From 22503d066e7e21d9b987fb56478d33a059498443 Mon Sep 17 00:00:00 2001 From: SKT-Sukatat <82657530+SKT-Sukatat@users.noreply.github.com> Date: Tue, 31 Oct 2023 17:41:14 +0700 Subject: [PATCH] Task_03 --- ...predict.py => 02_detect_number_predict.py} | 0 assignment_3/03_TFLite_detection_webcam.py | 154 ++++++++++++++++++ ...lenet_ssd_v2_coco_quant_postprocess.tflite | Bin 0 -> 6175408 bytes 3 files changed, 154 insertions(+) rename assignment_3/{09_detect_number_predict.py => 02_detect_number_predict.py} (100%) create mode 100644 assignment_3/03_TFLite_detection_webcam.py create mode 100644 assignment_3/mobilenet_ssd_v2_coco_quant_postprocess.tflite diff --git a/assignment_3/09_detect_number_predict.py b/assignment_3/02_detect_number_predict.py similarity index 100% rename from assignment_3/09_detect_number_predict.py rename to assignment_3/02_detect_number_predict.py diff --git a/assignment_3/03_TFLite_detection_webcam.py b/assignment_3/03_TFLite_detection_webcam.py new file mode 100644 index 0000000..570bcb0 --- /dev/null +++ b/assignment_3/03_TFLite_detection_webcam.py @@ -0,0 +1,154 @@ +import cv2 +import time +import numpy as np +from tflite_runtime.interpreter import Interpreter +from threading import Thread + +class VideoStream: + """Camera object that controls video streaming from the Picamera""" + def __init__(self,resolution=(640,480),framerate=30): + # Initialize the PiCamera and the camera image stream + self.stream = cv2.VideoCapture(0) + ret = self.stream.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG')) + ret = self.stream.set(3,resolution[0]) + ret = self.stream.set(4,resolution[1]) + + # Read first frame from the stream + (self.grabbed, self.frame) = self.stream.read() + + # Variable to control when the camera is stopped + self.stopped = False + + def start(self): + # Start the thread that reads frames from the video stream + Thread(target=self.update,args=()).start() + return self + + def update(self): + # Keep looping indefinitely until the thread is stopped + while True: + # If the camera is stopped, stop the thread + if self.stopped: + # Close camera resources + self.stream.release() + return + + # Otherwise, grab the next frame from the stream + (self.grabbed, self.frame) = self.stream.read() + + def read(self): + # Return the most recent frame + return self.frame + + def stop(self): + # Indicate that the camera and thread should be stopped + self.stopped = True + +min_conf_threshold = 0.5 # Minimum confidence threshold for displaying detected objects default 0.5 + +# Path to .tflite file, which contains the model that is used for object detection +tflite_model_path = "./TFLite_model/mobilenet_ssd_v2_coco_quant_postprocess.tflite" +# Path to label map file +Labels_path = "./TFLite_model/coco_labels.txt" # from MobileNet SSD v2 (COCO) https://coral.ai/models/?fbclid=IwAR347RorBNMeLiFZ6A_5z7UfNJ-bCZbXIsfQ81XDdkKFs7TrPt3hYmv61DI + +indexs = [] +labels = [] +# Load the label map +with open(Labels_path, 'r') as f: + labels_data = [line.strip() for line in f.readlines()] + for count in range(0,len(labels_data)): + indexs.append(labels_data[count].split(" ")[0]) + labels.append(labels_data[count].split(" ")[1]) + +# Load the Tensorflow Lite model. +interpreter = Interpreter(model_path=tflite_model_path) +interpreter.allocate_tensors() + +# Get model details +input_details = interpreter.get_input_details() +output_details = interpreter.get_output_details() +height = input_details[0]['shape'][1] +width = input_details[0]['shape'][2] + +floating_model = (input_details[0]['dtype'] == np.float32) + +input_mean = 127.5 +input_std = 127.5 + +# Initialize frame rate calculation +frame_rate_calc = 1 +freq = cv2.getTickFrequency() + +imW = 640 +imH = 480 + +# Initialize video stream +videostream = VideoStream(resolution=(imW,imH),framerate=30).start() +time.sleep(1) + +#for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True): +while True: + # Start timer (for calculating frame rate) + t1 = cv2.getTickCount() + + # Grab frame from video stream + frame1 = videostream.read() + + # Acquire frame and resize to expected shape [1xHxWx3] + frame = frame1.copy() + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame_resized = cv2.resize(frame_rgb, (width, height)) + input_data = np.expand_dims(frame_resized, axis=0) + + # Normalize pixel values if using a floating model (i.e. if model is non-quantized) + if floating_model: + input_data = (np.float32(input_data) - input_mean) / input_std + + # Perform the actual detection by running the model with the image as input + interpreter.set_tensor(input_details[0]['index'],input_data) + interpreter.invoke() + + # Retrieve detection results + boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects + classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects + scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detected objects + + # Loop over all detections and draw detection box if confidence is above minimum threshold + for i in range(len(scores)): + if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)): + + # Get bounding box coordinates and draw box + # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min() + ymin = int(max(1,(boxes[i][0] * imH))) + xmin = int(max(1,(boxes[i][1] * imW))) + ymax = int(min(imH,(boxes[i][2] * imH))) + xmax = int(min(imW,(boxes[i][3] * imW))) + + cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2) + + # Draw label + object_name = labels[int(classes[i])] # Look up object name from "labels" array using class index + label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%' + labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size + label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window + cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in + cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text + + # Draw framerate in corner of frame + cv2.putText(frame,'FPS: {0:.2f}'.format(frame_rate_calc),(30,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),2,cv2.LINE_AA) + + # All the results have been drawn on the frame, so it's time to display it. + cv2.imshow('Object detector', frame) + + # Calculate framerate + t2 = cv2.getTickCount() + time1 = (t2-t1)/freq + frame_rate_calc= 1/time1 + + # Press 'q' to quit + if cv2.waitKey(1) == ord('q'): + break + +# Clean up +cv2.destroyAllWindows() +videostream.stop() diff --git a/assignment_3/mobilenet_ssd_v2_coco_quant_postprocess.tflite b/assignment_3/mobilenet_ssd_v2_coco_quant_postprocess.tflite new file mode 100644 index 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