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README (3).md

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+---
+title: Object & Color Detection In Video
+emoji:  😏 
+colorFrom: green
+colorTo: purple
+sdk: gradio
+sdk_version: 3.11
+app_file: app.py
+pinned: false
+license: openrail
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app (4).py

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+import cv2
+import gradio as gr
+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)

coco.names

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+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+street sign
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+hat
+backpack
+umbrella
+shoe
+eye glasses
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+plate
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+mirror
+dining table
+window
+desk
+toilet
+door
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+blender
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
+hair brush

frozen_inference_graph.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3c95e720eed1bbaf264048ab8fbe6765e5b3a64fafe64020cd53ecd14ccf2c58
+size 13448454

gitattributes (2)

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requirements (3).txt

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+opencv-python
+fast_colorthief
+webcolors
+Pillow
+numpy