Added yolov5s example with labbels and model.

Signed-off-by: Zanz2 https://github.com/Zanz2

examples/tensorflow/coco_labels_yolov5.txt

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+0 person
+1 bicycle
+2 car
+3 motorcycle
+4 airplane
+5 bus
+6 train
+7 truck
+8 boat
+9 traffic light
+10 fire hydrant
+11 stop sign
+12 parking meter
+13 bench
+14 bird
+15 cat
+16 dog
+17 horse
+18 sheep
+19 cow
+20 elephant
+21 bear
+22 zebra
+23 giraffe
+24 backpack
+25 umbrella
+26 handbag
+27 tie
+28 suitcase
+29 frisbee
+30 skis
+31 snowboard
+32 sports ball
+33 kite
+34 baseball bat
+35 baseball glove
+36 skateboard
+37 surfboard
+38 tennis racket
+39 bottle
+40 wine glass
+41 cup
+42 fork
+43 knife
+44 spoon
+45 bowl
+46 banana
+47 apple
+48 sandwich
+49 orange
+50 broccoli
+51 carrot
+52 hot dog
+53 pizza
+54 donut
+55 cake
+56 chair
+57 couch
+58 potted plant
+59 bed
+60 dining table
+61 toilet
+62 tv
+63 laptop
+64 mouse
+65 remote
+66 keyboard
+67 cell phone
+68 microwave
+69 oven
+70 toaster
+71 sink
+72 refrigerator
+73 book
+74 clock
+75 vase
+76 scissors
+77 teddy bear
+78 hair drier
+79 toothbrush

examples/tensorflow/yolo_v5_real_time_with_labels.py

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+#!/usr/bin/python3
+
+# Copyright (c) 2022 Raspberry Pi Ltd
+# Author: Zanz2 <https://github.com/Zanz2>
+# SPDX-License-Identifier: BSD-3-Clause
+
+# A TensorFlow Lite example for Picamera2 on Raspberry Pi 5 (OS Bookworm) with an active cooler
+#
+# Install necessary dependences before starting,
+#
+# $ sudo apt update
+# $ sudo apt install build-essential
+# $ sudo apt install libatlas-base-dev
+# $ sudo apt install python3-pip
+
+# Use system python or if you prefer not to mess with system python:
+# install a version manager (like pyenv) and use 'pyenv virtualenv --system-site-packages ENV_NAME'
+# $ pip3 install tflite-runtime
+# $ pip3 install opencv-python-headless (if using system python: sudo apt install python3-opencv)
+#
+# and run from the command line,
+#
+# python3 yolo_v5_real_time_with_labels.py --model=yolov5s-fp16.tflite --label=coco_labels_yolov5.txt
+
+import argparse
+
+import cv2
+import numpy as np
+import tflite_runtime.interpreter as tflite
+
+from picamera2 import MappedArray, Picamera2, Platform, Preview
+
+normalSize = (1920, 1080)
+lowresSize = (640, 640)  # Shape Yolov5 s was trained with
+# if using other yolov5 flavour then image from stream will be resized accordingly.
+
+rectangles = []
+
+
+def ReadLabelFile(file_path):
+    with open(file_path, 'r') as f:
+        lines = f.readlines()
+    ret = {}
+    for line in lines:
+        pair = line.strip().split(maxsplit=1)
+        ret[int(pair[0])] = pair[1].strip()
+    return ret
+
+
+def DrawRectangles(request):
+    with MappedArray(request, "main") as m:
+        for rect in rectangles:
+            xmin, ymin, xmax, ymax = rect[0:4]
+
+            rect_start = xmin, ymin
+            rect_end = xmax, ymax
+            cv2.rectangle(m.array, rect_start, rect_end, (0, 255, 0, 0))
+            if len(rect) == 5:
+                text = rect[4]
+                font = cv2.FONT_HERSHEY_SIMPLEX
+                cv2.putText(m.array, text, (xmin, ymin - 10),
+                            font, 1, (255, 255, 255), 2, cv2.LINE_AA)
+
+
+def classFilter(classdata):
+    return [c.argmax() for c in classdata]
+    # generates a list, loop through all predictions and get the best classification location
+
+
+def YOLOdetect(output_data):  # input = interpreter, output is boxes(xyxy), classes, scores
+    output_data = output_data[0]                # x(1, 25200, 7) to x(25200, 7)
+    boxes = np.squeeze(output_data[..., :4])    # boxes  [25200, 4]
+    scores = np.squeeze(output_data[..., 4:5])  # confidences  [25200, 1]
+    classes = classFilter(output_data[..., 5:])  # get classes
+    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+    x, y, w, h = boxes[..., 0], boxes[..., 1], boxes[..., 2], boxes[..., 3]  # xywh
+    xyxy = [x - w / 2, y - h / 2, x + w / 2, y + h / 2]  # xywh to xyxy
+
+    return xyxy, classes, scores  # output is boxes(x,y,x,y), classes(int), scores(float) [predictions length]
+
+
+def main():
+    global rectangles
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', help='Path of the detection model.', required=True)
+    parser.add_argument('--label', help='Path of the labels file.')
+    args = parser.parse_args()
+
+    if args.label:
+        labels = ReadLabelFile(args.label)
+    else:
+        labels = None
+    picam2 = Picamera2()
+    picam2.start_preview(Preview.QTGL)
+
+    stream_format = "YUV420"
+    if Picamera2.platform == Platform.PISP:
+        stream_format = "RGB888"
+
+    config = picam2.create_preview_configuration(main={"size": normalSize},
+                                                 lores={"size": lowresSize, "format": stream_format})
+    picam2.configure(config)
+    picam2.post_callback = DrawRectangles
+
+    picam2.start()
+    interpreter = tflite.Interpreter(model_path=args.model, num_threads=4)
+    interpreter.allocate_tensors()
+
+    while True:
+        img = picam2.capture_array("lores")
+
+        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 = False
+        if input_details[0]['dtype'] == np.float32:
+            floating_model = True
+
+        if stream_format == "YUV420":
+            img = cv2.cvtColor(img, cv2.COLOR_YUV420p2RGB)
+
+        new_shape = (width, height)  # the shape the model was trained with
+        if new_shape != lowresSize:
+            img = cv2.resize(img, new_shape)
+
+        input_data = np.expand_dims(img, axis=0)
+        if floating_model:
+            input_data = (np.float32(input_data) - 127.5) / 127.5
+
+        interpreter.set_tensor(input_details[0]['index'], input_data)
+        interpreter.invoke()
+        output_data = interpreter.get_tensor(output_details[0]['index'])
+        xyxy, classes, scores = YOLOdetect(output_data)
+        rectangles = []
+
+        for i in range(len(scores)):
+            if ((scores[i] > 0.4) and (scores[i] <= 1.0)):
+                xmin = int(max(1, (xyxy[0][i] * normalSize[0])))
+                ymin = int(max(1, (xyxy[1][i] * normalSize[1])))
+                xmax = int(min(normalSize[0], (xyxy[2][i] * normalSize[0])))
+                ymax = int(min(normalSize[1], (xyxy[3][i] * normalSize[1])))
+
+                box = [xmin, ymin, xmax, ymax]
+                rectangles.append(box)
+                if labels:
+                    rectangles[-1].append(labels[classes[i]])
+
+
+if __name__ == '__main__':
+    main()