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Configuration error
Configuration error
import cv2 | |
import gradio as gr | |
import fast_colorthief | |
import webcolors | |
from PIL import Image | |
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) |