Mineral Processing Technology - Image Analytics

projects/Mineral Processing Technology-Image Analytics/README.md

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+# Mineral-Processing-Technology-Image-Analytics
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+In the field of Mineral Processing Technology, size analysis of the various particles of an extracted sample is of importance in determining the quality of minerals, entropy values and in establishing the degree of liberation of the values from the gangue at various particle sizes.
+In this problem statement, candidate is required to analyze the mineral particles in the input folder and calculate the following - 
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+The smallest circle that just encapsulates the particle (the circle has to be generated on the image).
+	Example –
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+![image](https://github.com/manishkumar00208/Mineral-Processing-Technology-Image-Analytics/assets/76589863/93a433c2-fefb-4412-a3a3-b6b4cf67f1f4)
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+2. Total surface area of the particle (in pixels) (Has to be generated on the image)
+	Example – 
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+![image](https://github.com/manishkumar00208/Mineral-Processing-Technology-Image-Analytics/assets/76589863/f922f999-61cd-465a-8676-e9c0998bfd3b)
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+3. The major axis (longest axis) in the particle that lies entirely inside the particle (in pixels) (Has to be generated on the image)
+Example – 
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+![image](https://github.com/manishkumar00208/Mineral-Processing-Technology-Image-Analytics/assets/76589863/fc388074-06ab-4401-b18d-020926baacaf)
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+4. Total perimeter of the particle (in pixels) (Has to be generated on the image)
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+![image](https://github.com/manishkumar00208/Mineral-Processing-Technology-Image-Analytics/assets/76589863/e21c7946-06cb-47b8-9e8f-64863ac20cab)
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+5. Centroid of the particle (Has to be generated on the image)
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+![image](https://github.com/manishkumar00208/Mineral-Processing-Technology-Image-Analytics/assets/76589863/f01af9b5-3150-4bf4-af53-f6d70242baac)
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+Read the input images for the challenge form the “input” folder, process each image based on the calculations mentioned and save the output image in the “output” folder.
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projects/Mineral Processing Technology-Image Analytics/code.py

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+import cv2
+import os
+import numpy as np
+# Input and output folders
+input_folder = "input"
+output_folder = "output"
+
+# Ensure the output folder exists
+if not os.path.exists(output_folder):
+    os.makedirs(output_folder)
+
+# Function to process an image
+def process_image(filename):
+    # Read the image
+    img = cv2.imread(filename)
+
+    # Convert the image to grayscale
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    # Invert the image to make black figures on a white background
+    inverted = cv2.bitwise_not(gray)
+
+    # Apply thresholding to segment the black figures (adjust threshold value as needed)
+    _, thresh = cv2.threshold(inverted, 128, 255, cv2.THRESH_BINARY)
+
+    # Find contours in the binary image
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # Draw the calculations for each black figure on the image
+    img_processed = img.copy()
+    for contour in contours:
+        # Calculate the minimum enclosing circle
+        (x, y), radius = cv2.minEnclosingCircle(contour)
+        center = (int(x), int(y))
+        radius = int(radius)
+
+        # Calculate the surface area
+        area = cv2.contourArea(contour)
+
+        # Calculate the major axis as the longest distance between any two points
+        #points = contour.squeeze()
+        #distances = [((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)**0.5 for p1 in points for p2 in points]
+        #major_axis = max(distances)
+        # Calculate the major axis points
+        points = contour.squeeze()
+        max_distance = 0
+        major_axis_points = None
+        for i, p1 in enumerate(points):
+            for p2 in points[i + 1:]:
+                distance = np.linalg.norm(p1 - p2)
+                if distance > max_distance:
+                    max_distance = distance
+                    major_axis_points = p1, p2
+
+        # Draw a line representing the major axis
+        if major_axis_points is not None:
+            p1, p2 = major_axis_points
+            cv2.line(img_processed, tuple(p1), tuple(p2), (0, 0, 255), 2)  # Red line
+        # Calculate the perimeter of the contour
+        perimeter = cv2.arcLength(contour, closed=True)
+
+        # Calculate the centroid of the contour
+        M = cv2.moments(contour)
+        cx = int(M['m10'] / M['m00'])
+        cy = int(M['m01'] / M['m00'])
+        centroid = (cx, cy)
+
+        # Draw the minimum enclosing circle
+        cv2.circle(img_processed, center, radius, (0, 0, 255), 2)  # Red circle
+        # Draw a marker at the centroid
+        cv2.circle(img_processed, centroid, 5, (0, 0, 255), -1)  # Red circle
+
+
+        # Display the calculations as text
+        cv2.putText(img_processed, f"Total surface area: {area:.2f}", (cx - 40, cy - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+        cv2.putText(img_processed, f"Total Perimeter: {perimeter:.2f}", (cx - 60, cy + 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+        cv2.putText(img_processed, f"Centroid: ({cx},{cy})", (cx - 40, cy + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+        cv2.putText(img_processed, f"Major Axis Length: {max_distance:.2f}", (cx - 60, cy + 60),cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 0, 255), 2)
+
+    # Save the processed image with calculations
+    output_filename = os.path.join(output_folder, os.path.basename(filename))
+    cv2.imwrite(output_filename, img_processed)
+
+# Process each image in the input folder
+for filename in os.listdir(input_folder):
+    if filename.endswith((".jpg", ".png")):
+        input_image_path = os.path.join(input_folder, filename)
+        process_image(input_image_path)
+
+print("Processing complete.")