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Configuration error
Configuration error
File size: 2,893 Bytes
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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) |