Update app.py

app.py

@@ -4,101 +4,89 @@ import fast_colorthief
 import webcolors
 from PIL import Image
 import numpy as np
-thres = 0.45 # Threshold to detect object
-
-
-
-def Detection(filename):
-  cap = cv2.VideoCapture(filename)
-  framecount=0  
-
-  cap.set(3,1280)
-  cap.set(4,720)
-  cap.set(10,70)
-
-  error="in function 'cv::imshow'"
-  classNames= []
-  FinalItems=[]
-  classFile = 'coco.names'
-  with open(classFile,'rt') as f:
-    #classNames = f.read().rstrip('n').split('n')
-    classNames = f.readlines()
-
-
-  # remove new line characters
-  classNames = [x.strip() for x in classNames]
-  print(classNames)
-  configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
-  weightsPath = 'frozen_inference_graph.pb'
-
-
-  net = cv2.dnn_DetectionModel(weightsPath,configPath)
-  net.setInputSize(320,320)
-  net.setInputScale(1.0/ 127.5)
-  net.setInputMean((127.5, 127.5, 127.5))
-  net.setInputSwapRB(True)
-
-  while True:
-    success,img = cap.read()
-
-
-    
-    # #Colour
-    try:
-      image = Image.fromarray(img)
-      image = image.convert('RGBA')
-      image = np.array(image).astype(np.uint8)
-      palette=fast_colorthief.get_palette(image)
-    
-
-      for i in range(len(palette)):
-        diff={}
-        for color_hex, color_name in webcolors.CSS3_HEX_TO_NAMES.items():
-          r, g, b = webcolors.hex_to_rgb(color_hex)
-          diff[sum([(r - palette[i][0])**2,
-                    (g - palette[i][1])**2,
-                    (b - palette[i][2])**2])]= color_name
-        if FinalItems.count(diff[min(diff.keys())])==0:
-          FinalItems.append(diff[min(diff.keys())])
-
-    except:
-      pass
-        
-    try:
-        classIds, confs, bbox = net.detect(img,confThreshold=thres)
-    except:
-        pass
-    print(classIds,bbox)
-    try:
-      if len(classIds) != 0:
-          for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):
-    
-              #cv2.rectangle(img,box,color=(0,255,0),thickness=2)
-              #cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),
-              #cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
-              #cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),
-              #cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
-              if FinalItems.count(classNames[classId-1]) == 0:
-                FinalItems.append(classNames[classId-1])
-            
-      
-      #cv2.imshow("Output",img)
-      cv2.waitKey(10)
-      if framecount>cap.get(cv2.CAP_PROP_FRAME_COUNT):
-          break
-      else:
-          framecount+=1
-    except  Exception as err:
-      print(err)
-      t=str(err)
-      if t.__contains__(error):
-        break
-
-  print(FinalItems)
-  return str(FinalItems)
-
-interface = gr.Interface(fn=Detection, 
-                        inputs=["video"],
-                         outputs="text", 
-                        title='Object & Color Detection in Video')
-interface.launch(inline=False,debug=True)
+
+thres = 0.45  # Threshold to detect object
+
+
+def Detection(filename, confidence_threshold=0.45):
+    cap = cv2.VideoCapture(filename)
+    framecount = 0
+
+    cap.set(3, 1280)
+    cap.set(4, 720)
+    cap.set(10, 70)
+
+    error = "in function 'cv::imshow'"
+    classNames = []
+    FinalItems = []
+    classFile = 'coco.names'
+    with open(classFile, 'rt') as f:
+        classNames = f.readlines()
+
+    # remove new line characters
+    classNames = [x.strip() for x in classNames]
+
+    configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
+    weightsPath = 'frozen_inference_graph.pb'
+
+    net = cv2.dnn_DetectionModel(weightsPath, configPath)
+    net.setInputSize(320, 320)
+    net.setInputScale(1.0 / 127.5)
+    net.setInputMean((127.5, 127.5, 127.5))
+    net.setInputSwapRB(True)
+
+    while True:
+        success, img = cap.read()
+
+        # #Colour
+        try:
+            image = Image.fromarray(img)
+            image = image.convert('RGBA')
+            image = np.array(image).astype(np.uint8)
+            palette = fast_colorthief.get_palette(image)
+
+            for i in range(len(palette)):
+                diff = {}
+                for color_hex, color_name in webcolors.CSS3_HEX_TO_NAMES.items():
+                    r, g, b = webcolors.hex_to_rgb(color_hex)
+                    diff[sum([(r - palette[i][0]) ** 2,
+                              (g - palette[i][1]) ** 2,
+                              (b - palette[i][2]) ** 2])] = color_name
+                if FinalItems.count(diff[min(diff.keys())]) == 0:
+                    FinalItems.append(diff[min(diff.keys())])
+
+        except:
+            pass
+
+        try:
+            classIds, confs, bbox = net.detect(
+                img, confThreshold=confidence_threshold)
+        except:
+            pass
+
+        try:
+            if len(classIds) != 0:
+                for classId, confidence, box in zip(classIds.flatten(), confs.flatten(), bbox):
+                    if FinalItems.count(classNames[classId - 1]) == 0:
+                        FinalItems.append(classNames[classId - 1])
+
+            if framecount > cap.get(cv2.CAP_PROP_FRAME_COUNT):
+                break
+            else:
+                framecount += 1
+        except Exception as err:
+            print(err)
+            t = str(err)
+            if t.__contains__(error):
+                break
+
+    print(FinalItems)
+    return str(FinalItems)
+
+
+interface = gr.Interface(fn=Detection,
+                         inputs=["video", gr.inputs.Slider(0.01, 1, step=0.01, label="Limiar de Confiança")],
+                         outputs="text",
+                         title='Detecção de Objetos e Cores em Vídeo',
+                         description='Este aplicativo detecta objetos em um vídeo e identifica suas cores. Carregue um vídeo e ajuste o limiar de confiança para a detecção de objetos.')
+interface.launch(inline=False, debug=True)