modify bridge

app/serve.py

@@ -1,6 +1,6 @@
 import tensorflow as tf
 import numpy as np
-
+import keras.models
 
 def serve_unet_model():
     TFLITE_MODEL = "../app/UNet_25_Crack.tflite"
@@ -25,3 +25,9 @@ def serve_rcnn_model():
             od_graph_def.ParseFromString(serialized_graph)
             tf.import_graph_def(od_graph_def, name='')
     return detection_graph
+
+
+def serve_bridge_model():
+    mp = "../app/crack_model.h5"
+    model = keras.models.load_model(mp)
+    return model

app/server.py

@@ -8,8 +8,9 @@ from PIL import Image, ImageDraw
 import tensorflow as tf
 import ops as utils_ops
 import visualization_utils as vis_util
+import cv2
 
-from serve import serve_unet_model
+from serve import serve_unet_model, serve_bridge_model
 from serve import serve_rcnn_model
 
 app = Flask(__name__)
@@ -246,5 +247,95 @@ def index():
             return jsonify(data)
     return "Road Damage Detection"
 
+
+@app.route("/predict/bridge", methods=["POST"])
+def bridge():
+    if flask.request.method == "POST":
+        if flask.request.files.get("image"):
+            pred_data_colr = []
+            pred_data_inv = []
+            img_src = cv2.imread(flask.request.files["image"], 0)
+            image_dst = resize_keep_aspect_ratio(img_src, (227, 227))
+            bi_inv, colored_img = process_image(image_dst)
+            pred_data_colr.append(colored_img)
+            pred_data_inv.append(bi_inv)
+            final_pred_colr = np.array(pred_data_colr).reshape((len(pred_data_colr), 227, 227, 1))
+            final_pred_inv = np.array(pred_data_inv).reshape((len(pred_data_inv), 227, 227, 1))
+            is_crack = predict_image_util(final_pred_inv)
+            image_np = load_image_into_numpy_array(img_src)
+            img = Image.fromarray(image_np.astype("uint8"))
+            img = img.resize((128, 128))
+            raw_bytes = io.BytesIO()
+            img.save(raw_bytes, "JPEG")
+            raw_bytes.seek(0)
+            img_byte = raw_bytes.getvalue()
+            img_str = base64.b64encode(img_byte)
+            data = {
+                "result": is_crack,
+                "img": img_str.decode('utf-8')
+            }
+            return jsonify(data)
+        else:
+            data = {
+                "code": 10001,
+                "msg": "Could not find image"
+            }
+            return jsonify(data)
+    return "Bridge Detection"
+
+
+def predict_image_util(final_pred_inv):
+    model = serve_bridge_model()
+    img_test = (final_pred_inv[0].reshape((1, 227, 227, 1)))
+    raw_predicted_label = model.predict(img_test, batch_size=None, verbose=0, steps=None)[0][0]
+
+    predicted_label = 1
+    if raw_predicted_label < 0.8:
+        predicted_label = 0
+
+    predicted_label_str = 'Crack'
+    if predicted_label == 0:
+        predicted_label_str = 'No Crack'
+
+    print('Raw Predicted Label(Numeric): ' + str(raw_predicted_label))
+    print('Predicted Label : ' + predicted_label_str)
+    return predicted_label
+
+
+def process_image(img):
+    ret, bi_inv = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
+    return bi_inv, img
+
+
+def resize_keep_aspect_ratio(image_src, dst_size):
+    src_h, src_w = image_src.shape[:2]
+    dst_h, dst_w = dst_size
+
+    # 判断应该按哪个边做等比缩放
+    h = dst_w * (float(src_h) / src_w)  # 按照ｗ做等比缩放
+    w = dst_h * (float(src_w) / src_h)  # 按照h做等比缩放
+
+    h = int(h)
+    w = int(w)
+
+    if h <= dst_h:
+        image_dst = cv2.resize(image_src, (dst_w, int(h)))
+    else:
+        image_dst = cv2.resize(image_src, (int(w), dst_h))
+
+    h_, w_ = image_dst.shape[:2]
+
+    top = int((dst_h - h_) / 2)
+    down = int((dst_h - h_ + 1) / 2)
+    left = int((dst_w - w_) / 2)
+    right = int((dst_w - w_ + 1) / 2)
+
+    value = [0, 0, 0]
+    border_type = cv2.BORDER_CONSTANT
+    image_dst = cv2.copyMakeBorder(image_dst, top, down, left, right, border_type, None, value)
+
+    return image_dst
+
+
 if __name__ == "__main__":
     app.run()

install.sh

@@ -0,0 +1,4 @@
+#/bin/bash
+
+pip3 install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/
+

requirements.txt

@@ -1,4 +1,7 @@
 Flask==1.1.2
-numpy==1.18.4
+numpy==1.19.5
 Pillow==7.1.2
 six==1.15.0
+tensorflow==2.5.1
+opencv-contrib-python==4.5.3.56
+opencv-python==4.5.3.56