try get image info

requirements.txt

+faiss-cpu==1.6.3
+pandas==1.1.2
+elasticsearch==7.9.1
+opencv-python==4.4.0.42
+pillow==7.2.0
+dlib==19.21.0
+requests==2.24.0
+numpy

src/diary_cover_similarity.py

+import time
+
+from utils.images import (BASE_DIR, MODELS_DIR, face_to_vec, file_to_ndarray, url_to_ndarray)
+
+
+def main():
+    img_url = "https://pic.igengmei.com/2020/07/04/1802/1fd23e101178-w"
+    img = url_to_ndarray(img_url)
+    print(img)
+
+    faces = face_to_vec(img)
+
+    print(faces)
+    print("============")
+
+    for face in faces:
+        print(face["feature"] + "\n")
+
+
+if __name__ == "__main__":
+    begin_time = time.time()
+
+    print("base_dir: " + BASE_DIR)
+    print("models_dir: " + MODELS_DIR)
+
+    main()
+
+    print("total cost: " + str(time.time() - begin_time))

src/utils/es.py

+from elasticsearch import Elasticsearch as Es
+
+
+def es_index_adapt(index_prefix, doc_type, rw=None):
+    """get the adapted index name
+    """
+    assert rw in [None, "read", "write"]
+    index = "-".join((index_prefix, doc_type))
+    if rw:
+        index = "-".join((index, rw))
+    return index
+
+
+def get_es():
+    init_args = {
+        "sniff_on_start": False,
+        "sniff_on_connection_fail": False,
+    }
+    new_hosts = [{
+        "host": "172.16.31.17",
+        "port": 9000,
+    }, {
+        "host": "172.16.31.11",
+        "port": 9000,
+    }, {
+        "host": "172.16.31.13",
+        "port": 9000,
+    }]
+    new_es = Es(hosts=new_hosts, **init_args)
+    return new_es
+
+
+def es_query(doc, body, offset=0, size=100, es=None, rw="read"):
+    if es is None:
+        es = get_es()
+    index = es_index_adapt(index_prefix="gm-dbmw", doc_type=doc, rw=rw)
+    res = es.search(index=index, timeout="10s", body=body, from_=offset, size=size)
+    return res

src/utils/images.py

+import io
+import json
+import os
+import time
+import traceback
+
+import cv2
+import dlib
+import requests
+from numpy import np
+from PIL import Image
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+MODELS_DIR = os.path.join(BASE_DIR, "_models")
+FACEREC_PATH = os.path.join(MODELS_DIR, "dlib_face_recognition_resnet_model_v1.dat")
+SHAPE_PATH = os.path.join(MODELS_DIR, "shape_predictor_68_face_landmarks.dat")
+
+face_rec = dlib.face_recognition_model_v1(FACEREC_PATH)
+face_detector = dlib.get_frontal_face_detector()
+shape_predictor = dlib.shape_predictor(SHAPE_PATH)
+
+
+def url_to_ndarray(url):
+    result = requests.get(url, timeout=30)
+    if result.ok:
+        img = Image.open(io.BytesIO(result.content))
+        img = img.convert("RGB")
+        data = np.array(img)
+        return data
+    else:
+        print("http get: {}".format(result.status_code))
+        return None
+
+
+def file_to_ndarray(path):
+    result = cv2.imread(path)
+    img = Image.fromarray(result).convert("RGB")
+    data = np.array(img)
+    return data
+
+
+def face_to_vec(img, max_size=700):
+    start = time.time()
+    orig_img = img
+    print("detect image...")
+    scale = 1
+
+    height, width = img.shape[:2]
+    if max(height, width) > max_size:
+        scale = max_size / float(max(height, width))
+        size = (int(width * scale), int(height * scale))
+        img = cv2.resize(img, size, interpolation=cv2.INTER_AREA)
+
+    try:
+        dets = face_detector(img, 1)
+    except Exception as e:
+        print(e)
+        traceback.print_exc()
+        return []
+
+    print("Number of faces detected: {}".format(len(dets)))
+
+    faces = []
+
+    for i, d in enumerate(dets):
+        face = {}
+
+        shape = shape_predictor(img, d)
+        rect = shape.rect
+        landmark = extract_landmark(shape, scale=scale)
+        face["rect"] = check_rect(rect, orig_img.shape, scale=scale, landmark=landmark)
+        face["landmark"] = json.dumps(landmark)
+
+        try:
+            face_descriptor = face_rec.compute_face_descriptor(img, shape)
+            face["feature"] = json.dumps(np.array(face_descriptor).tolist())
+            faces.append(face)
+            # del face_descriptor
+        except Exception as e:
+            print(e)
+            traceback.print_exc()
+        # del shape
+
+    print("Compute face cost: {}".format(time.time() - start))
+    # del dets
+
+    return faces
+
+
+def check_rect(rect, shape, scale=1, landmark=[]):
+    w, h = shape[1], shape[0]
+
+    # 面部区域最大化
+    top = rect.top() / scale
+    right = rect.right() / scale
+    bottom = rect.bottom() / scale
+    left = rect.left() / scale
+    #     print
+    top = max(min([top] + [p[1] for p in landmark]), 0)
+    right = min(max([right] + [p[0] for p in landmark]), w)
+
+    bottom = min(max([bottom] + [p[1] for p in landmark]), h)
+    left = max(min([left] + [p[0] for p in landmark]), 0)
+
+    # 检测框转换成正方形
+    squareY = bottom - top
+    squareX = right - left
+    diff = abs(squareY - squareX) / 2
+    if squareX > squareY:
+        bottom = min(bottom + diff, h)
+        top = max(top - diff, 0)
+    elif squareX < squareY:
+        right = min(right + diff, w)
+        left = max(left - diff, 0)
+
+    # 检测框放大
+    rect_scale = min((h - bottom) / squareY, top / squareY, (w - right) / squareX, left / squareX, 0.1)
+    top -= squareY * rect_scale
+    bottom += squareY * rect_scale
+    left -= squareX * rect_scale
+    right += squareX * rect_scale
+    return int(top), int(right), int(bottom), int(left)
+
+
+def extract_landmark(shape, scale=1):
+    """特征点提取"""
+    ret = []
+    for i in range(68):
+        point = shape.part(i)
+        ret.append((int(point.x / scale), int(point.y / scale)))
+    return ret