Linked Images endpoint to the Model (#90)

* Updating api branch to include model (#85)

* add nginx configuration and deployment script

* update deployment script and nginx configuration as well as adding deplyoment information to README

* add more urls and prevent overwriting images issue

* enable scraping for base64 encoded images

* move company urls to separate file + small refector

* refactor code

* collect adidas images

* add correct file extension to images

* restore original images

* move image bytes logic

* save image with file extension

* remove unuseful images

* rename some images

* comment out urls used

* Feat: Update docker building for model requirements (#69)

update docker building with new requirements and rename models directory to avoid conflicts

* add flake8 configuration to ignore unused imports from __init__.py

* add make_temp_directory helper for temporarily saving files

* Added 'images' app (#70)

* Chore: Bump Django and OpenCV versions (#75)

* bump django version to avoid vulnerabilities

* bump open cv version to avoid vulnerabilities

* collect under armour images (#72)

* Bugfix: Update deployment script for larger docker images (#78)

* force remove dangling docker resources and remove call to pull images before building

* add index url to install CPU version of torch by prebuilt wheel

* combine environment instructions and add all unnecessary files to docker ignore

* Update README with CI/CD status badges and database migration instructions (#79)

add ci status badges and update installation instructions to migrate

* Added model for detection and processing (#80)

* Added model for detection and processing

* Removed and added changes

* Commented out and removed code to pass make lint

* Uncomment __init__.py

---------

Co-authored-by: Sam <[email protected]>

---------

Co-authored-by: Sam <[email protected]>
Co-authored-by: temi-oye <[email protected]>
Co-authored-by: Peng Hui Liu <[email protected]>

* Initial attempt to link model to endpoint

* Trying to fix failures and changing variable and method names to match rest of the repository

* Linked endpoint to the model (Hopefully)

* Fixed the no 400 error code issue

* Fix spelling error

* fixed issue of writing image to temporary directory

* NoChangesAddedComment

* Removed redundant comments

* Removed redundant comments

* Formatting changes to adhere to snake_case convention and removing redundant printing

* More snake_case changes

* Naming convention changes

---------

Co-authored-by: Sam <[email protected]>
Co-authored-by: temi-oye <[email protected]>
Co-authored-by: Peng Hui Liu <[email protected]>
Co-authored-by: egaynor <[email protected]>

api/images/services/__init__.py

@@ -0,0 +1,2 @@
+# from .ImageProcessingService import ImageProcessingService
+# from .LogoDetectionService import LogoDetectionService

api/images/services/image_processing_service.py

@@ -0,0 +1,16 @@
+from api.utils.make_temp_directory import make_temp_directory
+
+from .logo_detection_service import LogoDetectionService
+
+
+class ImageProcessingService:
+    def process_images(self, images):
+        detections = {}
+
+        with make_temp_directory() as temp_directory:
+            for image in images:
+                file_path = temp_directory + "/" + image.name
+                with open(file_path, "wb") as file:
+                    file.write(image.file.read())
+                detections.update({image.name: LogoDetectionService().detect_in_image(file_path)})
+        return detections

api/images/services/logo_detection_service.py

@@ -0,0 +1,19 @@
+from ml.models.logo_detection.process import process as detect_logos
+
+# from rest_framework import serializers
+
+
+class LogoDetectionService:
+    def detect_in_image(self, image_path):
+        inference_results = []
+        inferences = detect_logos(image_path)  # List LogoDetectionInference objects (ml.models.logo_detection.data)
+        for inference in inferences:
+            bbox = {
+                "x": inference.bounding_box.x,
+                "y": inference.bounding_box.y,
+                "w": inference.bounding_box.w,
+                "h": inference.bounding_box.h,
+            }
+            detection = {"bbox": bbox, "confidence": inference.confidence}
+            inference_results.append(detection)
+        return inference_results

api/images/views.py

@@ -1,16 +1,60 @@
-from rest_framework import generics
+from rest_framework import generics, status
 from rest_framework.response import Response
 
+from .services.image_processing_service import ImageProcessingService
+
 
 # Create your views here.
 class PredictionsViewSet(generics.GenericAPIView):
     def post(self, request):
+        images = request.FILES
+        if not images:
+            return Response(status=status.HTTP_400_BAD_REQUEST)
+        images = images.values()
+        return Response(data=ImageProcessingService().process_images(images))
+
+
+"""
+EVA PREVIOUS CODE - does not work lol
+import os
+import tempfile
+
+from django.http import FileResponse
+from rest_framework import generics, status
+from rest_framework.response import Response
+
+from .services.ImageProcessingService import ImageProcessingService
+
+
+# Create your views here.
+class PredictionsViewSet(generics.GenericAPIView):
+    def post(self, request):
+        print(request.data)
+        images = request.FILES.values()
+        if not images:
+            return Response(status=status.HTTP_400_BAD_REQUEST)
+
+        temp_dir = tempfile.mkdtemp()
+        responses = []
+        output = []
+        for image in images:
+            filename = image.name
+            filepath = os.path.join(temp_dir, filename)
+
+            with open(filepath, "wb") as f:
+                for c in image.chunks():
+                    f.write(c)
+
+            output_path = ImageProcessingService(filepath)
+            with open(output_path, "rb") as f:
+                output_image = f.read()
+            responses.append(output_path)
+            output.append(output_image)
+        print(responses)
         return Response(
             data={
-                "x1": 30,
-                "x2": 78,
-                "y1": 79,
-                "y2": 89,
-                "Confidence": 97.96,
+                "images": responses,
             }
         )
+
+"""