Initial commit of project

predictors/predict_beauty.py

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+from keras.layers import Conv2D, Input, MaxPool2D,Flatten, Dense, Permute, GlobalAveragePooling2D
+from keras.models import Model
+from keras.optimizers import Adam
+import numpy as np
+import pickle
+import keras
+import cv2
+import sys
+import dlib
+import os.path
+from keras.models import Sequential
+from keras.applications.resnet50 import ResNet50
+#from keras.applications.resnet50 import Dense
+from keras.layers.core import Dense
+from keras.optimizers import Adam
+import pickle
+import numpy as np
+import cv2
+import os
+from keras.layers import Dropout
+import config
+
+#APP_ROOT = os.path.dirname(os.path.abspath(__file__))
+
+#parent_path = os.path.dirname(APP_ROOT)
+#parent_path = os.path.dirname(parent_path)
+#model_path = parent_path+"/common/mmod_human_face_detector.dat"
+#cnn_face_detector = dlib.cnn_face_detection_model_v1(model_path)
+cnn_face_detector = dlib.cnn_face_detection_model_v1(config.CNN_FACE_DETECTOR_MODEL_PATH)
+resnet = ResNet50(include_top=False, pooling='avg')
+beautyModel = Sequential()
+beautyModel.add(resnet)
+beautyModel.add(Dense(5, activation='softmax'))
+beautyModel.layers[0].trainable = False
+
+beautyModel.load_weights(config.BEAUTY_MODEL_WEIGHTS_PATH)
+
+def score_mapping(modelScore):
+
+    if modelScore <= 1.9:
+        mappingScore = ((4 - 2.5) / (1.9 - 1.0)) * (modelScore-1.0) + 2.5
+    elif modelScore <= 2.8:
+        mappingScore = ((5.5 - 4) / (2.8 - 1.9)) * (modelScore-1.9) + 4
+    elif modelScore <= 3.4:
+        mappingScore = ((6.5 - 5.5) / (3.4 - 2.8)) * (modelScore-2.8) + 5.5
+    elif modelScore <= 4:
+        mappingScore = ((8 - 6.5) / (4 - 3.4)) * (modelScore-3.4) + 6.5
+    elif modelScore < 5:
+        mappingScore = ((9 - 8) / (5 - 4)) * (modelScore-4) + 8
+
+    return mappingScore
+
+def predict_beauty(img):
+    #im0 = cv2.imread(imgPath)
+    im0 = img
+
+    if im0.shape[0] > 1280:
+        new_shape = (1280, im0.shape[1] * 1280 / im0.shape[0])
+    elif im0.shape[1] > 1280:
+        new_shape = (im0.shape[0] * 1280 / im0.shape[1], 1280)
+    elif im0.shape[0] < 640 or im0.shape[1] < 640:
+        new_shape = (im0.shape[0] * 2, im0.shape[1] * 2)
+    else:
+        new_shape = im0.shape[0:2]
+
+    im = cv2.resize(im0, (int(new_shape[1]), int(new_shape[0])))
+    dets = cnn_face_detector(im, 0)
+
+    for i, d in enumerate(dets):
+        face = [d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom()]
+        croped_im = im[face[1]:face[3], face[0]:face[2], :]
+        resized_im = cv2.resize(croped_im, (224, 224))
+        normed_im = np.array([(resized_im - 127.5) / 127.5])
+
+        pred = beautyModel.predict(normed_im)
+        ldList = pred[0]
+        out = 1 * ldList[0] + 2 * ldList[1] + 3 * ldList[2] + 4 * ldList[3] + 5 * ldList[4]
+
+        out = score_mapping(out)
+        return out
+
+    #     print(img + " 打分:" + str('%.2f' % (out)))
+    #     cv2.rectangle(im, (face[0], face[1]), (face[2], face[3]), (0, 255, 0), 3)
+    #     cv2.putText(im, str('%.2f' % (out)), (face[0], face[3]), cv2.FONT_HERSHEY_SIMPLEX,
+    #                 1, (0, 0, 255), 2)
+    #
+    # ret = path + "/output-" + img
+    # cv2.imwrite(ret, im)
+    # return ret
+
+def predict_beauty_from_faces(detected_faces, img):
+    outList = []
+    im0 = img
+
+    if im0.shape[0] > 1280:
+        new_shape = (1280, im0.shape[1] * 1280 / im0.shape[0])
+    elif im0.shape[1] > 1280:
+        new_shape = (im0.shape[0] * 1280 / im0.shape[1], 1280)
+    elif im0.shape[0] < 640 or im0.shape[1] < 640:
+        new_shape = (im0.shape[0] * 2, im0.shape[1] * 2)
+    else:
+        new_shape = im0.shape[0:2]
+
+    im = cv2.resize(im0, (int(new_shape[1]), int(new_shape[0])))
+
+    for i, d in enumerate(detected_faces):
+        face = [d.left(), d.top(), d.right(), d.bottom()]
+        croped_im = im[face[1]:face[3], face[0]:face[2], :]
+        resized_im = cv2.resize(croped_im, (224, 224))
+        normed_im = np.array([(resized_im - 127.5) / 127.5])
+
+        pred = beautyModel.predict(normed_im)
+        ldList = pred[0]
+        out = 1 * ldList[0] + 2 * ldList[1] + 3 * ldList[2] + 4 * ldList[3] + 5 * ldList[4]
+
+        out = score_mapping(out)
+        outList.append(out)
+    return outList
+
+# beauty_predict(parent_path+"/samples/image",'fengjie.jpg')
+# beauty_predict(parent_path+"/samples/image",'nenghua.jpg')
+# beauty_predict(parent_path+"/samples/image",'shunli.jpg')
+# beauty_predict(parent_path+"/samples/image",'test1.jpg')
+# beauty_predict(parent_path+"/samples/image",'test2.jpg')
+# beauty_predict(parent_path+"/samples/image",'test3.jpg')
+# beauty_predict(parent_path+"/samples/image",'fty1845.jpg')
+#beauty_predict(parent_path+"/samples/image",'fty1959.jpg')
+# beauty_predict(parent_path+"/samples/image",'jiyou.png')