Update app.py

app.py

@@ -4,89 +4,101 @@ import fast_colorthief
 import webcolors
 from PIL import Image
 import numpy as np
-
-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)
+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)