def classify_image(): image_data = request.form['image_data'] #base64 to cv2 encoded_data = image_data.split(',')[1] nparr = np.frombuffer(base64.b64decode(encoded_data), np.uint8) img = cv2.imdecode(nparr, cv2.IMREAD_COLOR) cv2.imwrite('image.jpg', img) # enhancing image im = Image.open('image.jpg') enhancers = ImageEnhance.Sharpness(im) enhanced_im = enhancers.enhance(2.75) enhanced_im.save("sample.jpg") im = Image.open('sample.jpg') enhancercon = ImageEnhance.Contrast(im) enhanced_im = enhancercon.enhance(1.2) enhanced_im.save("sample.jpg") im = Image.open('sample.jpg') enhancerc = ImageEnhance.Color(im) enhanced_im = enhancerc.enhance(1.1) enhanced_im.save("sample.jpg") # enhanced Image file data = {} with open('sample.jpg', mode='rb') as file: imgw = file.read() data['imgw'] = base64.b64encode(imgw) response = jsonify(util.classify_image("b," + str(data['imgw'])[2:])) response.headers.add('Access-Control-Allow-Origin', '*') return response
def classify_image(): image_data = request.form['image_data'] response = jsonify(util.classify_image(image_data)) response.headers.add('Access-Control-Allow-Origin', '*') return response
def classify_image(): image_data = request.form['image_data'] back_response = util.classify_image(image_data) response = jsonify(back_response) print(back_response) response.headers.add('Access-control-Allow-Origin', '*') return response
def classify_image(): image_data = request.form['image_data'] response = jsonify(util.classify_image(image_data)) response.headers.add('Access-Control-Allow-Origin', '*') response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization') response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE') return response
def classify_image(): image_data = request.form['image_data'] data=[] url = "https://api.unsplash.com/search/photos" query={"page":"1", "client_id":"vrB0FKvJ2uIq780UySc3VE8LI8uxKhja3MyxU1MN9MI"} q = util.classify_image(image_base64_data=image_data) query["query"]=q response = requests.get(url,params=query) for i in range(0,10): data.append(response.json()["results"][i]["urls"]["raw"]) response = jsonify(data) response.headers.add('Access-Control-Allow-Origin', '*') return response
model_name = 'mobilenet_v1_1.0_224_quant.tflite' repeat = 10 model_dir = download_model_zoo(model_dir, model_name) tflite_model_file = os.path.join(model_dir, model_name) tflite_model_buf = open(tflite_model_file, "rb").read() try: import tflite tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0) except AttributeError: import tflite.Model tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0) interpreter = Interpreter(tflite_model_file, num_threads=get_cpu_count()) interpreter.allocate_tensors() _, height, width, _ = interpreter.get_input_details()[0]['shape'] image = load_test_image('uint8', height, width) numpy_time = np.zeros(repeat) for i in range(0, repeat): start_time = time.time() results = classify_image(interpreter, image) elapsed_ms = (time.time() - start_time) * 1000 numpy_time[i] = elapsed_ms print("tflite %-20s %-19s (%s)" % (model_name, "%.2f ms" % np.mean(numpy_time), "%.2f ms" % np.std(numpy_time)))