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Modelos/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c520d4cf2558ae76288879a4d9bebac5c592b6affa7b0f06ef54de967b431cc6
+size 87625726

TrashDetect.py

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+# Importamos librerias
+from ultralytics import YOLO
+import cv2
+import math
+
+# Modelo
+model = YOLO('Modelos/best.pt')
+
+# Cap
+cap = cv2.VideoCapture(0)
+cap.set(3, 1280)
+cap.set(4, 720)
+
+# Clases
+clsName = ['Metal', 'Glass', 'Plastic', 'Carton', 'Medical']
+
+# Inference
+while True:
+    # Frames
+    ret, frame = cap.read()
+
+    # Yolo | AntiSpoof
+    results = model(frame, stream=True, verbose=False)
+    for res in results:
+        # Box
+        boxes = res.boxes
+        for box in boxes:
+            # Bounding box
+            x1, y1, x2, y2 = box.xyxy[0]
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+
+            # Error < 0
+            if x1 < 0: x1 = 0
+            if y1 < 0: y1 = 0
+            if x2 < 0: x2 = 0
+            if y2 < 0: y2 = 0
+
+            # Class
+            cls = int(box.cls[0])
+
+            # Confidence
+            conf = math.ceil(box.conf[0])
+            print(f"Clase: {cls} Confidence: {conf}")
+
+            if conf > 0:
+                # Draw
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
+                cv2.putText(frame, f'{clsName[cls]} {int(conf * 100)}%', (x1, y1 - 20),
+                            cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), 2)
+
+    # Show
+    cv2.imshow("Waste Detect", frame)
+
+    # Close
+    t = cv2.waitKey(5)
+    if t == 27:
+        break
+
+cap.release()
+cv2.destroyAllWindows()

main.py

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+# Libraries
+from tkinter import *
+from PIL import Image, ImageTk
+import imutils
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import math
+
+def clean_lbl():
+    # Clean
+    lblimg.config(image='')
+    lblimgtxt.config(image='')
+
+def images(img, imgtxt):
+    img = img
+    imgtxt = imgtxt
+
+    # Img Detect
+    img = np.array(img, dtype="uint8")
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    img = Image.fromarray(img)
+
+    img_ = ImageTk.PhotoImage(image=img)
+    lblimg.configure(image=img_)
+    lblimg.image = img_
+
+    # Img Text
+    imgtxt = np.array(imgtxt, dtype="uint8")
+    imgtxt = cv2.cvtColor(imgtxt, cv2.COLOR_BGR2RGB)
+    imgtxt = Image.fromarray(imgtxt)
+
+    img_txt = ImageTk.PhotoImage(image=imgtxt)
+    lblimgtxt.configure(image=img_txt)
+    lblimgtxt.image = img_txt
+
+# Scanning Function
+def Scanning():
+    global img_metal, img_glass, img_plastic, img_carton, img_medical
+    global img_metaltxt, img_glasstxt, img_plastictxt, img_cartontxt, img_medicaltxt, pantalla
+    global lblimg, lblimgtxt
+
+    # Interfaz
+    lblimg = Label(pantalla)
+    lblimg.place(x=75, y=260)
+
+    lblimgtxt = Label(pantalla)
+    lblimgtxt.place(x=995, y=310)
+    detect = False
+
+    # Read VideoCapture
+    if cap is not None:
+        ret, frame = cap.read()
+        frame_show =cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # True
+        if ret == True:
+            # Yolo | AntiSpoof
+            results = model(frame, stream=True, verbose=False)
+            for res in results:
+                # Box
+                boxes = res.boxes
+                for box in boxes:
+                    detect = True
+                    # Bounding box
+                    x1, y1, x2, y2 = box.xyxy[0]
+                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+
+                    # Error < 0
+                    if x1 < 0: x1 = 0
+                    if y1 < 0: y1 = 0
+                    if x2 < 0: x2 = 0
+                    if y2 < 0: y2 = 0
+
+                    # Class
+                    cls = int(box.cls[0])
+
+                    # Confidence
+                    conf = math.ceil(box.conf[0])
+                    #print(f"Clase: {cls} Confidence: {conf}")
+                    # Metal
+                    if cls == 0:
+                        # Draw
+                        cv2.rectangle(frame_show, (x1, y1), (x2, y2), (255, 255, 0), 2)
+                        # Text
+                        text = f'{clsName[cls]} {int(conf) * 100}%'
+                        sizetext = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
+                        dim = sizetext[0]
+                        baseline = sizetext[1]
+                        # Rect
+                        cv2.rectangle(frame_show, (x1, y1 - dim[1] - baseline), (x1 + dim[0], y1 + baseline), (0, 0, 0),cv2.FILLED)
+                        cv2.putText(frame_show, text, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
+
+                        # Clasificacion
+                        images(img_metal, img_metaltxt)
+
+                    if cls == 1:
+                        # Draw
+                        cv2.rectangle(frame_show, (x1, y1), (x2, y2), (255, 255, 255), 2)
+                        # Text
+                        text = f'{clsName[cls]} {int(conf) * 100}%'
+                        sizetext = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
+                        dim = sizetext[0]
+                        baseline = sizetext[1]
+                        # Rect
+                        cv2.rectangle(frame_show, (x1, y1 - dim[1] - baseline), (x1 + dim[0], y1 + baseline),
+                                      (0, 0, 0), cv2.FILLED)
+                        cv2.putText(frame_show, text, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+
+                        # Clasificacion
+                        images(img_glass, img_glasstxt)
+
+                    if cls == 2:
+                        # Draw
+                        cv2.rectangle(frame_show, (x1, y1), (x2, y2), (0, 0, 255), 2)
+                        # Text
+                        text = f'{clsName[cls]} {int(conf) * 100}%'
+                        sizetext = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
+                        dim = sizetext[0]
+                        baseline = sizetext[1]
+                        # Rect
+                        cv2.rectangle(frame_show, (x1, y1 - dim[1] - baseline), (x1 + dim[0], y1 + baseline),
+                                      (0, 0, 0), cv2.FILLED)
+                        cv2.putText(frame_show, text, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+                        # Clasificacion
+                        images(img_plastic, img_plastictxt)
+
+                    if cls == 3:
+                        # Draw
+                        cv2.rectangle(frame_show, (x1, y1), (x2, y2), (150, 150, 150), 2)
+                        # Text
+                        text = f'{clsName[cls]} {int(conf) * 100}%'
+                        sizetext = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
+                        dim = sizetext[0]
+                        baseline = sizetext[1]
+                        # Rect
+                        cv2.rectangle(frame_show, (x1, y1 - dim[1] - baseline), (x1 + dim[0], y1 + baseline),
+                                      (0, 0, 0), cv2.FILLED)
+                        cv2.putText(frame_show, text, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (150, 150, 150), 2)
+
+                        # Clasificacion
+                        images(img_carton, img_cartontxt)
+
+                    if cls == 4:
+                        # Draw
+                        cv2.rectangle(frame_show, (x1, y1), (x2, y2), (255, 0, 0), 2)
+                        # Text
+                        text = f'{clsName[cls]} {int(conf) * 100}%'
+                        sizetext = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
+                        dim = sizetext[0]
+                        baseline = sizetext[1]
+                        # Rect
+                        cv2.rectangle(frame_show, (x1, y1 - dim[1] - baseline), (x1 + dim[0], y1 + baseline),
+                                      (0, 0, 0), cv2.FILLED)
+                        cv2.putText(frame_show, text, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
+
+                        # Clasificacion
+                        images(img_medical, img_medicaltxt)
+
+            if detect == False:
+                # Clean
+                clean_lbl()
+
+
+            # Resize
+            frame_show = imutils.resize(frame_show, width=640)
+
+            # Convertimos el video
+            im = Image.fromarray(frame_show)
+            img = ImageTk.PhotoImage(image=im)
+
+            # Mostramos en el GUI
+            lblVideo.configure(image=img)
+            lblVideo.image = img
+            lblVideo.after(10, Scanning)
+
+        else:
+            cap.release()
+
+# main
+def ventana_principal():
+    global cap, lblVideo, model, clsName, img_metal, img_glass, img_plastic, img_carton, img_medical
+    global img_metaltxt, img_glasstxt, img_plastictxt, img_cartontxt, img_medicaltxt, pantalla
+    # Ventana principal
+    pantalla = Tk()
+    pantalla.title("RECICLAJE INTELIGENTE")
+    pantalla.geometry("1280x720")
+
+    # Background
+    imagenF = PhotoImage(file="setUp/Canva.png")
+    background = Label(image=imagenF, text="Inicio")
+    background.place(x=0, y=0, relwidth=1, relheight=1)
+
+    # Clases: 0 -> Metal | 1 -> Glass | 2 -> Plastic | 3 -> Carton | 4 -> Medical
+    # Model
+    model = YOLO('Modelos/best.pt')
+
+    # Clases
+    clsName = ['Metal', 'Glass', 'Plastic', 'Carton', 'Medical']
+
+    # Images
+    img_metal = cv2.imread("setUp/metal.png")
+    img_glass = cv2.imread("setUp/vidrio.png")
+    img_plastic = cv2.imread("setUp/plastico.png")
+    img_carton = cv2.imread("setUp/carton.png")
+    img_medical = cv2.imread("setUp/medical.png")
+    img_metaltxt = cv2.imread("setUp/metaltxt.png")
+    img_glasstxt = cv2.imread("setUp/vidriotxt.png")
+    img_plastictxt = cv2.imread("setUp/plasticotxt.png")
+    img_cartontxt = cv2.imread("setUp/cartontxt.png")
+    img_medicaltxt = cv2.imread("setUp/medicaltxt.png")
+
+    # Video
+    lblVideo = Label(pantalla)
+    lblVideo.place(x=320, y=180)
+
+    # Elegimos la camara
+    cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+    cap.set(3, 1280)
+    cap.set(4, 720)
+    Scanning()
+
+    # Eject
+    pantalla.mainloop()
+
+if __name__ == "__main__":
+    ventana_principal()