Merge pull request 'Used retina' (#1) from Retina into main

Reviewed-on: #1

.gitignore

@@ -1 +1,2 @@
 .venv
+.conda

README.md

@@ -23,3 +23,7 @@ services:
       - ${PWD}/app_data/:/app/data/
 networks: {}
 ```
+
+```python
+pip3 install -r requirements.txt
+```

app.py

@@ -3,8 +3,9 @@ import time
 import os
 import requests
 import json
+from retinaface import RetinaFace
 
-treashold = float(os.getenv('FACE_TRASHOLD', '0.5'))
+treashold = float(os.getenv('FACE_TRASHOLD', '0.9'))
 sever = os.getenv('API_ROOT', 'localhost:8000')
 root_api_url = 'http://{}/local'.format(sever)
 base_path = '/app/data/'
@@ -18,7 +19,6 @@ try:
         print("LOOP Started !!!", flush=True)
         
         r = requests.get('{}/anonymize'.format(root_api_url))
-        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
         # print(r.text, flush=True)
         response =  r.json()
 
@@ -26,36 +26,43 @@ try:
             print(str(detection['id']) + " Anonymization Started", flush=True)
             
             img_path = "{}{}".format(base_path, detection['source_image_path'])
-
+            #print(img_path, flush=True)
             #Skip Non existing files
             if (not os.path.exists(img_path)):
                 continue
 
             img = cv2.imread(img_path)
-            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            faces = RetinaFace.detect_faces(img, treashold)
-            faces, rejects, weights = face_cascade.detectMultiScale3(gray, 1.1, 4, outputRejectLevels = 1)
 
             #Count faces  is null dont anonimize file
             print(str(len(faces)) + " Faces Found" , flush=True)
-            if (len(faces) < 0):
+            if (len(faces) <= 0):
+                data = {
+                    "id": detection['id'],
+                    "source_image_path": img_path.replace(base_path,""),
+                    "face_found": 0,
+                }
+                response = requests.post('{}/anonymize'.format(root_api_url), json=data)
+                print(str(detection['id']) + " Anonymized 0 faces" , flush=True)
                 continue
 
             i = 0
-            for (x, y, w, h) in faces:
+            for (key) in faces:
-                confidence = float(weights[i])
+                confidence = faces[key]["score"]
                 print(str(confidence) + " Faces Confidence" , flush=True)
-                
+                x1, y1 = min(faces[key]["facial_area"][0], faces[key]["facial_area"][2]) - 2, min(faces[key]["facial_area"][1], faces[key]["facial_area"][3]) - 2
-                if (confidence > treashold):
+                x2, y2 = max(faces[key]["facial_area"][0], faces[key]["facial_area"][2]) + 2, max(faces[key]["facial_area"][1], faces[key]["facial_area"][3]) + 2
-                    face_roi = img[y:y+h, x:x+w]
+                x1, x2 = max(x1, 0), min(x2, img.shape[1])
-                    blurred_face = cv2.GaussianBlur(face_roi, (99, 99), 50)
+                y1, y2 = max(y1, 0), min(y2, img.shape[0])
-                    img[y:y+h, x:x+w] = blurred_face
+                face_roi = img[y1:y2, x1:x2]
-                    print("Faces Anonymized" , flush=True)
+                blurred_face = cv2.GaussianBlur(face_roi, (99, 99), 50)
-                    text = "{}".format(weights[i])
+                img[y1:y2, x1:x2] = blurred_face
-                    #cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
+                print("Faces Anonymized" , flush=True)
-                    #cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
 
                 i = i +1
             
+            #cv2.imshow("test_img", img)
+            #cv2.waitKey(0)
             extension = img_path[img_path.index(".")+1:]
             
             new_img_path = img_path

requirements.txt

@@ -1,2 +1,5 @@
 opencv-python
+numpy
 requests
+retina-face
+tf-keras