added new script

predicct_bbox.py save the bounding box in test_outputs.csv as well

predict_bbox.py

@@ -0,0 +1,220 @@
+from __future__ import print_function, division
+import warnings
+warnings.filterwarnings("ignore")
+import os.path
+import pandas as pd
+import torch
+import torch.nn as nn
+import numpy as np
+import torchvision
+from torchvision import datasets, models, transforms
+import dlib
+import os
+import argparse
+
+def rect_to_bb(rect):
+	# take a bounding predicted by dlib and convert it
+	# to the format (x, y, w, h) as we would normally do
+	# with OpenCV
+	x = rect.left()
+	y = rect.top()
+	w = rect.right() - x
+	h = rect.bottom() - y
+	# return a tuple of (x, y, w, h)
+	return (x, y, w, h)
+
+
+def detect_face(image_paths,  SAVE_DETECTED_AT, size = 300, padding = 0.25):
+    cnn_face_detector = dlib.cnn_face_detection_model_v1('dlib_models/mmod_human_face_detector.dat')
+    sp = dlib.shape_predictor('dlib_models/shape_predictor_5_face_landmarks.dat')
+    base = 2000  # largest width and height
+    rects = []
+    for index, image_path in enumerate(image_paths):
+        if index % 1000 == 0:
+            print('---%d/%d---' %(index, len(image_paths)))
+
+        img = dlib.load_rgb_image(image_path)
+        img = dlib.resize_image(img, 628, 628)
+        dets = cnn_face_detector(img, 1)
+        num_faces = len(dets)
+        if num_faces == 0:
+            print("Sorry, there were no faces found in '{}'".format(image_path))
+            continue
+        # Find the 5 face landmarks we need to do the alignment.
+        faces = dlib.full_object_detections()
+
+        for detection in dets:
+            rect = detection.rect
+            faces.append(sp(img, rect))
+            rects.append(rect)
+        images = dlib.get_face_chips(img, faces, size=size, padding = padding)
+        for idx, image in enumerate(images):
+            img_name = image_path.split("/")[-1]
+            path_sp = img_name.split(".")
+            face_name = os.path.join(SAVE_DETECTED_AT,  path_sp[0] + "_" + "face" + str(idx) + "." + path_sp[-1])
+            dlib.save_image(image, face_name)
+    return rects
+
+def predidct_age_gender_race(save_prediction_at, bboxes, imgs_path = 'cropped_faces/'):
+    img_names = [os.path.join(imgs_path, x) for x in os.listdir(imgs_path)]
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    model_fair_7 = torchvision.models.resnet34(pretrained=True)
+    model_fair_7.fc = nn.Linear(model_fair_7.fc.in_features, 18)
+    model_fair_7.load_state_dict(torch.load('fair_face_models/res34_fair_align_multi_7_20190809.pt'))
+    model_fair_7 = model_fair_7.to(device)
+    model_fair_7.eval()
+
+    model_fair_4 = torchvision.models.resnet34(pretrained=True)
+    model_fair_4.fc = nn.Linear(model_fair_4.fc.in_features, 18)
+    model_fair_4.load_state_dict(torch.load('fair_face_models/fairface_alldata_4race_20191111.pt'))
+    model_fair_4 = model_fair_4.to(device)
+    model_fair_4.eval()
+
+    trans = transforms.Compose([
+        transforms.ToPILImage(),
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    # img pth of face images
+    face_names = []
+    # list within a list. Each sublist contains scores for all races. Take max for predicted race
+    race_scores_fair = []
+    gender_scores_fair = []
+    age_scores_fair = []
+    race_preds_fair = []
+    gender_preds_fair = []
+    age_preds_fair = []
+    race_scores_fair_4 = []
+    race_preds_fair_4 = []
+
+    for index, img_name in enumerate(img_names):
+        if index % 1000 == 0:
+            print("Predicting... {}/{}".format(index, len(img_names)))
+
+        face_names.append(img_name)
+        image = dlib.load_rgb_image(img_name)
+        image = trans(image)
+        image = image.view(1, 3, 224, 224)  # reshape image to match model dimensions (1 batch size)
+        image = image.to(device)
+
+        # fair
+        outputs = model_fair_7(image)
+        outputs = outputs.cpu().detach().numpy()
+        outputs = np.squeeze(outputs)
+
+        race_outputs = outputs[:7]
+        gender_outputs = outputs[7:9]
+        age_outputs = outputs[9:18]
+
+        race_score = np.exp(race_outputs) / np.sum(np.exp(race_outputs))
+        gender_score = np.exp(gender_outputs) / np.sum(np.exp(gender_outputs))
+        age_score = np.exp(age_outputs) / np.sum(np.exp(age_outputs))
+
+        race_pred = np.argmax(race_score)
+        gender_pred = np.argmax(gender_score)
+        age_pred = np.argmax(age_score)
+
+        race_scores_fair.append(race_score)
+        gender_scores_fair.append(gender_score)
+        age_scores_fair.append(age_score)
+
+        race_preds_fair.append(race_pred)
+        gender_preds_fair.append(gender_pred)
+        age_preds_fair.append(age_pred)
+
+        # fair 4 class
+        outputs = model_fair_4(image)
+        outputs = outputs.cpu().detach().numpy()
+        outputs = np.squeeze(outputs)
+
+        race_outputs = outputs[:4]
+        race_score = np.exp(race_outputs) / np.sum(np.exp(race_outputs))
+        race_pred = np.argmax(race_score)
+
+        race_scores_fair_4.append(race_score)
+        race_preds_fair_4.append(race_pred)
+
+    result = pd.DataFrame([face_names,
+                           race_preds_fair,
+                           race_preds_fair_4,
+                           gender_preds_fair,
+                           age_preds_fair,
+                           race_scores_fair, race_scores_fair_4,
+                           gender_scores_fair,
+                           age_scores_fair,
+                           bboxes]).T
+    result.columns = ['face_name_align',
+                      'race_preds_fair',
+                      'race_preds_fair_4',
+                      'gender_preds_fair',
+                      'age_preds_fair',
+                      'race_scores_fair',
+                      'race_scores_fair_4',
+                      'gender_scores_fair',
+                      'age_scores_fair',
+                      "bbox"]
+
+
+
+    result.loc[result['race_preds_fair'] == 0, 'race'] = 'White'
+    result.loc[result['race_preds_fair'] == 1, 'race'] = 'Black'
+    result.loc[result['race_preds_fair'] == 2, 'race'] = 'Latino_Hispanic'
+    result.loc[result['race_preds_fair'] == 3, 'race'] = 'East Asian'
+    result.loc[result['race_preds_fair'] == 4, 'race'] = 'Southeast Asian'
+    result.loc[result['race_preds_fair'] == 5, 'race'] = 'Indian'
+    result.loc[result['race_preds_fair'] == 6, 'race'] = 'Middle Eastern'
+
+    # race fair 4
+
+    result.loc[result['race_preds_fair_4'] == 0, 'race4'] = 'White'
+    result.loc[result['race_preds_fair_4'] == 1, 'race4'] = 'Black'
+    result.loc[result['race_preds_fair_4'] == 2, 'race4'] = 'Asian'
+    result.loc[result['race_preds_fair_4'] == 3, 'race4'] = 'Indian'
+
+    # gender
+    result.loc[result['gender_preds_fair'] == 0, 'gender'] = 'Male'
+    result.loc[result['gender_preds_fair'] == 1, 'gender'] = 'Female'
+
+    # age
+    result.loc[result['age_preds_fair'] == 0, 'age'] = '0-2'
+    result.loc[result['age_preds_fair'] == 1, 'age'] = '3-9'
+    result.loc[result['age_preds_fair'] == 2, 'age'] = '10-19'
+    result.loc[result['age_preds_fair'] == 3, 'age'] = '20-29'
+    result.loc[result['age_preds_fair'] == 4, 'age'] = '30-39'
+    result.loc[result['age_preds_fair'] == 5, 'age'] = '40-49'
+    result.loc[result['age_preds_fair'] == 6, 'age'] = '50-59'
+    result.loc[result['age_preds_fair'] == 7, 'age'] = '60-69'
+    result.loc[result['age_preds_fair'] == 8, 'age'] = '70+'
+
+
+
+    result[['face_name_align',
+            'race', 'race4',
+            'gender', 'age',
+            'race_scores_fair', 'race_scores_fair_4',
+            'gender_scores_fair', 'age_scores_fair',
+            "bbox"]].to_csv(save_prediction_at, index=False)
+    print("saved results at ", save_prediction_at)
+
+
+def ensure_dir(directory):
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--csv', dest='input_csv', action='store',
+                        help='csv file of image path where col name for image path is "img_path')
+    print("using CUDA?: %s" % dlib.DLIB_USE_CUDA)
+    args = parser.parse_args()
+    SAVE_DETECTED_AT = "detected_faces"
+    ensure_dir(SAVE_DETECTED_AT)
+    imgs = pd.read_csv(args.input_csv)['img_path']
+    bboxes = detect_face(imgs, SAVE_DETECTED_AT)
+    print(len(bboxes))
+    print("detected faces are saved at ", SAVE_DETECTED_AT)
+    predidct_age_gender_race("test_outputs.csv", bboxes, SAVE_DETECTED_AT)