Progress Clean Up

app.py

@@ -3,44 +3,47 @@ import time
 import os
 import requests
 
-treashold = float(5)
+treashold = float(os.getenv('FACE_TRASHOLD', '0,5'))
 sever = os.getenv('API_ROOT', 'localhost')
 root_api_url = 'http://{}/local'.format(sever)
 
 try:
-    r = requests.get('{}/detections/anonymize'.format(root_api_url))
-    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    while True:
+        r = requests.get('{}/anonymize'.format(root_api_url))
+        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
 
-    for detection in r.json():
-        img_path = "/data/{}".format(detection.filename)
-        img = cv2.imread(img_path)
-        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        faces, rejects, weights = face_cascade.detectMultiScale3(gray, 1.1, 4, outputRejectLevels = 1)
+        for detection in r.json():
+            img_path = "/data/{}".format(detection.source_image_path)
+            img = cv2.imread(img_path)
+            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            faces, rejects, weights = face_cascade.detectMultiScale3(gray, 1.1, 4, outputRejectLevels = 1)
 
-        i = 0
-        for (x, y, w, h) in faces:
-            confidence = float(weights[i])
-            if (confidence > treashold):
-                face_roi = img[y:y+h, x:x+w]
-                blurred_face = cv2.GaussianBlur(face_roi, (99, 99), 20)
-                img[y:y+h, x:x+w] = blurred_face
+            i = 0
+            for (x, y, w, h) in faces:
+                confidence = float(weights[i])
+                if (confidence > treashold):
+                    face_roi = img[y:y+h, x:x+w]
+                    blurred_face = cv2.GaussianBlur(face_roi, (99, 99), 20)
+                    img[y:y+h, x:x+w] = blurred_face
 
-                print(weights[i])
-                text = "{}".format(weights[i])
-                #cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
-                #cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
+                    print(weights[i])
+                    text = "{}".format(weights[i])
+                    #cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
+                    #cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
 
-            i = i +1
+                i = i +1
+            
+            extension = img_path[img_path.index(".")+1:]
+            new_img_path = img_path.replace(extension, ('_anonym{}'.format(extension)))
+            cv2.imwrite(new_img_path, img)
+
+            data = {
+                "id": detection.id,
+                "source_image_path": new_img_path
+            }
+            response = requests.post('{}/anonymize'.format(root_api_url), json=data)
         
-        extension = img_path[img_path.index(".")+1:]
-        new_img_path = img_path.replace(extension, ('_anonym{}'.format(extension)))
-        cv2.imwrite(new_img_path, img)
-
-        data = {
-            "detection_id": detection.id,
-            "detection_path": new_img_path
-        }
-        response = requests.post('{}/detections/anonymized'.format(root_api_url), json=data)
+        time.sleep(3)
 
 except Exception as e:
     print("An exception occurred")