beta 1.0

pics downloaded improved; add paras of CD; loading alert improved;

Author: TwSphinx54

README.md

@@ -1,3 +1,10 @@
 # Intelligent Interpretation of Remote Sensing Images
 
-developing...
+## Update log  
+* [2022.6.28] **Beta 1.0 released**: Every basic function has been established.
+## TODO
+* Multi pictures processing
+* Semi-automatic completion of roads extracted
+* Smaller and better model of Change Detection
+* Slider prediction of Object Classification
+

functions/change_detection.py

@@ -17,7 +17,7 @@ def repair(img, o_threshold, h_threshold):
     return img_t.astype(int) * 255
 
 
-def change_detection(A, B, predictor):
+def change_detection(A, B, predictor, o_thres, h_thres):
     t1 = time.time()
     result = predictor.predict(img_file=(A, B))[0]
     t2 = time.time()
@@ -28,7 +28,7 @@ def change_detection(A, B, predictor):
 
     result = np.array(result_map, dtype=np.uint8)
     result = result * 255
-    result = repair(result, 200, 200)
+    result = repair(result, o_thres, h_thres)
     # result = repair(result, 200, 1000)
     label = (result / 255).astype(int)
     score = sum(map(sum, score_map[label == 1])) / (len(score_map[label == 1]) + 1)
@@ -40,10 +40,9 @@ def change_detection(A, B, predictor):
 
     img_a = cv.imread(A)
     img_b = cv.imread(B)
-    alpha_channel = np.ones(img_a.shape[:2], dtype=img_a.dtype) * 125
-    img_a_al = cv.merge((img_a, alpha_channel))
-    img_b_al = cv.merge((img_b, alpha_channel))
-    mixed = cv.add(img_a_al, img_b_al)
-    mixed = cv.add(mixed, alpha)
+    img_bo = img_b.copy()
+    img_b[label == 1] = [1, 0, 255]
+    mixed = cv.addWeighted(img_bo, 0.5, img_b, 0.5, 1)
+    mixed = cv.addWeighted(img_a, 0.5, mixed, 0.5, 1)
 
     return result, alpha, score, period, mixed

functions/object_classification.py

@@ -92,9 +92,12 @@ def object_classification(img_path, predictor):
                 len(score_map[types[num][:, :, 3] == 255]) + 1)
         areas[num] = len(types[num][types[num][:, :, 3] == 255])
 
-    alpha_channel = np.ones(img.shape[:2], dtype=img.dtype) * 255
-    img_BGRA = cv.merge((img, alpha_channel))
-    for k in range(4):
-        img_BGRA = cv.add(img_BGRA, types[k])
-
-    return res_img, types, scores, period, areas, img_BGRA
+    img_a = img.copy()
+    img_a[label_map == 0] = [0, 0, 255]
+    img_a[label_map == 1] = [142, 255, 30]
+    img_a[label_map == 2] = [255, 0, 60]
+    img_a[label_map == 3] = [0, 222, 255]
+    mixed = cv.addWeighted(img, 0.3, img_a, 0.7, 1)
+    mixed = cv.addWeighted(img, 0.3, mixed, 0.7, 1)
+
+    return res_img, types, scores, period, areas, mixed

functions/object_extraction.py

@@ -28,9 +28,11 @@ def object_extraction(img_path, predictor):
     res_img = lut[label_map]
     roads = add_alpha(res_img)
     scores = sum(map(sum, score_map[label_map == 1])) / (len(score_map[label_map == 1]) + 1)
-    alpha_channel = np.ones(img.shape[:2], dtype=img.dtype) * 255
-    img_BGRA = cv.merge((img, alpha_channel))
-    mixed = cv.add(img_BGRA, roads)
+
+    img_o = img.copy()
+    img[label_map == 1] = [1, 0, 255]
+    mixed = cv.addWeighted(img_o, 0.3, img, 0.7, 1)
+    mixed = cv.addWeighted(img_o, 0.3, mixed, 0.7, 1)
 
     '''
     IoU为0.59，Acc为0.78，Kappa系数为0.72, F1为0.74

process.py

@@ -16,6 +16,7 @@
 app = Flask(__name__, template_folder="./webpage", static_folder='./webpage', static_url_path="")
 app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 pro, model, loc, score, period, shape, areas = [0] * 7
+o_threshold, h_threshold = [200, 200]
 
 
 # animation = True
@@ -34,12 +35,14 @@ def welcome():
         animation = request.args.get('animation')
         return render_template('welcome.html', animation=animation)
     elif request.method == 'POST':
-        global pro, model
+        global pro, model, o_threshold, h_threshold
         pro = request.values['pro']
         if pro == '0':
             model = load_object_extraction(WEIGHT_FOLDER + '/object_extraction/')
         elif pro == '1':
             model = load_change_detection(WEIGHT_FOLDER + '/change_detection/')
+            o_threshold = int(request.values['o_thres'])
+            h_threshold = int(request.values['h_thres'])
         elif pro == '2':
             pro_type = int(request.values['type'])
             model = load_object_detection(WEIGHT_FOLDER + '/object_detection/' + MODELS[pro_type])
@@ -72,7 +75,7 @@ def upload_file():
                     B = os.path.join(app.config['UPLOAD_FOLDER'], filename2)
                     file1.save(A)
                     file2.save(B)
-                    res, alpha, score, period, mixed = change_detection(A, B, model)
+                    res, alpha, score, period, mixed = change_detection(A, B, model, o_threshold, h_threshold)
                     shape = list(res.shape)
                     cv.imwrite(UPLOAD_FOLDER + '/result.png', res)
                     cv.imwrite(UPLOAD_FOLDER + '/change.png', alpha)

webpage/index.html

@@ -63,6 +63,7 @@
     <div class="container">
         <div class="loader"></div>
         <div class="loading_text">解译中</div>
+        <div class="loading_time"></div>
     </div>
 </div>
 <script>
@@ -99,6 +100,7 @@
     const load_bg = document.getElementById('load_bg');
     const container = document.getElementsByClassName('container')[0];
     const loading_text = document.getElementsByClassName('loading_text')[0];
+    const loading_time = document.getElementsByClassName('loading_time')[0];
     const rs_input = document.getElementById('rs_input');
     const rs_input_bg = document.getElementById('rs_input_bg');
     const rs_input_txt = document.getElementById('rs_input_txt');
@@ -423,6 +425,8 @@
                 load_bg.classList.add('show');
                 container.classList.add('show');
                 loading_text.classList.add('show');
+                loading_time.innerHTML = '处理将会耗时一到两分钟';
+                loading_time.classList.add('show');
 
                 let xhr = new XMLHttpRequest();
                 xhr.open('POST', url, true);
@@ -485,6 +489,10 @@
                 load_bg.classList.add('show');
                 container.classList.add('show');
                 loading_text.classList.add('show');
+                if (method === 0){
+                    loading_time.innerHTML = '处理将会耗时一到两分钟';
+                    loading_time.classList.add('show');
+                }
 
                 let xhr = new XMLHttpRequest();
                 xhr.open('POST', url, true);

webpage/styles/index.css

@@ -408,6 +408,30 @@
     line-height: 500px;
 }
 
+.loading_time{
+    position: absolute;
+    width: 100%;
+    height: 100%;
+    line-height: 700px;
+    text-align: center;
+    font-size: 20px;
+    font-family: Ubuntu, "Microsoft YaHei", serif;
+    font-weight: bold;
+    color: rgba(0, 0, 0, 0.7);
+
+    opacity: 0;
+
+    cursor: default;
+    animation: ease;
+    transition: all 0.5s 0.5s;
+    -webkit-transition: all 0.5s 0.5s;
+}
+
+.loading_time.show {
+    line-height: 600px;
+    opacity: 100%;
+}
+
 #rs_input {
     width: 250px;
     height: 100px;

webpage/styles/wel.css

@@ -706,7 +706,7 @@
 }
 
 .intro {
-    background-color: rgba(0, 0, 0, 0.2);
+    background-color: rgba(255, 255, 255, 0.2);
     backdrop-filter: blur(12px);
     -webkit-backdrop-filter: blur(12px);
     border: 0px solid rgba(255, 255, 255, 0.18);
@@ -844,6 +844,119 @@
     color: rgba(255, 255, 255, 0.6);
 }
 
+#cd_para_wrapper {
+    overflow: hidden;
+    height: 200px;
+    width: 400px;
+    position: absolute;
+    display: flex;
+    flex-direction: column;
+    justify-content: right;
+    align-items: end;
+    right: 50%;
+    /*border: 1px black solid;*/
+}
+
+.cd_para {
+    width: 100%;
+    height: 50px;
+    display: flex;
+    justify-content: right;
+    align-items: end;
+
+    cursor: default;
+    animation: ease-out;
+    transition: all 0.5s;
+    -webkit-transition: all 0.5s;
+}
+
+.cd_para_name {
+    background-color: rgba(255, 255, 255, 0.2);
+    backdrop-filter: blur(12px);
+    -webkit-backdrop-filter: blur(12px);
+    border: 0px solid rgba(255, 255, 255, 0.18);
+    box-shadow: rgba(142, 142, 142, 0.19) 0px 6px 15px 0px;
+    -webkit-box-shadow: rgba(142, 142, 142, 0.19) 0px 6px 15px 0px;
+
+    height: 50px;
+    padding: 5px;
+    font-size: 30px;
+    font-weight: bold;
+    line-height: 40px;
+    font-family: Ubuntu, "Microsoft YaHei", serif;
+    text-align: center;
+    box-sizing: border-box;
+    overflow: hidden;
+
+    width: 0;
+    color: rgba(0, 0, 0, 0);
+
+    cursor: default;
+    animation: ease-out;
+    transition: all 0.5s;
+    -webkit-transition: all 0.5s;
+}
+
+.cd_para_name.show0 {
+    width: 40%;
+    color: rgba(0, 0, 0, 0.6);
+    transition: all 0.5s, width 0.5s 0.6s, color 0.3s 0.9s;
+    -webkit-transition: all 0.5s, width 0.5s 0.6s, color 0.3s 0.9s;
+}
+
+.cd_para_name.show1 {
+    width: 40%;
+    color: rgba(0, 0, 0, 0.6);
+    transition: all 0.5s, width 0.5s 0.7s, color 0.3s 1s;
+    -webkit-transition: all 0.5s, width 0.5s 0.7s, color 0.3s 1s;
+}
+
+.cd_para_name.active{
+    background-color: rgba(255, 255, 255, 0.7);
+}
+
+.cd_para_value {
+    background-color: rgba(255, 255, 255, 0.4);
+    backdrop-filter: blur(12px);
+    -webkit-backdrop-filter: blur(12px);
+    border: 0px solid rgba(255, 255, 255, 0.18);
+    box-shadow: rgba(142, 142, 142, 0.19) 0px 6px 15px 0px;
+    -webkit-box-shadow: rgba(142, 142, 142, 0.19) 0px 6px 15px 0px;
+
+    height: 50px;
+    outline: none;
+    padding: 5px;
+    font-size: 30px;
+    font-weight: bold;
+    line-height: 40px;
+    font-family: Ubuntu, "Microsoft YaHei", serif;
+    text-align: center;
+    box-sizing: border-box;
+    overflow: hidden;
+
+    width: 0;
+    color: rgba(0, 0, 0, 0);
+
+    cursor: text;
+    animation: ease-out;
+    transition: all 0.5s;
+    -webkit-transition: all 0.5s;
+}
+
+.cd_para_value.show0 {
+    width: 60%;
+    color: rgba(0, 0, 0, 0.6);
+    transition: all 0.5s, width 0.5s 0.6s, color 0.3s 0.9s;
+    -webkit-transition: all 0.5s, width 0.5s 0.6s, color 0.3s 0.9s;
+}
+
+.cd_para_value.show1 {
+    width: 60%;
+    color: rgba(0, 0, 0, 0.6);
+    transition: all 0.5s, width 0.5s 0.7s, color 0.3s 1s;
+    -webkit-transition: all 0.5s, width 0.5s 0.7s, color 0.3s 1s;
+}
+
 .go {
     background-color: rgba(0, 0, 0, 0.2);
     backdrop-filter: blur(12px);