Initial commit of project

imageProcessing.py

@@ -0,0 +1,77 @@
+import cv2
+import dlib
+import numpy as np
+import tensorflow as tf
+
+import config
+from predictors.predict_gender import detect_genders
+from predictors.predict_bmi import predict_bmi
+from predictors.predict_age import predict_age
+#from predictors.predict_beauty import predict_beauty
+
+detector = dlib.get_frontal_face_detector()
+
+
+def extract_metadata(image_file_path):
+    img = cv2.imread(image_file_path)
+    # img = cv2.resize(img, (640, 480))
+    input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img_h, img_w, _ = np.shape(input_img)
+
+    detected, scores, idx = detector.run(input_img, 1)
+    faces = np.empty((len(detected), config.RESNET50_DEFAULT_IMG_WIDTH,
+                      config.RESNET50_DEFAULT_IMG_WIDTH, 3))
+    filteredFaces = []
+
+    for i, d in enumerate(detected):
+        x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, \
+                               d.bottom() + 1, d.width(), d.height()
+        faceSize = (d.right() - d.left()) * (d.bottom() - d.top())
+        faceSizePercentage = (faceSize / (img_w * img_h)) * 100
+        xw1 = max(int(x1 - config.MARGIN * w), 0)
+        yw1 = max(int(y1 - config.MARGIN * h), 0)
+        xw2 = min(int(x2 + config.MARGIN * w), img_w - 1)
+        yw2 = min(int(y2 + config.MARGIN * h), img_h - 1)
+        # cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
+        cv2.imwrite(''.join(image_file_path.split('.')[:-1]) + '-cropped-' + str(i) + '.jpg',
+                    img[yw1:yw2, xw1:xw2])
+        faces[i, :, :, :] = cv2.resize(img[yw1:yw2 + 1, xw1:xw2 + 1, :], (
+            config.RESNET50_DEFAULT_IMG_WIDTH,
+            config.RESNET50_DEFAULT_IMG_WIDTH)) / 255.00
+
+        face_boundary = (d.top(), d.right(), d.bottom(), d.left())
+        gender = detect_genders(face_boundary, input_img)
+
+        # FILTERING
+        female = gender["woman"] >= 0.5
+        # faceSizePercentage > config.MIN_FACE_SIZE_PERCENTAGE:
+        if female and faceSize > config.MIN_FACE_SIZE_PIXELS and idx[i] == 0:
+            filteredFaces.append((d, faceSize, faceSizePercentage, gender["woman"]))
+    results = []
+    for i, d in enumerate(filteredFaces):
+        bmiResult = predict_bmi([d[0]], img)
+        if len(bmiResult) > 0:
+            bmiResult = float(str(bmiResult[0][0]))
+        else:
+            bmiResult = None
+        ageResult = predict_age([d[0]], img)
+        if len(ageResult) > 0:
+            ageResult = float(str(ageResult[0]))
+        else:
+            ageResult = None
+
+        # beautyResult = predict_beauty([d[0]], img)
+        results.append({
+            "face": {
+                "left": d[0].left(),
+                "top": d[0].top(),
+                "right": d[0].right(),
+                "bottom": d[0].bottom(),
+                "size": d[1],
+                "sizePercentage": d[2]
+            },
+            "gender_woman": d[3],
+            "bmi": bmiResult,
+            "age": ageResult
+        })
+    return results