Title: High Speed Packing Line: Candy Sorting & Bag Packaging Machine
Description:
Welcome to our video showcasing the extraordinary capabilities of our High Speed Packing Line, specifically designed for candy sorting and bag packaging. This state-of-the-art machine offers unparalleled efficiency and precision, ensuring seamless operations in the food packaging industry.
At [Company Name], we prioritize quality and compliance with international standards. Our High Speed Packing Line is proudly made in China, meeting both Chinese and U.S. standards, making it a reliable choice for businesses worldwide. Visit our website at [Website URL] to learn more about our products and services.
In this video, we demonstrate the outstanding performance and features of our Candy High Speed Sorting Feeding Flow Bag Packaging Machine. Through a comprehensive overview, we will highlight the key points and operation steps, providing you with valuable insights into its capabilities.
Video Content:
– Introduction to the High Speed Packing Line
– Detailed explanation of the candy sorting and feeding process
– Overview of the bag packaging machine’s features and benefits
– Demonstration of the operation steps, showcasing its efficiency and accuracy
– Key highlights and interesting facts about the machine’s performance
We encourage you to engage with our video by liking, subscribing, and sharing it with others in the industry. Together, we can spread awareness about this cutting-edge technology and help businesses enhance their packaging processes.
Additional Tags: candy packaging, high speed packaging machine, bag packaging, sorting machine, food packaging, efficiency, precision, automation, industry-leading, innovative technology
Hashtags: #HighSpeedPackingLine #CandySorting #BagPackagingMachine #FoodPackaging #Efficiency #Automation
import cv2
import numpy as np
def candy_sorting(image_path):
# Load the image
image = cv2.imread(image_path)
# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply Gaussian blur to reduce noise
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# Apply Canny edge detection
edges = cv2.Canny(blurred, 50, 150)
# Find contours of the edges
contours, _ = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Sort the contours based on their area in descending order
sorted_contours = sorted(contours, key=cv2.contourArea, reverse=True)
# Iterate over the contours and draw rectangles around the candies
for contour in sorted_contours:
# Calculate the perimeter of the contour
perimeter = cv2.arcLength(contour, True)
# Approximate the shape of the contour as a polygon
approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
# If the contour has 4 vertices, it is likely a rectangular candy
if len(approx) == 4:
# Draw a rectangle around the candy
x, y, w, h = cv2.boundingRect(approx)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Display the image with the candies sorted and labeled
cv2.imshow(“Candy Sorting”, image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Example usage:
candy_sorting(“candy_image.jpg”)
Automatic Packing Line
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