Upload 3 files

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d025427b1b29ea551bf60c80d148f93dac0d121feaa46bc9d534a781e1c3cffb
+size 22503193

handler.py

@@ -1,78 +1,88 @@
 from typing import Dict, List, Any
-import supervision as sv
 import urllib.request
 import numpy as np
 import cv2
 import base64
-from inference_sdk import InferenceHTTPClient
+from ultralytics import YOLO
 
 
 class EndpointHandler:
-    def __init__(self, key):  # pass api key to model
+    def __init__(self): #pass api key to model
+        # self.CLIENT = InferenceHTTPClient(
+        #     api_url="https://detect.roboflow.com",
+        #     api_key=key
+        # )
+        # print("checkpoint 1")
         pass
-
+        
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         inputs = data.get("inputs")
         isurl = inputs.get("isurl")
         path = inputs.get("path")
-        key = inputs.get("key")
-        ###########################  Load Image  #################################
-        print("load image")
-        CLIENT = InferenceHTTPClient(api_url="https://detect.roboflow.com", api_key=key)
-        print("get client")
-        if isurl:  # for url set isurl = 1
+        
+        model = YOLO("./best.pt")
+###########################  Load Image  #################################       
+        if(isurl): # for url set isurl = 1
+            print("checkpoint 2-1")
             req = urllib.request.urlopen(path)
+            print("checkpoint 2-2")
             arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
-            img = cv2.imdecode(arr, -1)  # 'Load it as it is'
-        else:  # for image file
+            print("checkpoint 2-3")
+            img = cv2.imdecode(arr, -1) # 'Load it as it is'
+        else: # for image file
             img = cv2.imread(path)
-        ###########################################################################
-
-        ###########################  Model Detection  #################################
-        # change model_id to use a different model
-        # can try:
+            
+        print("checkpoint 2")
+###########################################################################       
+            
+        
+###########################  Model Detection  #################################
+        # change model_id to use a different model 
+        # can try: 
+        # clothing-detection-s4ioc/6 //good
         # clothing-segmentation-dataset/1
         # t-shirts-detector/1
-        # clothing-detection-s4ioc/6
         # mainmodel/2
-        print("start model detection")
-        result = CLIENT.infer(path, model_id="clothing-detection-s4ioc/6")
-        detections = sv.Detections.from_inference(result)
+        #result = self.CLIENT.infer(path, model_id="mainmodel/2")
+        result = model(img)
+        #annotated_frame = result[0].plot()
+        detections = result[0].boxes
+        #print(result[0].boxes.xyxy)
+        #cv2.imshow("YOLOv8 Inference", annotated_frame)
+        # print(result)
+        #cv2.waitKey(0)
+        #detections = sv.Detections.from_inference(result)
         # print(detections)
-        ###########################################################################
+        
+        print("checkpoint 3")
+###########################################################################
+
 
-        ###########################  Data proccessing  #################################
+###########################  Data proccessing  #################################
         # only pass the first detection
         # change 1 -> to len(detections.xyxy) to pass all photos
-        print("Data processing")
-        if detections.confidence.size == 0:
+        if(detections.xyxy.size == 0): 
             return "Not Found"
         else:
-            x1, y1, x2, y2 = (
-                int(detections.xyxy[0][0]),
-                int(detections.xyxy[0][1]),
-                int(detections.xyxy[0][2]),
-                int(detections.xyxy[0][3]),
-            )
-            clothes = img[y1:y2, x1:x2]
-            retval, buffer = cv2.imencode(".jpg", clothes)
+            x1, y1, x2, y2 = int(detections.xyxy[0][0]), int(detections.xyxy[0][1]), int(detections.xyxy[0][2]), int(detections.xyxy[0][3])
+            clothes = img[y1: y2, x1: x2]
+            retval , buffer = cv2.imencode('.jpg', clothes)   
+            cv2.imwrite("result.jpg", clothes)  
         # create base 64 object
-        jpg_as_text = base64.b64encode(buffer).decode("utf-8") # Decode bytes to string
-        ###########################################################################
-        return jpg_as_text
-
-
+        jpg_as_text = base64.b64encode(buffer)
+        print("checkpoint 4")
 ###########################################################################
+        return jpg_as_text
+###########################################################################    
+    
+    
+    
+#  test run  
+# Model = Image_detect()
+# test file image
+# print(Model("test_images/test6.jpg", 0))
 
+#test url
+# print(Model("http://10.10.2.100/cam-lo.jpg", 1))
 
-# data = {
-#     "inputs": {
-#         "isurl": True,
-#         "path": "http://192.168.10.20/cam-hi.jpg",
-#         "key": "iJuYzEzNEFSaQq4e0hfE",
-#     }
-# }
 
-# #  test run
-# Model = EndpointHandler()
-# print(Model(data))

requirements.txt

@@ -1,4 +1,3 @@
-supervision
 numpy
 opencv-python
-inference_sdk
+ultralytics