import numpy as np
import cv2
import argparse
from collections import deque
from pathlib import Path
#c_path = Path.cwd() / Path("chromedriver")
#print('c_path',c_path)
c_path='chromedriver.exe'
from agora_community_sdk import AgoraRTC
client = AgoraRTC.create_watcher("49c2343ef91c4517a6c6a93a445e0a43", str(c_path))
client.join_channel("gesture")
users = client.get_users() # Gets references to everyone participating in the call
for i in range(len(users)):
user1 = users[i] # Can reference users in a list
print('-----user1------',user1)
#cap=cv2.VideoCapture(0)
binary_image = user1.frame.convert("RGB") # Gets the latest frame from the stream as a PIL image
binary_image.save("test.jpeg", "jpeg")
pts = deque(maxlen=64)
Lower_green = np.array([24,50,50])
Upper_green = np.array([42,255,255])
from agora_community_sdk import AgoraRTC
import imutils
import os
import cv2
import numpy as np
import rect
client = AgoraRTC.create_watcher('4970dca4fd784a8683966e33bb37cb72', '\chromedriver.exe')
client.join_channel("test")
users = client.get_users() # Gets references to everyone participating in the call
user1 = users[0] # Can reference users in a list
binary_image = user1.frame # Gets the latest frame from the stream as a PIL image
binary_image.save("test.png") #Replace test.png with your file name
# image = cv2.imread("test.png")
# image = cv2.resize(image, (1500, 880))
# orig = image.copy()
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# edged = cv2.Canny(blurred, 0, 50)
# orig_edged = edged.copy()
# (contours, _) = cv2.findContours(edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
# contours = sorted(contours, key=cv2.contourArea, reverse=True)
from agora_community_sdk import AgoraRTC
from imageai.Prediction.Custom import CustomImagePrediction
import os
client = AgoraRTC.create_watcher("<insert app id here>", "chromedriver.exe")
client.join_channel("naynika")
users = client.get_users(
) # Gets references to everyone participating in the call
user1 = users[0] # Can reference users in a list
binary_image = user1.frame # Gets the latest frame from the stream as a PIL image
#with open("test.jpg") as f:
# f.write(str(binary_image)) # Can write to file
binary_image.save("in.png") #Replace test.png with your file name
execution_path = os.getcwd() #Returns current working directory of the project
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(
os.path.join(execution_path, "model_ex-068_acc-0.900000.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=3)
predictions, probabilities = prediction.predictImage(
os.path.join(execution_path, "in.png"))
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction, " : ", eachProbability)
from agora_community_sdk import AgoraRTC
from imageai.Detection import ObjectDetection
import os
client = AgoraRTC.create_watcher("dc96e5c14025414ea38980c9b1b1fbe4",
"chromedriver.exe")
client.join_channel("meher")
users = client.get_users(
) # Gets references to everyone participating in the call
user1 = users[0] # Can reference users in a list
binary_image = user1.frame # Gets the latest frame from the stream as a PIL image
#with open("test.jpg") as f:
# f.write(str(binary_image)) # Can write to file
binary_image.save("in.png") #Replace test.png with your file name
execution_path = os.getcwd() #Returns current working directory of the project
detector = ObjectDetection(
) #Calls the object detection function from the library ImageAI
detector.setModelTypeAsRetinaNet()
detector.setModelPath(
os.path.join(execution_path, "resnet50_coco_best_v2.0.1.h5")
) #make sure that you have downloaded resnet50_coco_best_v2.0.1.h5 to your main folder
detector.loadModel()
#detections = detector.detectObjectsFromImage(input_image=os.path.join(execution_path , "test.png"), output_image_path=os.path.join(execution_path , "test_output.png"))
detections, extracted_images = detector.detectObjectsFromImage(
input_image=os.path.join(execution_path, "in.png"),
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("classifier.h5")
print("Loaded model from disk")
# Part 3 - Making new predictions
import numpy as np
from keras.preprocessing import image
from agora_community_sdk import AgoraRTC
# from imageai.Detection import ObjectDetection
import os
client = AgoraRTC.create_watcher("6297937a41ff430690344df869d6e273",
"chromedriver.exe")
client.join_channel("demoChannel1")
[]
users = client.get_users(
) # Gets references to everyone participating in the call
print(len(users))
user1 = users[0] # Can reference users in a list
print("Hello")
print("Arnav")
print("Here")
print(user1)
# binary_image = user1.frame # Gets the latest frame from the stream as a PIL image
# with open("test.jpg") as f:
# f.write(str(binary_image)) # Can write to file
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("classifier.h5")
print("Loaded model from disk")
# Part 3 - Making new predictions
import numpy as np
from keras.preprocessing import image
from agora_community_sdk import AgoraRTC
# from imageai.Detection import ObjectDetection
import os
client = AgoraRTC.create_watcher("48f74ecd63554e2b844ca47b20c84116",
"chromedriver.exe")
client.join_channel("viren")
[]
users = client.get_users(
) # Gets references to everyone participating in the call
print(len(users))
user1 = users[0] # Can reference users in a list
print("Hello")
print("Arnav")
print("Here")
print(user1)
# binary_image = user1.frame # Gets the latest frame from the stream as a PIL image
# with open("test.jpg") as f:
# f.write(str(binary_image)) # Can write to file