift
+def matching_points(img1, img2):
+    img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+    img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
+    # sift = cv2.SIFT_create()
+    sift = cv2.xfeatures2d.SIFT_create()
+    kp1, des1 = sift.detectAndCompute(img1, None)
+    kp2, des2 = sift.detectAndCompute(img2, None)
+    des1 = np.float32(des1)
+    des2 = np.float32(des2)
+    # Initialize and use FLANN
+    FLANN_INDEX_KDTREE = 1
+    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
+    search_params = dict(checks=50)
+    flann = cv2.FlannBasedMatcher(index_params, search_params)
+    matches = flann.knnMatch(des1, des2, k=2)
+
+    filtered_matches = []
+    for m, n in matches:
+        if m.distance < 0.7 * n.distance:
+            filtered_matches.append(m)
+
+    # draw_matches(filtered_matches, img1, img2, kp1, kp2)
+
+    return filtered_matches, kp1, kp2
+
+
+# extract match points from matches
+def kp_from_matches(matches, kp1, kp2):
+    kp1 = np.float32([kp1[m.queryIdx].pt for m in matches])
+    kp2 = np.float32([kp2[m.trainIdx].pt for m in matches])
+    return kp1, kp2
+
+
+# estimate a crop corner for posts image via matches
+def compute_crop_corner(
+    matches, kp1, kp2, region=30, h_margin=28, v_margin=5, min_match=6
+):
+    kp1, kp2 = kp_from_matches(matches, kp1, kp2)
+    ys = kp2[:, 1]
+    covers = []
+    for y in ys:
+        ys_c = ys - y
+        series = pd.Series(ys_c)
+        is_between = series.between(0, region)
+        covers.append(is_between.sum())
+
+    covers = np.array(covers)
+    if covers.max() < min_match:
+        return None
+
+    kp_id = ys[covers.argmax()]
+    v = int(kp_id) - v_margin if int(kp_id) > v_margin else int(kp_id)
+    hs = []
+    for kp in kp2:
+        if 0 <= kp[1] - v <= region:
+            hs.append(kp[0])
+
+    h = int(np.min(hs)) - h_margin if int(np.min(hs)) > h_margin else 0
+
+    return v, h
+
+
+# crop the posts image
+def crop_posts_image(
+    ref_view, view, plt_match=False, plt_crop=False, correct_margin=700
+):
+    """
+    get file lists from dir and sub dirs
+    ref_view：ref_view for crop the posts images
+    view: posts image that need cropping
+    rte： None - not cropped, or (crop_view, number of matches)
+    """
+    filtered_matches, kp1, kp2 = matching_points(ref_view, view)
+    MIN_MATCH_COUNT = 6
+    if len(filtered_matches) < MIN_MATCH_COUNT:
+        return None
+
+    if plt_match:
+        img1 = cv2.cvtColor(ref_view, cv2.COLOR_BGR2GRAY)
+        img2 = cv2.cvtColor(view, cv2.COLOR_BGR2GRAY)
+        draw_matches(filtered_matches, img1, img2, kp1, kp2)
+
+    corner = compute_crop_corner(filtered_matches, kp1, kp2)
+    if corner is None:
+        return None
+    v, h = corner
+    if view.shape[1] - h > correct_margin:
+        h = view.shape[1] - ref_view.shape[1]
+    if view.shape[1] - h < ref_view.shape[1]:
+        h = view.shape[1] - ref_view.shape[1]
+
+    crop_view = view[0:v, h:, :]
+    if plt_crop:
+        view[v, :, 0:3] = [255, 0, 0]
+        view[:, h, 0:3] = [255, 0, 0]
+        plt.imshow(view)
+        plt.show()
+
+        plt.imshow(crop_view)
+        plt.show()
+
+    return crop_view, len(filtered_matches)
+
+
+def get_file_list(dir, filelist, ext=None, convert_unix=True):
+    """
+    get file lists from dir and sub dirs
+    dir：root dir for file lists
+    ext: file extension
+    rte： File list
+    """
+    if os.path.isfile(dir):
+        if ext is None:
+            filelist.append(dir)
+        else:
+            if ext in dir[-3:]:
+                filelist.append(dir)
+
+    elif os.path.isdir(dir):
+        for s in os.listdir(dir):
+            newDir = os.path.join(dir, s)
+            get_file_list(newDir, filelist, ext)
+
+    if convert_unix:
+        new_filelist = []
+        for file in filelist:
+            file = file.replace("\\", "/")
+            new_filelist.append(file)
+        return new_filelist
+    else:
+        return filelist
+
+
+def crop_posts_from_refs(ref_views, view, plt_match=False, plt_crop=False):
+    crop_view = None
+    max_matchs = 0
+    for ref_view in ref_views:
+        rte = crop_posts_image(ref_view, view, plt_match=plt_match, plt_crop=plt_crop)
+        if rte is not None:
+            crop_img, match_num = rte
+            if match_num > max_matchs:
+                crop_view = crop_img
+                max_matchs = match_num
+                # print("match_num = ", match_num)
+
+    return crop_view
+
+
+def crop_posts_from_files(
+    ref_dir, crop_dir, save_crop_dir, plt_match=False, plt_crop=False
+):
+    ref_list = []
+    ref_list = get_file_list(ref_dir, ref_list, ext="png")
+    ref_views = []
+    for ref_file in ref_list:
+        ref_view = np.array(Image.open(ref_file))
+        ref_views.append(ref_view)
+    crop_list = []
+    crop_list = get_file_list(crop_dir, crop_list, ext="png")
+
+    for crop_file in crop_list:
+        view = np.array(Image.open(crop_file))
+        crop_view = crop_posts_from_refs(
+            ref_views, view, plt_match=plt_match, plt_crop=plt_crop
+        )
+        if crop_view is not None:
+            filename = ntpath.basename(crop_file)
+            save_path = os.path.join(save_crop_dir, filename)
+            save_path = save_path.replace("\\", "/")
+            cv2.imwrite(save_path, crop_view)
+
+
+def test_crop_from_file():
+    # Load images
+    # view1 = np.array(Image.open("data/ref/ref-00.png")) / 255
+    # view2 = np.array(Image.open("data/napsa/100539_ben.png")) / 255
+    view1 = np.array(Image.open("data/ref/ref-06.png"))
+    view2 = np.array(Image.open("data/napsa/102956_eng.png"))
+    crop_view, match_num = crop_posts_image(view1, view2, plt_match=True, plt_crop=True)
+    cv2.imwrite("data/crop_100489_ind.png", crop_view)
+
+
+def test_crop_from_folder():
+    ref_dir = "./data/ref"
+    crop_dir = "./data/apsa"
+    save_crop_dir = "data/crop"
+    crop_posts_from_files(
+        ref_dir, crop_dir, save_crop_dir, plt_match=False, plt_crop=False
+    )
+
+
+# do tests:
+
+# test_crop_from_file()
+
+# test_crop_from_folder()
diff --git a/notebooks/cropposts.ipynb b/notebooks/cropposts.ipynb
new file mode 100644
index 0000000..a6955e0
--- /dev/null
+++ b/notebooks/cropposts.ipynb
@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b25986d7",
+   "metadata": {},
+   "source": [
+    "# Crop posts from social media posts images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8a5a491",
+   "metadata": {},
+   "source": [
+    "Crop posts from social media posts images, to keep import text informations from social media posts images.\n",
+    "We can set some manually cropped views from social media posts as reference for cropping the same type social media posts images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ae02c45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import misinformation.cropposts as crpo\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import cv2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d04d0e86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load ref view for cropping the same type social media posts images.\n",
+    "ref_view = np.array(Image.open(\"data/ref/ref-00.png\"))\n",
+    "plt.imshow(ref_view)\n",
+    "plt.show()\n",
+    "\n",
+    "view = np.array(Image.open(\"data/napsa/100539_ben.png\"))\n",
+    "plt.figure(figsize=(10, 15))\n",
+    "plt.imshow(view)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71850d9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# crop a posts from reference view, set plt_match=True, plt_crop=True\n",
+    "crop_view, match_num = crpo.crop_posts_image(\n",
+    "    ref_view, view, plt_match=True, plt_crop=True\n",
+    ")\n",
+    "\n",
+    "# save cropped images\n",
+    "cv2.imwrite(\"data/100539_ben.png\", crop_view)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67fc7b82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "view2 = np.array(Image.open(\"data/napsa/100541_ben.png\"))\n",
+    "plt.figure(figsize=(10, 15))\n",
+    "plt.imshow(view2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21d87359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# crop a posts from the same reference view, set plt_match=True, plt_crop=True\n",
+    "crop_view, match_num = crpo.crop_posts_image(\n",
+    "    ref_view, view2, plt_match=True, plt_crop=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eef89291",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Batch crop images from image folders.\n",
+    "# The cropped images will save in save_crop_dir folders with the same file name form origin.\n",
+    "# We can set many types reference images in ref_dir folder, to crop posts images in different types social media, like twitter or facebook.\n",
+    "ref_dir = \"./data/ref\"\n",
+    "crop_dir = \"./data/apsa\"\n",
+    "save_crop_dir = \"data/crop\"\n",
+    "crpo.crop_posts_from_files(\n",
+    "    ref_dir, crop_dir, save_crop_dir, plt_match=False, plt_crop=False\n",
+    ")\n",
+    "\n",
+    "print(\"Batch cropping images well done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c10d9f6f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}