Object detection

import requests
import cv2
import json
import time

url =Given URL

headers = {
    'accept': 'application/x-www-form-urlencoded'
}

path = './testdir/test1.jpg'#Free path

#file = {'file': open(path, 'rb')}
data = {'urls' : ['https://goo.gl/ICoiHc', 'https://goo.gl/ICoiHc']}

start = time.time()
#response = requests.post(url, auth=requests.auth.HTTPBasicAuth('Given', ''), files=file)
response = requests.post(url, auth=requests.auth.HTTPBasicAuth('Given', ''), data=data)
end = time.time()
print("Post time", end-start, "Seconds")
print(response.text)

jsondata = response.json()
result = jsondata["result"]
image = cv2.imread(path)

sep_score = 0.3

for r in result:
    labells = []
    score_ls = []
    print(r)
    for p in r["prediction"]:
        print(p)
        label, left, top, right, bottom, score = p["label"], p["xmin"], p["ymin"], p["xmax"], p["ymax"], p["score"]

        if score >= sep_score and not label in labells:
            labells.append(label)
            score_ls.append(score_ls)
            cv2.rectangle(image, (left, top), (right, bottom), (255, 0, 0), thickness=3)
            color = (255, 0, 0)
            fontScale = 1.0
            fontFace = cv2.FONT_HERSHEY_COMPLEX
            text = label + " " + str(round(score, 2))
            org = (left, top)
            cv2.putText(image, text, org, fontFace, fontScale, color, thickness=4, lineType=cv2.LINE_8)


cv2.imshow("pic", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Image classification

import requests
import time

url = 'Given'

headers = {
  'accept': 'application/x-www-form-urlencoded'
}

nls = ["test1.jpg ", "test2.jpg ", "test3.jpg ", "test4.jpg ", "test5.jpg ", "test6.jpg "]

for n in nls:
    path = n

    file = {'file': open(path, 'rb')}

    data = {'modelId': 'Given'}

    start = time.time()

    response = requests.request('POST', url, headers=headers, auth=requests.auth.HTTPBasicAuth('Given', ''), data=data, files=file)

    end = time.time()
    print("Post time", end - start, "Seconds")

    print(response.text)
    jsondata = response.json()
    result = jsondata["result"]

[Nanonets] How to post Memo [Python]

Object detection

Image classification